Tino T . Her den
Managing Supply Chain Analy tics – Guiding org aniz a tions
t o e x ecut e Analytics init ia tiv es in Logis tics and Supply
Chain Managemen t

M a n a g i n g S u p p l y C h a i n A n a l y t i c s
Guiding organizations to exe cute Analytics initiatives in Logistics and Supply Chain
Management
vorgelegt von
M. Sc.
Tino T. Herden
ORCID : 0000- 0002-586 7-4708
an der Fakultät VII – Wirtschaft und Mana gement
der Technischen Universität Berlin
zur Erlangung des aka de mischen Grades
Doktor der I ngenieurwissenschaften
- Dr.-I ng. -
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr. Rüdiger Zarnekow
Gutachter: Prof. Dr.-Ing. Frank Straube
Gutachter: Prof . Dr. André Ludwig
Tag der w issenschaftlichen Aussprache: 06. November 2019

Berlin 20 20

II

III
Abstract
The application o f Analytics in the domain of Logist ics and Supply Chain Management
(LSCM) – Supply Chain Ana lytics (SCA) – presents a wide range of a dvantages. The
domain ha s historically been a first mover in the application of a nalytical methods due to
the c omplex decision-making under uncertainty and has the potential to exploit manif old
new data sources accessible through recent technological advances. Sc holars have
provided e vidence for the perf ormance increase in this domain due to Ana lytics and ha ve
also argued for competiti ve adv antages enabled through it . Surveys amongst practitioners
and reports show similar potential to improve the eff iciency of processes, create new
business opportunities with it and eventually increa se customer orientation. However,
LSCM organizations are not extensive ly and compre hensively utilizing these potential
advantage s and the value and bene fits resulting from it. Organiza tions are either prevented
to take advantage through barriers, or they disbelieve in the advantages and behave
reluctant, while new co mpetitors exploit Analytics to gain market share. These issues
demonstrate that exec uting Analytics initiatives goes beyond the application of analytical
methods and requires supporting and dir ecting managerial actions. This thesis
investigates these managerial actions and practices for Analytics initiatives in LSCM .
Thereby, initiative describes the entire lifecycle of Analytics solut ions, from the
definition of the problem to be solved and its deve lopment to its use and m aintenance in
the value-added processes. With a va riety of research methods, including Grounded
Theory, Clustering, C ase Studies, and th e Q-M ethodology, this thesis examines SCA
initiatives in exploratory a nd confirmatory research designs. In four articles,
investigations are conducted of (1) c haracteristics distinguish ing LSCM fr om other
domains in the execution of Analytics, (2) Analytics initiatives curre ntly executed in
LSCM, (3) the process of executing Ana lytics init iatives in LSCM to gain valuable
Analytics solut ions, and (4) barriers LSCM organizations enc ounter in the exec ution of
Analytics initiatives and the measures the y employ to overcome the barrier s. Thes e
articles re sult in a map of characteristics to distinguish domains in the execution of
Analytics initiatives and an individual pro file of LS CM, six distinct Archetypes of SCA
initiatives, comprehensive explanations of the competitive advantage from Analytics in
LSCM, and frameworks of barriers and measures in S CA. The individual re sults of each
article are use d to describe the va lue LSCM organiz ations can create for their customers,
and by further combining the results of the articles with 15 process approaches to manage

IV
Analytics initiatives, a supplemented app roach to manage Analytics initiatives, espe cially
SCA initiat ives, is developed. I n this pursuit, Analytics is established as a tool, that
requires human creativity and expertise to unfol d its full potential. As such a tool, it
provides solutions to support humans in their decision -making and can lea d to improved
decisions if, like any othe r tool , it is applied in the appropriate approa ch. Wit h the research
results described above, t his thesis provides guidance for managers in LSCM to manage
SCA initiatives such that they understand th is appropriate approach for Analytics in
LSCM and are enabled to employ it for (competitive) advantage and continuous
improvements of processes and c ustomer satisfaction.

V
Zusammenfassung
Die Anwendung von Analyti cs in der Logistik und Supply Chain Management (LSCM)
Domäne – Supply Chai n Analytics (SCA) – bie tet eine Vielza hl von Vorteilen. Die
Domäne war in der Vergangenheit aufgrund der komplexen Entscheidungsfindung unter
Unsicherhe it ein „ First Mover “ bei der Anwendung analytischer Methoden und verfügt
über das Potenzial, die zahlreichen neu en Dat enquellen ertragreich zu nut zen, die durch
die jüngsten technologischen Fortschritte zugänglich sind. Die wiss enschaftliche
Literatur ha t Belege für die Leistungssteigerung i n de r LSCM Domän e du rch An alyti cs
erbracht und auch fü r Wettbewerbsvorteile ar gumentiert, die durch si e ermöglicht
werden. Umf ragen unter Praktikern und Berichte zeigen e in ähnliches Potenzial, die
Effizienz von Prozessen zu verbessern, damit neue Ges chäftsmöglichkeiten zu schaffen
und schließlich die Kundenorientierung zu erhöhen. LSCM-Organisationen nutzen diese
potenziellen Vorteile jedoch nicht umfassend aus und erschließen somit de n da raus
resultierende n Wert und Nutzen nicht im vol len Umfa ng. Unternehmen wer den e ntweder
durch B arrieren da ran ge hindert, die Vorteile auszunutzen, oder sie glauben nicht an die
Vorteile und verhalten si ch zögerlich, während neue Wettbewerber Analytics einsetz en,
um Markta nteile zu gewinnen. Diese Probleme z eigen, dass die Durchführung von
Analyti cs -Initiativen über die Anwendung von Analysemethoden hinau sgeht und die
Unterstützung und Steuerung von Managementmaßnahmen erfordert. Diese Arbeit
untersucht diese Ma nagementmaßnahmen und -praktiken für Analyti cs -Initiativen in der
LSCM Domäne. Dabei beschreibt Initiative den gesamten Lebenszyklus von Analy tics-
Lösungen, von der Definition des zu lösenden Problems, über die Lösungsentwicklung
bis hin zu dessen Einsatz und Wartung in den Wertschöpfu ngsprozesse n. Mit einer
Vielzahl von Forschung smethoden, einschließlich Grounded Theory, Clustering, Case
Studies und der Q-Methodik, untersucht diese Arbeit S CA-Initiativen in explorativen und
konfirmatorischen Forschungsdesigns. I n vier wissensc haftlichen Artikeln werden
Untersuchunge n durchge führt zu (1) M erkmalen, die LSCM von anderen Domänen bei
der Durchführung von Analytics-Initiativen abgrenzt, (2) Analytics- Initiativen, die
derze it in der LSC M Domäne durchgeführt werden, (3) dem Prozess der Ausführung von
Analytics-Initiativen in LSCM, um we rtvolle Analytics-Lösungen zu schaffen, und (4 )
Barrieren, auf die LSCM-Organisationen be i der Durchführung von Analytics-I nitiativen
treffe n und den Maßnahmen, die sie anwenden, um die Barrieren zu über winden. Diese
Artikel haben resultiert in kartogra fierten Merkmalen zur Unt erscheidung von Domänen

VI
bei der Durchführ ung von Analytics- Initiativen und einem individuellen Profil von
LSCM bez üglich dieser Merkmale; sechs a bgegrenzt en Arche typen von SCA-Initiativen;
umfassenden Erklärungen der Erlangung von Wettbewerbsvorteile durch Analytics in
LS CM ; sowie Rahmenkonz epte von Barrieren und Maßnahmen in SCA. Einerseits
werden die Ergebnisse der einzelnen A rtikel dazu verwendet, um den Wert zu
beschre iben, den LSCM -Organisationen für ihre Kunden schaffen könn en . Andererseits
wird durch die weiterführende Kombination der Erge bnisse der Artikel mit 15
Prozessansätzen zum M anagement von Analytics-Initiativen ein ergänzender Ansat z zum
Management von Ana lytics-Initiativen, insbesondere von SC A-I nitiativen, entwickelt. I n
diesem Bestreben etabliert diese Arbeit Analyti cs als ein Werkzeug, das menschliche
Kreativität und Expertis e er fordert, um sein volles Potenzial zu entf alten. Als solches
Werkzeug bietet es Lösungen zur Unterst ützung des Menschen bei seiner
Entscheidungsfindung und kann zu verbesserten Ent scheidungen führen, wenn es wie
jedes a ndere Werkzeug im gee igneten Ansatz eingese tzt wird. Mit den oben
beschriebenen Forschungsergebnisse n bietet diese Arbeit Anleitung für Manager in
LSCM, um SCA-Initiativen so zu managen, dass sie diesen geeigneten Ans atz für
Analyti cs in LSCM verstehen und in di e Lage ve rsetzt werden, ihn für (Wettbewerbs -)
Vorteile und kontinuierliche Verbesserungen der Prozesse und d er Kundenzufriedenheit
einzusetze n.

VII

Content
Abstract ........................................................................................................................... III
Zusammenfassung ........................................................................................................... V
Content ........................................................................................................................... VII
Figures ......................................................................................................................... XIII
Tables ............................................................................................................................ XV
Abbreviations ............................................................................................................... XVI
1 Introduction ............................................................................................................... 1
1.1 Researc h Motivation .......................................................................................... 1
1.1.1 Theoretical Motivation ............................................................................... 2
1.1.2 Practical Motivation ................................................................ .................... 6
1.1.3 Summary Motivation .................................................................................. 9
1.2 Researc h Objective ........................................................................................... 11
1.3 Unit of analysis ................................................................................................ 15
1.4 Structure ........................................................................................................... 17
1.4.1 Introduction and theoretica l background .................................................. 17
1.4.2 Article 1: M apping Dom ain cha racteristics influen cing Analytics ini tiatives
17
1.4.3 Article 2: Arc hetypes of Supply C hain Analytics .................................... 18
1.4.4 Article 3: Explaining the Competitive Advantage Generated from Analytic s
with the Knowledge-based View ............................................................................ 19
1.4.5 Article 4: Overcoming Barr iers in Supply Chain Analytics ..................... 20
1.4.6 Managing Supply Chain Analytics ........................................................... 21
2 Theoretic Background ............................................................................................. 23
2.1 Logistics and Supply Chain Management ........................................................ 23
2.1.1 Logistics .................................................................................................... 23
2.1.2 Supply Chain Management ................................................................ ....... 24

VIII
2.1.3 Synopsis .................................................................................................... 25
2.2 Analytics and re lated terms .............................................................................. 26
2.2.1 Analytics .................................................................................................... 27
2.2.2 Big Data ..................................................................................................... 28
2.2.3 Data Science .............................................................................................. 29
2.2.4 Artificial Intelligence ................................ ................................................ 30
2.2.5 Synopsis .................................................................................................... 32
2.3 Analytics in different Domains......................................................................... 32
2.3.1 Supply Chain Analytics ............................................................................. 32
2.3.2 Marketing Analytics .................................................................................. 34
2.3.3 Healthcare Analytics ................................................................................. 36
2.3.4 Public Sector Analytics ............................................................................. 37
2.3.5 Sports Analytics ........................................................................................ 39
3 Mapping domain characteristics influencing Analytics initiatives - The example of
Supply Chain Analytics ................................................................................................... 41
3.1 Introduction ...................................................................................................... 41
3.2 Theoretical bac kground .................................................................................... 44
3.2.1 The matter of domain in Analytics ............................................................ 44
3.2.2 The impact of domain on Analytics .......................................................... 46
3.2.3 Modes of incorporating domain knowledge in Analytics initiatives ........ 48
3.3 Methodology ..................................................................................................... 50
3.3.1 Sample and data c ollection ........................................................................ 51
3.3.2 Data Coding and Ana lysi s ......................................................................... 53
3.3.3 Trustworthiness ......................................................................................... 54
3.4 Results and discussion ...................................................................................... 55
3.4.1 The map of characteristics of Analytics initiatives differe ntiating domains
55
3.4.2 Specifics of the LSCM domain ................................................................. 66

IX
3.5 Conclusion and directions for further Researc h ............................................... 70
3.5.1 Theoretical Implications ........................................................................... 70
3.5.2 Managerial Implications ........................................................................... 71
3.5.3 Future researc h and Limitations ............................................................... 72
4 Archetypes of Supply Chain Analytics Initiatives – an exploratory study ............. 75
4.1 Introduction ...................................................................................................... 75
4.2 Theoretical Backgrou nd ................................................................................... 78
4.2.1 Analytics ................................................................................................... 78
4.2.2 Supply Chain Analytics ................................ ............................................ 79
4.2.3 Dismantling Supply Chain Analytics Initiatives ...................................... 81
4.3 Methodology ................................................................................................ .... 84
4.3.1 Data Collection ......................................................................................... 84
4.3.2 Data Ana lysis ............................................................................................ 84
4.4 Results and Discussion ..................................................................................... 87
4.4.1 Cluster 1 – Educating ................................................................ ................ 87
4.4.2 Cluster 2 – Observing ............................................................................... 89
4.4.3 Cluster 3 – Alerting ................................................................................... 89
4.4.4 Cluster 4 – Advancing ................................ .............................................. 90
4.4.5 Cluster 5 – Refining .................................................................................. 91
4.4.6 Cluster 6 – Investigating ........................................................................... 92
4.4.7 Discussion on Archetypes ................................................................ ......... 92
4.4.8 Discussion on overcoming barrier s with archetype s ................................ 94
4.5 Conclusion........................................................................................................ 95
4.5.1 Theoretical Contribution ........................................................................... 97
4.5.2 Managerial Contribution ........................................................................... 98
4.6 Final remarks .................................................................................................. 100
4.6.1 Limitations .............................................................................................. 100

X
4.6.2 Future Research ....................................................................................... 100
5 Explaining the Competitive Advantage Generated fr om Ana lytics with the
Knowledge-based View – The Example of Logistics and Supply Chain Management103
5.1 Introduction .................................................................................................... 103
5.2 Theoretical Backgrou nd ................................................................................. 106
5.2.1 Knowledge-based view ........................................................................... 106
5.2.2 Analytics .................................................................................................. 113
5.2.3 Parallelism of knowledge-based view and Analytics .............................. 120
5.3 Methodology ................................................................................................... 124
5.3.1 Researc h Design ...................................................................................... 125
5.3.2 Data Collection ........................................................................................ 126
5.3.3 Data Ana lysis .......................................................................................... 129
5.3.4 Trustworthiness ....................................................................................... 129
5.4 Results and Discussion ................................................................................... 130
5.4.1 Starting position for Analytics initiatives ................................................ 130
5.4.2 Focus of A nalytics initiatives .................................................................. 132
5.4.3 Problem-solving process ................................ ......................................... 134
5.4.4 Roles in Analytics initiatives ................................................................... 136
5.4.5 Including exter nal expertise .................................................................... 138
5.4.6 Data as a resource .................................................................................... 141
5.4.7 Deploying Analytics solutions ................................ ................................ 143
5.4.8 The responsibilities of the user ................................................................ 145
5.4.9 Organiza tional factors of Analytics initi atives ........................................ 148
5.4.10 The long-term usability of solution in Analytics initiatives .................... 150
5.5 Conclusion ...................................................................................................... 151
5.5.1 Theoretical implications .......................................................................... 154
5.5.2 Managerial Implications .......................................................................... 155

XI
5.5.3 Future researc h and limitations ............................................................... 157
6 Overcoming Barriers in Supply Cha in Analytics – Inve stigating mea sures in LSCM
organizations ................................................................................................................. 159
6.1 Introduction .................................................................................................... 159
6.2 Theoretical Backgrou nd ................................................................................. 161
6.2.1 Supply chain Analytics ........................................................................... 161
6.2.2 Barriers of Supply Chain Analytics ........................................................ 163
6.2.3 Measures to fully utilize the benefits of Analytics ................................. 167
6.3 Methodology ................................................................................................ .. 170
6.3.1 Researc h Design ..................................................................................... 170
6.3.2 Data Collection ....................................................................................... 171
6.3.3 Data Ana lysis .......................................................................................... 173
6.3.4 Reliability ................................................................................................ 174
6.4 Results and Discussion ................................................................................... 176
6.4.1 Barriers .................................................................................................... 177
6.4.2 Measures ................................................................................................. 183
6.4.3 Discussion on applying measures a nd handling barriers ........................ 190
6.5 Conclusion...................................................................................................... 194
6.5.1 Managerial Implications ......................................................................... 195
6.5.2 Limitations and Further Researc h ........................................................... 196
7 Enabling LSCM organizations to use Ana lytics ................................................... 199
7.1 Scope of application ....................................................................................... 199
7.1.1 The focus of this thesis ................................ ........................................... 199
7.1.2 The scope of data c ollect ion ................................ ................................... 201
7.2 The value f or the custom er ............................................................................. 206
7.2.1 Indirect value for the cus tomers .............................................................. 206
7.2.2 Direct value for the customers ................................................................ 209

XII
7.3 An approach to manage Supply C hain Analytics i nitiatives .......................... 211
7.3.1 Motivation a nd procedure of deriving an approach to manage Supply C hain
Analytics initiatives ............................................................................................... 212
7.3.2 Comparison of approaches to manage Analytics initiatives ................... 215
7.3.3 A supplemented CR ISP-DM approach to manage S upply Chain Analytics
219
7.3.4 Case study-based eva luation of approach to manage Supply Chain
Analytics initiatives ............................................................................................... 234
8 Conclusion ............................................................................................................. 239
8.1 Summary ......................................................................................................... 240
8.2 Limitations ................................................................ ...................................... 244
8.3 Future Research .............................................................................................. 246
Refere nces ..................................................................................................................... 248
Appendix A ................................................................ ................................................... 266
Appendix B ................................................................................................................... 281

XIII
Figures
Figure 1: Summary of researc h motivation ................................................................ .... 10
Figure 2: A pr oblem’s definiti on ................................ .................................................... 14
Figure 3: Affiliation of thesis content to S EP ................................................................. 14
Figure 4: Focus of article 1 ............................................................................................. 18
Figure 5: Focus of article 2 ............................................................................................. 19
Figure 6: Focus of article 3 ............................................................................................. 20
Figure 7: Focus of article 4 ............................................................................................. 21
Figure 8: Intended goa l of aggregating and enriching the articles ................................. 22
Figure 9: (le ft) Duration of I nterviews with Experts, (r ight) Experts Experience in
Analytics ......................................................................................................................... 52
Figure 10: The paradigm scheme of components ........................................................... 54
Figure 11: Map of domain-specific aspects of Analytics ini tiatives .............................. 57
Figure 12: Characteristics of a Supply Chain Ana lytics Initiative ................................. 82
Figure 13: Dendrogram of Cluster Ana lysis with Ward's method .................................. 86
Figure 14: Cluster Eva luation (best evaluated in grey) .................................................. 87
Figure 15: Propose d Su pply Chain Analytics archetypes (no chrono logy or se quence
intended) ................................................................ ......................................................... 88
Figure 16: Knowledge integration illustrated ............................................................... 108
Figure 17: Sustainable c ompetitive advantage from knowledge illustrated ................. 110
Figure 18: Knowledge creation illustrated .................................................................... 112
Figure 19: Knowledge sources a nd transfer illustra ted. ............................................... 113
Figure 20: Visualiz ing the knowledge- based view ....................................................... 114
Figure 21: The process of Analytics initiatives illustrated ........................................... 116
Figure 22: Process accompanying conditions illustrated ................................ .............. 118
Figure 23: Ena bling adva ntages from Analytics illustrated................................ .......... 119
Figure 24: The value creation process of Analytics ini tiatives ................................ ..... 121
Figure 25: Creating competitive adva ntage from Analytics ......................................... 152
Figure 26: Barriers of Supply C hain Analytics ............................................................ 178
Figure 27: Measures to support Supply Chain Analytics ............................................. 184
Figure 28: Overview of cases for article 2 ................................................................ .... 203
Figure 29: CRISP-DM approach to Analytics initiatives (Chapman e t al., 2000) ....... 213

XIV
Figure 30: Comparison of sequence, number and scope of phases of management
approac hes to Analytics i nitiatives ................................................................................ 217
Figure 31: Overview of the sCRI SP-A to manage Supply Chain Analytics initiatives 219

XV
Tables
Table 1: Propositions and rival explana tions ................................................................ 127
Table 2: Case study interviewee s and organizations .................................................... 128
Table 3: Inter view Participants ..................................................................................... 172
Table 4: Overview of interviewe d experts for article 1 ................................................ 202
Table 5: Overview of interviewe d experts for article 3 ................................................ 204
Table 6: Overview of interviewe d experts for article 4 ................................................ 205
Table 7: Character istics of compared approaches to manage Analytics initiatives ...... 216
Table 8: Tasks of the orientation phase ........................................................................ 221
Table 9: Tasks of the business understanding phase .................................................... 224
Table 10: Tasks of the data understanding phase ......................................................... 226
Table 11: Tasks of the data preparation phase .............................................................. 227
Table 12: Tasks of the modeling phase ........................................................................ 229
Table 13: Tasks of the evaluation phase ....................................................................... 230
Table 14: Tasks of the deployment phase ................................................................ ..... 232
Table 15: Tasks of support phase ................................................................................. 234

XVI
Abbreviations

AI

Artificial Intelligence

AO

Category: Analytics objective

CRISP- DM

Cross-industry standard process for data mining

DALM

Data analytics lifecyc le model

DAT

Category: Data ma nagement

EDI

Electronic Data Interchange

e.g.

For example

ERP

Enterprise Resource Planning

ETA

Estimated- time -of- arrival

GPS

Global Positioning System

GSM

Global System for Mobile Communications

HUM

Category: Human involvement

IoT

Internet- of -Things

IT

Information tec hnology

KBV

Knowledge-based view

LSCM

Logistics and Supply Chain Mana gement

LSP

Logistics service provider

m in .

Minimum

N/A

Not available

No.

Numero

OEM

Original Equipment Manufa cturer

PADI E

process-application-data-insight-embed

RFID

Radio-frequency identification

RO

Researc h objective

SCA

Supply Chain Analytics

SCO

Category: Supply Chain objective

SCOR

Supply Chain Operations Refere nce

sCRISP-A

Supplemented CRISP-DM for Analytics initiatives

SEMMA

Sample, Explore, Modify, Model, and Assess

XVII

SEP

Strategy exec ution process

TA

Category: Type of A naly tics

TEC

Category: Technological aspec ts

TS P

Theory on structuring problems

UPGMA

Unweighted Pair Group Method with Arithmetic Me an

VOIP

Voice over Internet Protocol

WPGMA

Weighted Pair Group Me thod with Arithmetic Mean

y rs .

Years

XVIII

1
1 Introduction
“ The purpose of computing is insight, not numbers ” is the motto of Richard W.
Hamming ’s book “ Numerical methods for s cientists and engineers ” (1962, p. 395), whi ch
covers problem solvi ng with the help of c omputin g for scientists and engineers. The book
is an ea rly ex ample of modelling and data analysis that goes beyond th e question of
calculating and raises concerns about the purpose of calculations. The aut hor highlights
that c omputing may not only answer questions, but ra ther ca n he lp to gain understanding
of the situation around th e question that is examined. He indi cates a r esidual to be handled
– a lack of knowledge about the purpose of the ca lc ulation – and a proble m proposer who
may not exa ctly know, what he w ants. In the li ght of thi s thesis, the lacking knowledge
may include the actual problem, the applicability of the solution or whether a user would
actually apply the solution. These issues, which are managerial instead of numerical, are
similarly occurring for Supply Chain Analytics and are under investigation in this thesis.
1.1 Research Motivation
During the r esearch for t his thesis and a sho rt -lived timespan before it, a large body of
research about Analytics in Logistics and Supply Chain Management (LS CM) emerged,
motivating this thesis. To preemptively summarize the subsequently presented research
motivation, Analytics composes a field full of potential to reduce costs , improve
efficie ncy and enabl e new courses for actions for organizations in LSCM. However, it is
a c omplex fie ld with tec hnological, organizational and human-based drivers of
complexity leading t o a vast variety of barriers and reluctance hindering the successful
realiza tion of the desired be nefits and creation of v alue or rather v aluable so lutions. Thus,
this thesis does not inten d to develop further new technological solutions, algorithms or
models, organizations may equally be obstructed and reluctant to apply. The m otivation
of this thesis, found ed on the subseque nt section, is to ga in understanding a nd insights on
the se barriers, the reluctance and th e requirements to enable benefits from Analytics.
Based on understanding and insights, this thesis intends to provide organizations in
LSCM a nd organi zations exe cuting LSCM activities with means to identify, comprehend
and control these drivers of complexity . Consequ ently, the motivation is to enable th em
to successfully use Analytics, realize the desired b enefits and generate valuable solutions ,
such that they can create sustainable competitive advantage and continuous im provement.
In summary, this thesis focusses on mea ns and practices of the mana gement of Ana lytics
in LSCM – the management of “ Supply Chain Analytics ” (SCA) .

2
1.1.1 Theoretical Mot ivation
Prior to discussing the body of rese arch, a short notice on taxonomy is nece ssary. For the
purpose of research on manage ment of Analytics, the terms Analytics, Data Scienc e, Big
Data (Analytics) and Artificial Intelligence (AI) a re considered as synonyms. As will be
discussed in se ction 2.2, t hese terms show differen ces in the founda tional technologies or
the deployed analytical methods but are identically based on the analysis of data to impact
decision-making in org anizations and displ ay sim ilar organizational ef fects and bar riers.
Hence , the terms are regarded synonymous in thi s thesis for their objective to cre ate v alue
from data.
The following discussion on theoretical motivation considers scientific literature on
theoretica l arguments and inference f rom usually smaller samples ( e.g., case studies and
small sample surveys). From thi s li terature , the extracted aspects of eligibili ty,
accessibility, performanc e increase, competitive advantage, and barriers have been
extracted.
A primary consideration ha s to be, whether the domain of LSCM is eligible to adopt and
apply Analytics. Thereby, eligibility is supposed to describe wheth er the d omain meets
the requirements and has a need for Analytics. T he relationship between Analytics and
LSCM has been emphasized in the li terature fr om various points of vi ew. LS C M is
considered as an early ad opter and traditional user of analytical methods such as statistical
forecasting and Operati ons Research (Chae, Olson, et al., 2014; Davenport, 2009;
Matthias et al., 2017; Sanders, 2016; Souza, 2014) . LSCM has developed into a
knowledge-ba sed domain relying on data and analytics for better decision -making.
Additionally, many activi ties in LSCM focus on ex ploitation of data (Brinc h et al., 2018;
Trkman et al., 2010). A variety of activities benefit from Analytics and accurate provision
of data, since the decisions in these activities are rich in optional actions and requirements
to be c onsidered, as we ll as their trade-offs (Chae, Yang, e t al., 2014; Souza , 2014; W ang
et al., 2016). In addition, the demand for Analytics in LSCM is growing due to incre ases
in the complexity of d ecision -making caus ed b y increased competition, uncertainty,
customization, need for sustainability and globalization (Chae, Yang, et al., 2014; Lai et
al., 2018; Roßmann et a l., 2018). The domain of LSCM is also c onsidered a s data int ense
due to produ cing a lot of operational da ta per org anization which a re eventu ally share d in
collaborative activities with business models (e.g., forth-party logisti cs providers)
completely reliant on data exchange for coordination, planning and integrat ion of shared

3
logistical tasks (Chae, Y ang, et al., 2014; Dutta a nd Bose, 2015; Hopkins and Hawking,
2018; Ludwig, 2014). This makes LSCM a good fit for Ana lytics. This characteristic ha s
been recognized by p ractitioners for its potential of high returns for org anizations (Jeske
et al., 2013; Kiron et al., 2012; Lavalle et al., 2011) . Further, an achiev ed increase in
performa nce may e ventually improve the performance of supply chain partners (Oliveira
et al., 2012; Richey et a l., 2016) . In summary, LSCM is very well suited to employ
quantitative methods from Analytics to exploit data.
The aspect of accessib ility shall describe the te chnological changes that enable ne w
opportunities for creating value from Analytics in LSCM and, of course, in ot her domains.
The most famous compo nent of this, is the huge a mount of data collected t oday, since a
considerable number of publi cations on SCA mentions the newest pr ojection of the
wo rldwide amount of dat a in some future year ( e.g., Chae, Yang, et al., 20 14; Roßmann
et al., 2018). The incr easing ease of collecting d ata has led to a n increase of c ollected data
in LSC M as well (Schoenherr and Speier-Pe ro, 2015; Waller and Fawcett, 2013). Along
the process, data is collec ted from all sorts of sources including GPS sensors, RF ID
sensors crea tively used in various forms, mobile de vices, tra nsactional I T systems, point-
of-sales devices, differen t forms of scanners, and i ncrea singly f rom machines and assets
which are delivered with data collection abilities through several sensors s ending status
and per formance ( Kache and Seuring, 2017; M atthias et al., 2017; Richey et al., 2016;
Sanders, 2016; Souza, 2 014; Wang et al., 2016) . Additional ly , data may come from
external sources such as data exchange with partners and customers, social media,
customer feedback, or external signals su ch as traffic conditions (Kache and S euring,
2017; Matthias et al., 2017; Sanders, 2016; Srini vasan and S wink, 2018) . Besides data
collection, the exchange of data has improved especially in velocity due to the int ernet
giving real-time abilities in an internet speed like information exchange internally and
externally (Kache and S euring, 2017; Sanders, 2016; Wang et al., 20 16) . Finally,
technologies and methods have improved. Methods for analyzing data have had large
developments, espe cially in machine learning te chniques to exploit the larger amount of
data and sources (Chae, Olson, et al., 2014; Sande rs, 2016; Waller and Fawcett, 2013) .
Technologies like in-memory da tabases, virtualization (“cloud computing”) and
distributed computing (e.g., Hadoop) provid e the technical abilities to exploi t the data
(Hahn and Packowski, 2015; Hopkins and Hawking, 2018; Roßmann et al., 2018).

4
The review of scientific literature p resented an ext ensive potential for the p erformance
increase of LSCM activ ities. However, taking a deeper look, most effects are indirect
from better and more fre quent vis ibility, gained transparency into the past , present and
future, and resulting decision-making. Ha ving an extensive ly transparent insi ght int o past
performa nce, issues, fa ilures/ unwanted beh avior of assets or employe es, costs of
activities and suppliers, bo ttlenecks as well as all ongoing activities along the supply
chain allows to identify i mprovement potential and distribute resources in a more efficient
way (Brinch et a l., 2018; Chae, Yang, e t al., 2014; Dutta and Bose, 2015; Sanders, 2016;
Wang et al., 2016; Zhu e t al., 2018). This is key t o mastering the increasing challenging
environment for logistics, such as dense urban are as (Straube, Reipert, et al., 2017) .
Visibility about the future due to forecasts and improved understanding of uncertainties
in suppl y, demand and costs allows to create better and optimized plans for improved
utilization (effectiveness) with reduc ed slack resources and expensive reactions ( e.g.,
safety stocks, ove rtime, expedited shipments, markdowns) (Hazen e t al., 2014; Riche y e t
al., 2016; Roßmann et al., 2018; Sanders, 2016; S rinivasan and S wink, 2 018) . Finally,
monitoring the present ac tions in real-time allows dynamic decision -making for faster
reac tions to changing market conditions, changing needs of customers , and suppliers,
degrading pe rformance and incidents with subsequently faster correctiv e ac tions and
shorter downtime (Chavez et al., 2017; Dutta and Bose, 2015; Kache and Seuring, 2017;
Wang et al., 2016; Zhu et al., 2018) Given that improved decisions are m ade from th is
transpare ncy and visibility, improving efficienc y, utiliza tion and time, it ev entually lea ds
to reduc ed costs (Dutta a nd Bose, 2015; Ka che and Seuring, 2017; Richey et a l., 2016) as
an indirect effect from Analytics applie d to LSCM. An important c oncluding point is that
Analytics effects are indi rect by enabling and trig gering actions with improved outcome
(Chae, Yang, et al., 2014) and thus do not guarantee performance increase from
investments in Analytics if process f lexibility and process maturity do not allow to
execute the actions (Oliveira et al., 2012; Srinivasan and Swink, 2018) . There is no
performa nce incr ease from analyzing data itself, w hat makes measuring An alytics impac t
on LSCM performance difficult.
Researc h has also argued for the comp etitive ad vantage created from adopting Analytics
in LSCM. Scholars have argue d for competitive advantage based on observing
performa nce increase, gained market share, a nd improved manag ement decisions –
genera lly an im proved position in competition – after investing and adopt ing Analytics

5
to LSCM (Dutta and Bos e, 2015; Matthias et al., 2017; Oliveira et al., 201 2) . Adding to
this list is the strategic opportunity for improved diffe rentiation from competitors, which
is, however, not directly labeled as competitive advantage (Roßmann et al., 2018). It has
been argued, that SCA itself is a resource, which is valuable, inimitable, and non -
substitutable and thus is resultingly a source of s ustained competitive advantage (Chae,
Olson, et al., 2014). Furthe rmore, scholars argue it to be a second order suppor t or driver
of capa bilities, which g enera te competitive advantage. This includes manufacturing
capabilities, re presented by the ability to manufa cture goods of high qualit y, flexible and
with low costs for customer satisfaction, as well as effectiveness and efficiency
capabilities, and innovation ca pabilities (Cha vez et al., 2017; Hopkins and Hawking,
2018; Trkman et al., 2010). However, these interpretations of the impact of Analytics to
LSCM are theore tical.
As indicated above, adopti ng and applying Anal ytics is no sure -fire suc cess. There ar e
barriers and cha llenges to using it in LS CM and, thus, obstruct the benefits explained
above, and managers require guidanc e to overcome these b arriers . An extensive
discussion on barrier s fo llows in a later part of this thesis. As a preliminary re view, i t
shall be emphasized, that barriers are observab le at multiple stages and influencing
multiple re sources. On a mana gement level, commitment and knowle dge might be
missing (Lai et al., 2018 ; Richey et al., 2016) . On an operational level, the employee s
might be inexpe rienced with Ana lytics, uncre ative about using da ta to their advantage, or
unable to explain the value of their ideas (Kache and Seuring, 2017; Sa nders, 2016;
Schoenherr and Speier-Pero, 2015). On a cultural level, a lack of openness to new d ata
driven solutions may exist, possibly a strong unwillingness aga inst it (Dut ta and Bose,
2015; Richey et al., 2016) . Concerning resou rces, I T systems’ readiness for Analytics or
rather the ability to integrate the systems’ data might be missing (Kache and Seuring,
2017; Richey et al., 2016 ; S choenhe rr and Speier - Pero, 2015). The d ata, which are core
to Analytics, might not b e available or hav e a bad quality, resulting in ina ccurate o r faulty
analyses (Hazen et al., 2 014; Schoenherr and Speier-Pero, 2015). Finally, the physical
process may not be ready to exploit the opportunities presented by An alytics since
maturity and flexibility a re missing (Oliveira et al., 2012; Srinivasan and Swink, 2018) .
In summary, organizatio ns require guidance for managerial actions for Analytics in
LSCM before they can exploit the quantitative methods.

6
1.1.2 Practic al Motivation
For th e pr actical motivation, reports from o rganizations ( e.g., t echnology and Analytics
providers, service providers and associations in LSCM, consultancies) have been
considered, which infer the eff ects of Analytics o n LSCM from their own projects or
larger samples. Discusse d below are the extracted aspects of gains in efficiency, customer
orientation, business potential, and reluctance. These aspects strongly relate to the
theoretica l aspe cts displ ayed above but p resent the perspectives and comm unications
from prac titioners.
The practitioners’ perception, expectations and actual gains in efficiency are similar to
the theoretica lly argued p erformance effects. Analytics has been reported to gain strategic
priority due to improve d dec ision-making abilities from increa sed visibility, eve n though
the monetary r eturn is unclear (Johnson and Cole, 2016; Thie ullent et al., 2016) . Thereby,
organization-wide strategies to exploit Analytics are indicated to be more efficient with
leaders in LSCM expecting higher returns than foll owers (Pearson et al., 2014; S chmidt
et al., 2015). This concurs with the research results emphasizing the need for process
maturity to gain benefits from Analytics in LSCM. The reported gains from Analytics in
LSCM are usually discussed re lated to applic ations. A frequently used e xample is
predictive maintenance of machines and tr ansportation assets, which achiev es benefits
like less fa ilures a nd higher availability, and results in higher yield, higher utilization and
subsequently reduced cost (Lueth et al., 2016; Monahan et al., 2017; Opher et al., 2016;
Thieullent et al., 2016). R ea l-time visibility of assets and process conditions is repeatedly
emphasized for achieved asset control and uti lization from dyna mic adjustments on new
information (“real - time opti mization”) as well as fast reactions on in cidents, both
avoiding cost (Henke et al., 2016; Jeske et al., 2013; Pearson et al., 2014; Thieullent et
al., 2016; UPS, 2016). Fu rther specifically named applications are dem and forecasting
(with increa sed accuracy), which avoids cost of committi ng to o much or too few
resources, and syst ematic analysis of expenses, which allows improved control of
expenses (Henke et al., 2016; Jeske et al., 2013; Johnson and Cole, 2016; Phil lipp s and
Davenport, 2013). Additi onally reported improvements are shortened ti me of p rocesses,
optimized resource consumption, identification of issues and their root cause, improved
process quality a nd performance, r emoval of unnece ssary process steps, an d opti mization
of proce sses along the supply chain, all eventually resulting in cost savings (Erw in et al.,
2016; Henke et al., 2016; Jeske et al., 2013; Johns on and Cole, 2016; Ophe r et al., 2016;

7
Thieullent et al., 2016). The expectations from organizations planning to adopt S CA, are
similar to these reported benefits (Henke et al., 2016; Kersten et al., 2017; Thieullent et
al., 2016). In summary, reports show a similar impact of SCA as scientific research.
Considering the revi ew above, th e reported and theorized benefits are princip ally
internally focused on o perations and s how li ttle effect on the customer. However,
Analytics is also reported as a critical mean for cust omer orientation in LSC M. Research
has mentioned the customer briefly as an eventual beneficiary, sinc e monitoring of
suppliers and service providers becomes easier and resulting in the fulfillment of
customer requirements (S anders, 2016; Wang et al., 2016). Further, it becomes possi ble
to perceive c ustomer needs and behavior in more detail, and a ct more customer oriented,
which has been stated as ke y a rgument for investments in Analytics (Kache a nd Seuring,
2017; Ramanathan et al., 2017; Sanders, 2016) . Practitioners’ reports address th is aspect
from several p erspectives. One such effe ct being increased customer sati sfaction from
avoided product shortag es , is mentioned repeatedly (Jeske et al., 2013; Thieullent et al.,
2016). Further, Ana lytics in LSCM has been explained as nec essary to cope with changes
in customer behavior and requirements based on B usiness- to -Customer trends and the
digital transformation. These changes lead to increased expectations in customization of
products and se rvices, immediacy and convenience, which result in higher demand
uncertainty (Monahan et al., 2017; Opher et al., 2016; Thieullent et al., 2016; UPS, 2016) .
Business- to -Business sector customers ar e expected to demand similar data and
Analytics-driven se rvices as they observe in Business - to -Customer sectors l ike tracking
and tracing, but also transparency and comparabil ity of cost and qu ality, and full -service
offerings (Dichter et al., 2018; Garner and Kirkwood, 2017) . Additionally, Analytics is
argued as a critical driver for improved understand of customer needs, the context of their
needs, and reasons for their satisfaction or dissatisfaction (Dicht er et al., 2018; Jeske et
al., 2013). Based on Analytics, internally e xecuted customer segmentation and externally
offered data-driven services are d rivers to address customers more indi vidualized and
oriented on their identified needs (Jeske et al., 2013; Kersten e t al., 2017).
The previous aspe ct alr eady indi cates how business of LSCM is changing, but the
business p otential from S CA is far greater. The business potential from Analytics in
LSCM is highlighted by organizations perceiving it favorably to use LSCM as starting
business area for Analytics in the organization d ue to promising returns, a source o f
untapped efficiency and continuous improvement, and a mean to create reliable

8
orchestra tion of supply chains without the need of full control (Jeske et al., 2013; Kiron
et al., 2012; Lavalle et a l., 2011; Opher et al., 2016). However, this pa ragraph sh all
highlight the relevanc e for data-drive n busi ness mo dels a nd digi tal services in LSCM du e
to new for ms of collabora tion and innovation ena bling ne w re venue streams (Jeske et al.,
2013; Kersten et a l., 2017; Straube, Bahnse n, et a l., 2017). Business models are impacted
by Analytics in three di fferent ways: (1) upgrading existing services and products by
incorporating Analytics int o them, (2) changing the busin ess model due to new possible
actions enabled by Anal ytics, and (3) creating n ew Analytics -driven bus iness models
(Lueth et al., 2016). Upgrading the existing business models can result from enhancing
products and services with data and Analytics-d riven features that add value to the
customer such as end- to - end track and tr ace (“visi bility”), or end - to -end bo oking (Dichter
et al., 2018; Lueth et al., 2016). An example of changing the business model in LSC M
based on Analytics would be crowd-based last mile solut ions, which depend on analysis
of r eal -time data streams on demand and available supply in the crowd (Jeske et al., 2013) .
The reports fur ther prese nt examples between up grade and change of bus iness models,
taking and redesigning existing services such as brokerage and forwarding with Analytics
to prove better service quality, automation for increased efficiency, and advanced abilities
to identify opportunities for customers (Dichter et al., 2018; Monahan et al., 2017) .
However, these opportunities are under threat from technology-savvy organizations,
which might deploy Analytics-driven innovations fa ster and shape customer expectations
while attacking profitable processes and might be preferre d by shippers due to the savings
opportunities (Dichte r et al., 2018; Ga rner and Kirkwood, 2017). Finally, an e xample for
new business models fo r LSCM is the monetization of data including offerings to
completely new partners. Data can be collected with Inter net-of-Things (IoT) devices
attached to dist ributed an d moving assets in the network or is already collec ted to execute
global operations (Dichte r et al., 2018; Jeske et al., 2013). Thus, revenue may be
genera ted from data on climate, pollution, tra ffic, origin a nd destination pairs, w hich can
be of intere st to governmental agencies, economic analysts, insurance s or b anks.
In line with the barriers discussed in the theoretical motivation , but in contrast to the
potential value of Ana lytics for LSCM, practitioner reports show reluctance in adopting
An alytics to LSCM, which, like barriers, leads to missed benefits of analytics . Scientific
research has bri efly indi cated missi ng adoption and investments (Brinch et al., 2018;
Schoenherr and Speier-Pero, 2015) . B ut the presented picture in prac titioners’ reports is

9
more dramatic. These reports pre sent multiple a reas where organizations in LS CM
struggle with Analytics or with creating a condu cive environment to adopt Analytics,
since organizations experience challenges in collaboration, workforce, cost and data
(API CS, 2015; Kersten et al., 2017; Pearson et al., 2014; Thieullent et al., 2016) . S ome
organizations adopting Analytics do not realize the aspired success due to the barri ers or
missing commitment (Thieullent et al., 2016). However, some organizatio ns also see no
priority to invest in Anal ytics. A varie ty of reports (LSCM specific surveys and cross -
industry comparisons) id entify LSCM as a “laggard” in An alytics with 50 % to less than
10% of organizations reported to have implemen ted some form of Analytics (Erwin et
al., 2016; Kersten et al., 2017; Pearson et al., 2 014; Thieullent et al., 2016) . Taking a
deeper look, UPS presents im plementation spanning over several levels of maturity with
few organizations achieving the highest levels and warns about a wide ning gap with
latecomers at risk of becoming disadvantaged in competition (UPS , 2016). In addition,
LSCM is below average in cre ating value from Analytics in cross-industry comparison,
is ranked lowest as thou ght leader in business fu nction comparison and about 40% of
respondents in a LSCM survey don’t plan to invest (Bange et al., 2015; E rwin et al., 2016;
Kersten et al., 2017). Germany is especially highlighted for investing heavily in ha rdware
under the industry 4.0 mo vement while not investi ng in Analytics in a comparable manner
which creates a risky im balance (Thieullent et al., 2016) . Today’s high degree of
excellence in LSCM has been achieved without Ana lytics a nd the sector take s pride in it,
but there is a limit for these methods, which Analytics can overcome ( UPS, 2016) .
Analytics has currently no priority for many organiza tions in LSC M, with some
organizations expect ing it to become critica l within 5 years and others never (Johnson
and Cole, 2016; Lueth et al., 2016; P earson et al., 2014; S chmidt et al., 2015). B ut, as
mentioned above, shipp ers increasingly rely on Analytics -driven technologically savvy
organizations, which abs orb some profitable servi ces of traditional LSCM organizations
and “ it seems there ’s a disconnect b etween what shipp ers and [Logist ics Service
Providers] believe will happen ” (Johnson and Cole, 2016, p. 12).
1.1.3 Summary Motivation
As illustrated in F igure 1 , review ing the s cientific literature and p ractitioners’ reports has
presented an extensive range of advantages and va lues that can be gained from adopting
and employing Analytics in LSCM. While the advantages primarily re volve around
improvements of p rocesses to increase efficiency, increase utilization, r educe times, and

10
improve quality, they are widely expected to r educe costs as indirect effect due to the
process improvements. These have been interpreted as drivers of competitive advantage
in scientific literature and competition-cruc ial in practitioners-oriented publications. The
expectations and e arly adopter examples re port im proved customer orientation and newly
crea ted business opportu nities.
However, the review als o highlighted several issues to a chieve th e se aspired benefits. A
multitude of challenges are reported for using SC A, which are h ardly connected to
analytical methods but instead to the creation of an organizational and technologica l
environment to make the use of Analytics possible. Further, some organizati ons in LSCM
are unable to recognize how or which benefits can be achieved and need guidance for
Analytics. Building maturity in Analytics is essential for these organizations to compete
in an increasingly digitalized marke t. This market will see further ma rket entries from
organizations without a b ackground in LSC M but ea ger to exploit oppor tunities based on
tools such as Analytics and determined to gain market share with it. Currently, LSC M
organizations already co mpete with IT organizations employing digital business models
and may exp erience even more competition , if IT organizations start to pro vide physi cal
processes with autonomous vehicles as w ell (Ludwig, 2017; Straube, Bahnsen, et al.,
2017). The iss ues tha t need to be addressed and solved by this thesis are therefore
manageria l issues of guiding organizations to a mature state to execute SCA.

Eli g ib i l i ty : E arl y a d o p t i o n , d a ta -i n t en se d ec i s i o n - m a k i n g , co m p l e x i ty a n d
u n ce rta i n t y
Ac c e ss ib ili ty : In c re a s i n g l y e x te n si v e d a ta c o l l e c ti o n ,
p ro g re s si o n i n An a l y t i c s m e th o d s
B a r r ie r s : M i s si n g
k n o w l e d g e a t a l l l e v el s ,
Da ta a n d tec h n o l o g y
i ss u es
Re l u cta n c e : L ag g a rd
p o si ti o n , wi d e n i n g g ap
fo r l a te c o m e rs , l o w
p ri o ri ty
Cus to m e r O r ie nta tio n : Im p ro v e d u n d e rs tan d i n g a n d re a c ti o n to
c u s to m e r n ee d s, i n c re a se d c u sto m e r s a ti s fa c ti o n
Pe r f o r m a nce in cr e a s e :
Vi s i b i l i ty , p ro ce ss
e ffi c i e n c y a n d u ti l i z ati o n
g a i n s , c o st re d u c ti o n s
B u s in e s s Po t e nti a l:
Up g ra d ed a n d n e w
b u si n e ss m o d e l s , n ew
re v en u e s trea m s
G a in in Ef f icie ncy :
Dy n a m i c a d j u s tm e n t s,
i m p ro v e d re a c ti o n s,
i n cre a s e d a v ai l a b i l i ty
Co m p e ti tiv e Ad v a n ta g e : An a l y ti c s -d ri v en
e d g e o v e r c o m p e ti ti o n i n e ffi c i e n cy , i n n o v a ti o n a n d q u a l i ty

Figure 1 : Summary of research motivation

11
1.2 Research Objective
As presented in the previous section, major struggles for organizations in LSC M and
managing LSCM activities – from here on “ LSCM organizati ons ” – with Analytics are
manageria l and this thes is intends to provide guidance to overcome them . To form an
appropriate research desi gn to address these strug gles, this thesis assumes a managerial
approac h, adopted from strategic management, to cre ate specific re search objec tives,
develop directions for the thesis and gain structure. Hence, this thesis follows the five-
stage strategy exec ution process (SEP) as presented by Gamble et al. (2015). Besides
structure, the S EP provides an overa ll coherence to this researc h and a practitioner-
oriented terminology for the research process, which fits the application-oriented nature
of this thesis. The following list provides the adaptation of the SEP of Gamble et al.
(2015) to this thesis:
• Stage 1: Developing a strategic vision, mission and values [of the thesis]
• Stage 2: Setting objective s [for the scientific articles compiling the thesis]
• Stage 3: Crafting a strate gy to achieve the objectiv es [by creating research d esigns
for the scientific articles] and move the [thesis] along the intended path
• Stage 4: Executing the strategy [ by conducting the research]
• Stage 5: Evaluating and analyzing the external environment and the [research’s]
internal situation to identify corrective a djustments
Starting with Stage 1 o f t he SEP , the vision is sup posed to describe course and direction
(Gamble e t al., 2015). Ex plicitly, the vision of the thesis is:
LSCM organi zations ar e enabled to use Analytics such that it creates a
sustainable competitive advantage and continuous improvement of processes and
customer satisfaction .
There are three components to this vision. First, organizations shall be enabled, which
addresses the ir capabilities to use Analytics and to consequently deploy th e results into
business processes and h aving an organization wi de acceptance for and fami liarity with
Analyt ics. The word ‘enabled’ thereby refers to goal-oriented and delibe rated actions
when using Ana lytics an d setting up a supporting organizational environment. Second,
the effect from using Analytics shall be sustainable competitive advantage. By using
Analytics, organizations are suppos ed to posi tion themselves better in t he market by
having superior products, services, or value -added servic es a nd possessing deeper insi ght

12
into their mar ket. Third, t he internal effect shall be continuous im provement of proces ses
and customer satisfaction for products and service s. These examples ought to underline
that proc ess improvement can occ ur in forms perceptible by c ustomers or solely internal,
while both can provide value to the focal orga nization and customers.
From th is vision, a mis sion is derived. The mi ssion de scribes the present scope and
purpose (Gamble et al., 2015), in this case, of the thesis. This mis sion repre sents the
overall research objective of this thesis. Regarding the research mot ivation (section 1.1),
organizations hav e high expectations towards An alytics but lack the ability to use it in a
successful manne r, to provide value f ro m it or are not enabled to use it at all.
Consequently, the mission /research objective of this thesis, pursued by coll ecting and
analyzing empirical evidence from orga nizati ons successfully applying Analytics, is to:
Provide guidance that enabl es LSCM organizations to use Analytics successfully
and turn it into sustainable competitive advantage and continuous improvement
of processes and customer satisfaction .
Following the vision, “ enabl e” refers to goal-oriented and deliberated actions and the
existence of a supporting organizational environment in LSCM organizations.
To conclude stage 1 of the SEP , values a re dev eloped for pursuing the visi on and mission
statement of this thesis. Due to the a ctivities bein g of a re search natur e, the values for this
thesis are derived from recommendations for creating influential scie ntific results
(Fawce tt et al., 2014). Th e resulting values for this thesis are as follows:
(1) The divergence from pr evious research is clearly articulated and the contri bution
explicitly presented
(2) The research is justified by clearly highlighting the rea soning behind conducted
research a nd shortage in existing research
(3) The research is w ritten w ith precisi on to create un derstanding and persuasion for
the results.
(4) The research is grounded in existing theory and uses it appropria tely
(5) The methodology and data c ollection process are explained and justified in detail,
transpare nt and adhere to scientific standards while bias is aimed to be minimiz ed.
(6) Results are tested for validity and reliability.
Subsequently, objectives are set to convert the vision into specific targets in stage 2 of the
SEP, whereby obje ctives represent desired results in the SEP (Gamble et al., 2015) . To

13
derive the obje ctives, this thesis adapts the theory on structuring problems (T SP) from the
early AI research, which provides abstrac t directions for solving a problem (Sim on,
1973). According to th e TSP, a problem is defined by the following characteristics:
(1) Defined criteria to test proposed solutions
(2) Problem space delineating an initial state, a goal state and all other reachable states
(3) Attainable and legal state changes in the problem space
(4) Knowledge about the problem is presented by the problem space
(5) The problem space reflects the behavior of the external world
(6) Processes o f state changes require a practical amou nt of activit ies (“computa tion”)
and information for the processes is effectively available
The definitiveness o f thes e characteristic specifies t he problem’s posi tion on a theoretical
continuum between well-structured and ill -structured problems . Ac cording to the TS P,
the position on the struct ure continuum d etermines the ability of a “ solver ” to solve the
problem (Simon, 1973) – since the solver possesse s guidance to solve the problem . For
the purpose of thi s thesis, the solvers are LSCM managers using the results of this
research to solve their problems. Further, thei r problems are to enable their LSCM
organizations to us e Analyt ics in a manner creating sustainable competitive advantage
and continuously improving processes and custom er satisfac tion. Transferring the
abstract TS P to this thesis allows sub res earc h objective to be de rived, which in turn
contribute to defining this problem ’ s ch aracteristics and moving it towards being well -
structured. Consequentially, four characteristics have been chosen du e to th eir relevanc e
and close relation, as illustrated in Figure 2. The sub research objectives are as follows:
(1) Definition of the problem space : Identify th e different dimensions, which
delineate the states of LSCM organizations adopting and e mploying Analytics.
(2) Definition of the in itial stat e : Describe and ch aracterize the current state of
LSCM organizations using Analytics.
(3) Definition of the solution state : Describe and characterize how LSCM
organizations create sustainable competitive advantage and continuous
improvement of processes and customer satisfaction.
(4) Identification of operators to chan ge state s and conditions of their
applicability : Ide ntify o perators allowing a p ath of state ch anges from the initial
state of LSC M organizations using Ana lytics to the solut ion state along reachable
states in the problem space.

14
For the sake of completeness, furthe r characteristics have been formulated, which would
be requi red to be define such that the structure of the problem moves further towards a
well-structure (Simon, 1973). First, the differences distinguishing states and tests to detect
the presence of the se differences need to be defined. Second, conne ctions of operators to
the reduction or removal of the spec ific differences of states need to be established.
The four articles of this cumulative thesis will fo cus on consecutively addressing each
in dividual objective with specific research questions. As illustrated in Figure 3, the SEP
provides furth er stru cture to this thesis. S tage 3 of the SEP demands for strategies that
address how the objectives are a chieved (Gamble et al., 2015), which will be explained
in the next section and the motivation for the individual research design of the articles .

P r o b l e m
S p ac e
(a s i m p l i fi ed 3 D
R ep res en t at i o n )
D i m e ns i on 1 t o d es cri b e s ta t e o f s olut ion an d a c tion s
D i m e ns i on 3 t o d es cr ib e s ta t e o f s olut ion a nd a ctio ns
S o lu tion S tate : t h e
s o l u ti o n c o nsi d er e d a s
b est o r a s p i re d to th e
p rob l e m
I n i t i a l S t a te : t h e c u rr e n t
s o l ut i o n t o th e p rob l e m
Op e rators a l l o w i ng to m a k e l e g a l
m o ve s t o s h i ft fr om In i ti al S ta t e to
S o l ut i o n S ta t e
D im ens i on 2 to d es c ri b e s ta t e o f
s o lutio n a n d a c tio n s

Figure 2 : A problem ’ s definition

S tra t e g y E x ec u t i o n Pr o ce s s T h es i s
Sta g e 1 • V i s i o n
• M i s s i o n
• V a l u es
• S ec t i o n 1 .2 ( t h e s i s v i s i o n , t h e s i s
m i s s i o n / r e s e a r ch o b j ect i v e,
r es e a r ch v a l u e s )
Sta g e 2 • o b j e c t i v es • Sec t i o n 1 .2 (r e s e arch s u b o b j ect i v es
b a s e d o n T SP )
Sta g e 3 • S t r at eg i e s t o ac h i ev e
o b j e c t i v e s
• S ec t i o n 1 . 4 ( i n d i v i d u a l s u b -s e c t i o n s )
• A r t i c l e 1 – 4 ( i n t r o d u c t i o n ,
t h eo ret i c a l b a ck g ro u n d )
Sta g e 4 • E x ec u t i o n o f s t rat e g y • A r t i c l e 1 – 4 ( m e t h o d o l o g y , r es u l t s ,
d i s c u s s i o n )
Sta g e 5 • E v a l u a t i o n
• A d j u s t m en t s
• A r t i c l e 1 – 4 ( l i m i ta t i o n s )
• S ec t i o n 7 ( s co p e, cu s t o m e r v a l u e ,
m a n a g i n g SC A )
Th e o r y o n
s t ru ct u ri n g
p ro b l em s

Figure 3 : Affiliation of thesis content to SEP

15
These strategie s construct the strategic plan of this research and thus the overa ll research
objective of this thesis. The execution of the strategies (Stage 4) is presented by
conducting research in goal -oriented data collection, analysis and interpretation. Finally,
evaluating performance and ini tiating corrective adjustments (Stage 5) corresponds to
validation of results of each artic le and Section 7 o f this thesis.
1.3 Unit of analysis
After the research objective, the sub re search objectives a nd their construc tion have been
explained above, the u nit of analysis must be delineated. This thesis focusses on
Analytics initiatives, a special t erm defined in section 2.2, executed by LS CM
organizations. As such, the unit of analysis does neither concern s upply chains,
organizations or the characteristics of data -driven versions of them. Instead, it concerns
processes, th eir architecture and their man agement in organizations. Hence, the St. Gallen
Management Mode l (Rü egg-Stürm, 2003) is used to delineate the unit of analysis.
Regarding the processes considered, an Analytics initiative produces infrastruc ture for
business processes in form of Analytics solutions such as customer processes, valu e
crea tion processes o r val ue innovation pro cesses . The effects on th ese p rocesses will be
discussed but are not in focus of the investigation. Thus, negotiations with customers on
analytics services, chan ges of employee profiles and roles in value creation of new
business models are not in focus of this th esis. However, th e initiatives creating the
Analytics solutions are thus supporting process es . While this thesis investigates these
supporting proc esses, it further investigates their design, control and organiza tional
structure, which represents their manage ment processes as well.
In detail to management processes, normative management has several points of contact
to Analytics. The ethical us e of data, transparency of employees’ actions, adherence to
legal regulations on data priva cy and sec urity, data governance or the automa tion of jobs
are r elevant to the holisti c view on Analytics. These topi cs are investi gated for thei r
impact on the execution of Analytics, but since th ey are not part of th e ex ecution itself,
no in -depth investigation will be conducted, or designated frameworks designed. In
contrast, the strategic management of Analytics initiatives is specifically investigated in
this thesis. Thereby, on the one hand the use of Analytics initiatives to cre ate c apabilities
that provide responsiveness to market signals and improve the competitive posi tion are
investigated. On the othe r hand, the capabilities themselves are investigated in fo rm of
initiatives organizations execute. R egarding the operative management, practices are

16
investigated that concern leadership of employees, budget and qu ality management.
However, none of the pra ctices is individually investigated in detail. Rather an ov erview
of practices relevant to the ex ecution of Analytics i nitiatives shall be c reated. In the sense
of providing guidance to execute Analytics initiatives, this thesis focusses on providing
well founded directions and options, which are represented by th e possible capabilities,
crea tion of these capabil ities, and practices to improve the capability creation process.
However, the thesis does not assume any context of Analytics initi atives beyond the
application in LSCM. Detailed investigations of capabilit ies or practices i n a particular
context would allow individual procedures to be d eveloped in relation to the capabilities
and practices in that context, but the author interprets this as going beyond the principle
of guidance , which has been declare d as res earch objective.
Concerning the support proce sses, Analytics initiatives will be investigated r egarding the
sub processes listed by Rüegg-Stürm (2003) i ncluding human resources, e ducation,
infrastructure management, information management, communication, and risk
mitigation. However, like explained for manag ement processes, the investigation of
Analytics initiatives concerns guidance for their execution. Thus, the in vestigation is
intended to identify a va riety of superio r practices r egarding these sub processes that
present a ra nge of options to choose from, and derive a rational e for the tasks’ superiority,
since this is interpreted as guidance for this thesis. Investigating the pra ctices of these sub
processes in dif ferent co ntexts to develop individual procedures is, ag ain, not in foc us.
Hence , developing spe cific Analytics solut ions or recommending and categorizing
analytical methods and problems as well as assessing, developing or recommending
technologies is likewise not in focus of this thesis.
In accordance to the S t. Gallen M anagement Model (Rüegg-Stürm, 2 003), further
elements influence the pr ocesses and, thus, the execution of Analytics initiatives, which
have to be understood fo r their effect to provide g uidance . Hence, ordering moments of
strategy, structure and c ulture regarding An alytics are in focus for their effect on the
execution of An alytics ini tiatives including favoring and obstructing designs of them, and
the rationale of the effect. Their creation is not focus of thi s research, such that the creation
and im plementation of a data-driven strategy or data-driven culture is not in vestigated.
Ordering mom ents not spe cifically c oncerning Analytics beyond the restriction to LS CM
is also not in focus. Analytics initiatives are understood in this thesis to support atomic
business processes of an LSCM nature (e.g., d eliver, transport, store, order, alloc ate,

17
schedule, replenish) without concerning the o rganizational structure the business
processes are executed in (e.g., manufacturing, r etail, or logisti cs services organization).
These atomic business processes are assumed to induce similar problems t o be solved in
Analytics initiatives despite the or ganizational structure . Furthermore, interaction topics,
stakeholders and environmental spheres similarly comprise the potential to influence the
execution of Analytics initiatives. These aspects of the St. Gallen Management Model
will be collected for their influence and relevance. They will be investigated in regard to
their relevance, such that customers (users) and em ployees ( analysts) will be investigated
in more detail as compared to for example non-governmental organizations.
1.4 Structure
The structure of this the sis is created to consecutively advance in the def inition of the
problem – as describ ed by Simon (1973) – of h ow to enable org anizations to manage
SCA. Thus, six structural ele ments emerge: I ntroduction and theoretical background, the
four individual research inquiries, and guidance to manage S CA. Subsequentl y, thi s thesis
ends with a conc lusion, limitations and impli cation s for research and management.
1.4.1 Introduction and theoretical background
The introduction of this thesis explains the theoretical and practical research motivation.
Thus, aggregated aspects from respectively s cientific literature and organizat ional reports
are presented. These arg ue for the necessity of research and appraise the i mpact of it. In
addition, the introduction explains the ove rall research objective of th is thesi s and outlines
how the individual resea rch questions of the sc ientific a rticles compiling the thesis relate
to that overall researc h q uestion. Furthermore, the unit of analysis is described.
The theoretical b ackground introduces relevant terms and background r elated to LSCM
and Analytics. For that purpose, definitions and relevant rel ated terms ar e considered.
Further, to substantiate t he domain sp ecific res earch on Analytics limited to the domain
of LSCM, the use of Analytics in differe nt domains is briefly reviewed.
1.4.2 Article 1: Mapping Domain characteristics influencing Analytics initiatives
The first objective of d efining the problem spa ce is an exploratory task into differe nt
factors, which might affect the outcome of adopti ng and employing Analytics in LSC M.
Thereby, for the purpose of this researc h, using Ana lytics refers to the exec ution of
initiatives, which are either discoveries le ading up to a major decision with changes in
executing LS CM activities based on the results, or to a data product (or Analytics

18
product), which is supposed to be used continuously in a process. The outcome of
adopting a nd employing Ana lytics in LSCM is thus describing the outcome of an
Analytics initiative. Therefore, this outcome does not refer to the accuracy or optimality
of analytical methods or other evaluation crite ria of that sort . Instead, the considered
outcome of the Analytics initi ative is the achieved (continuous) decision support,
fulfillment of relevant requirements for providi ng support, and the fit of these
requirements to the decisions to be made. Further, releva nt conside rations of the outcome
of initiatives are, wh ether resu lts are complete, are deployable, are us ed by de cision
makers, and are providing their desired functionality acc ording to the require ments until
their re tirement. As illustrated in Figure 4 in a ccordance to the TSP, this spa ns a space of
multiple dimensions, allowing the description of an initiative ’s or organi zation’s state .
However, not all dimensions may be re levant or d ecisive at all instances.
To achieve the desired problem space, an explorat ory research method will be used, which
relies on the collection of empirical e vidence. To explore this space, f actors will be
inquired, that distingui sh Analytics i nitiatives. To create a rel ation to the d omain with in
the focus of thi s thesis and create an executable research design, these factors will be
derived f r om an inqui ry i nto characteristics that se t Analytics initiatives in LSC M apart
from initiatives in other domains.
1.4.3 Article 2: Archetypes of Supply Chain Analytics
The objective of describing the current state of Analytics in LSCM likewise demands
exploration. Thus, Analytics initiatives in LSCM are explored and aggregated to create
Probl e m Sp a c e (A rt i c le 1):
Wh i c h c h a r a cte r i s ti c s i nf l u e n c e
L SC M org a ni za t i o n s i n
e x e cut i ng A n a l y t i c s i ni ti at i v e s ?
S o lu ti o n S tate
I n i t i a l S tate
Op e r a tors
e.g . Ach i ev a b l e Co m p e titi v e Ad v an t a g e w i th Ana l y tics i n L S CM
e.g . S tra teg i es to co p e w i th B a rri e rs o f us i ng An a lyt ic s i n L S CM
e.g . E na b l e d E m p l oy ees to us e
A na l yt ic s in a L S CM con t ext i n a
g o a l - o rie nt ed m a nner

Figure 4 : Focus of article 1

19
an overview and more comprehensible insight into Analytics in LSCM. T he fit into the
overall re search objective regarding the TSP is displayed in Figure 5.
To execute this exploration, Analytics i nitiatives in LSCM are collected with the focus
on goa ls, as we ll as resources, and means to achieve the se goals. Thus, the data c ollected
will diverge from problem spac e cha racteristics of article 1, reasoned as f ollows. Firs t,
the research inquiry individually displays relevant and int eresting insi ghts. Second, the
research d esign is exec utable, since go als, resources and me ans are more a ccessible for
observation as compared to the various dimensio ns. Third, the intended resea rch results
are catered to the audience of this research – managers in LSCM – wh o can use them as
inspiration to create own i nitiatives. Thus, based on the collected data, the Analytics
initiatives will be cluster ed – aggrega ted based on patterns in the charac teristics – and
interpret ed for their clust er interna l commonalities to derive Archetypes. This presents
one perspective, but not a holistic view, of the current state of Analytics in LSCM.
1.4.4 Article 3: Exp laining the Competitive Advantage Generated fr om Anal ytics
with the Knowledge-based View
The thi rd objective addr esses the d escription of the solution state. While the pr evious
objectives are explorator y in nature, the thi rd objective is supposed to provide a confident
and evident guidance for manager s in which direction to develop th eir Analytics
activities. Thus, this guidance needs to provide reason – con firmation – that the solut ion
state is the best state to aspire towards. Explor ation is not intended to provide this
reasoning and thi s inqui ry nee ds to go beyond exploration . Thus, explanatory research

P r o b l e m
S p ac e
S o lu tion S ta te
Op e r a tors
e.g . Achie v ab l e Co m p e t i ti v e Ad v a nt a g e w i th An a ly tics i n L S CM
e.g . S tra teg i es to co p e w i th B a rri e rs of us i ng An a lyt ic s i n L S C M
e.g . E na b l e d E m p l oy e es to us e
A na l yt ics i n a L S CM co nt ex t i n a
g o a l-o rie nt ed m a nner
I n i t i a l S t a t e (A r ti cle 2 ): Wh a t ty p e s of S C A
i n i ti a t i v e s a r e L S CM o rg an i za t i o ns ex e cut i n g
to g a i n a d v a n t a g e s ?

Figure 5 : Focus of article 2

20
with confirmatory research methods are needed to address this research inquiry. The fit
of this objective into the TSP is illustrated in Figure 6.
To provide a description of the solution state, the actions taken in Analytics initiatives of
LSCM organizations successfully applying Analyt ics will be aligned and compared to a
theoretica l argumentation for competitive advantage. In more detail, the actions of LSCM
organization executing Analytics initiatives wi ll be aligned and compared to the
knowledge-ba sed view (KBV). Thereby, the considered actions certainly include
analytical actions. However, in accordance to the vision of this thesis, non -analytical
actions – specifically management actions – are c onsidered, which include management
of resources, teams, and t asks. Thus, the actions and the explained intentions behind these
actions within the empirically collected data will be compared to th e r easoning of the
KBV, which describes why competitive advantage emerges fr om knowledge, while
implying Analytics to incre ase knowledge. This allows for actions leading to valuable
and beneficial Ana lytics initiative s to be provided with reasoning for the ir effect.
1.4.5 Article 4: Overcoming Barrier s in Supp ly Cha in Analytics
Fo urth, operators to change state in the problem space according to the TSP are intended
to be identified. Transferring the abstract objective into a research design, this inquiry
intends to identify barriers and challenges in adopting and employing Analytics , since
these ba rriers are interpr eted as obstacles for st ate ch anges tow ards the s olution state .
Thus, overcoming b arriers would allow to change states as illust rated in Figure 7. To
overcome barriers, measures need to be identifi ed and evaluated for their im pact. Thus,

P r o b l e m
S p ac e
I n i t i a l Sta te
Operato r s
e.g . A chie v a b l e C o m p e titi v e Ad v an t a g e w i th An a ly tics i n L S CM
e.g . S tra t eg i e s to c op e w i th B a rr ie r s of us i ng An a lyt ic s i n L S C M
e.g . E na b l e d E m p l oy e es to us e
A na l yt ics i n a L S CM con t ex t i n a
g o a l-o rie nt ed m a nner
S o lu ti o n S tate (A r ti cle 3 ): H ow s h o u l d L S C M
o rg an i z a t i o n s a p p roach an d ex ecut e S C A
i n i ti a t i ve s t o ga i n c o m p et i ti ve adva n ta g e ?

Figure 6 : Focus of article 3

21
this research inquiry is exploratory fo r the most relevant barriers and measures and
intends to derive core themes in measures that help to overcome the barrier s and
successfully execute A nalyt ics initiatives in LSCM orga nizations.
To achieve this objectiv e, this article’s research design takes a mixed methods approach,
which allows for explor ation and cond ensation of data to d erive core c ategories. Th e
research design intends to identify the oper ators, assess their impact and g ather further
insight on the context in which the operators can be applied. This research inquiry will
focus on barriers and challenges in Supply Chain Analytics and will limit the collection
of empirical data to that domain.
1.4.6 Managing Supply Ch ai n Analytics
Finally, the findings of the four individual arti cles are used to create guidance for
managers in LSC M to manage their Analytics initiatives in two approaches. First, the
collected data and insigh ts are accumulated int o a discussion on the direct and indirect
value for customers created from Analytics applied in LS CM. This discussion prese nts a
variety of exemplified use cases and how they provide value. Second, a well -established
process model for Analytics is supplemented with the data and insights collected for this
thesis to provide guida nce for managing distinct SCA initiatives. In further d etail,
prece ding the suppl ement of an Analytics pro cess model with the c olle cted data and
insights for the inquiries, 14 proc ess models of Ana lytics are re viewed to converge terms
in Analytics process mod els and put them into cont ext of a 15 th process mode l – the Cross-
industry standard process for data mining (CRISP- DM). The CRISP-DM process model

P r o b l e m
S p ac e
S o lu ti on S ta t e
I n i t ial S ta te
e.g . Achiev a b l e Co m p etiti v e A d v a nt a ge w i t h An a lyt ics i n L S CM
e.g . S tra t eg i e s to c op e w i th B a rr i er s of us i ng Ana l yt ics i n L S CM
e.g . E na b l e d E m p l oy e es to us e
A na l yt ics i n a L S CM co nt ex t i n a
g o a l-o rie nt ed m a nner
Operato r s (A rt i c le 4): Wh i c h a c ti o n s c an
L SC M o r g a n i za t i o n s ta ke to i m pro v e th e
o u tco m e o f S C A i ni ti at i ve s ?

Figure 7 : Focus of article 4

22
is the most widely used process model in Analytics. Subsequent to the align ment of terms
and pro cess steps, it will be e nriched by the insights fr om the fou r research inquiries. This
process model provides a tool for manag ers to plan and execute An alytic s initi atives
beneficially with a nticipation of and resilience to eventual challenges.
The provided guidance is int ended for man agers in LSCM aspiring to execute Analytics
initiatives and supposed to enable them to apply Analytics in LSCM with sustainable
competitive advantage and continuous im prove ment of proc esses and customer
satisfaction. Thus, the provided guidance enables them to manage SCA. The fit to the
overall objec tive is illustrated in Figure 8 .

P r o b l e m
S p ac e
S o lu tion S ta te
I n i t i a l Sta te
Op e r a tors
A pp roa c h to m a n a g e S C A i n i t iativ e s : g ui d an ce
to a dva n ce o rg a n i z a ti o n s b y ra i s i n g a tt e n ti o n t o
re l ev an t fa c tors an d p ur po s e - ori e n t e d u se o f
o p er a t o r s .
e.g . Achie v ab l e Co m p e t i ti v e Ad v a nt a g e w i th An a ly tics i n L S CM
e.g . S tra teg i es to co p e w i th B a rri e rs of us i ng An a lyt ic s i n L S C M
e.g . E na b l e d E m p l oy e es to us e
A na l yt ics i n a L S CM co nt ex t i n a
g o a l - o ri ent ed m a nner

Figure 8 : Intended goal of aggre gating and enriching the article s

2 Theoretic Bac kground
This section introduces t he leading terms of this thesis. First, Logist ics a nd Supply Cha in
Management are d efined and the treatment of both terms as one for this thesis is argued.
Second, Analytics is explained and the relationshi p to the terms Big Data, Data Science
and Artificial I ntelligence is described. Third, Su pply Chain Analytics, the intersection
of Logistics and Supply Chain Management with Logistics is portrayed together with
other subfields of Analytics to illustrate the unique cha racteristics of it.
2.1 Logistics and Supply Chain Management
The core of this thesis is the part of Analytics that is refe rred to as Domain (Davenport et
al., 2010). The Domain is the field of application that is in focus of Analytics to create
improvements or insi ght. The design of this thesis intends to enable these im provements
and insight in the domai n of Logist ics and Supply Chain Management with the purpose
to contribute to the scientific li terature and pr actical execution of Analytics in this
domain.
2.1.1 Logistics
The term Logistics has b een defined in various w ays. Pfohl (2010) categorized definitions
in lifecycle oriented, service oriented and flow oriented. The latter is notably more
specific about subject and tasks and will be considered in more detail. The subj ect of
Logistics is material flo ws from the initi al supp lier to the final customer and related
information flows, which have to be planned, controlled, executed an d monitored
(Straube, 2004). Thes e fl ows are n ecessary since t he materials must transform in time and
space to g et fro m time and location of suppl iers to the ti me and lo cation of consum ption
by the customer (Pfohl, 2010). The focus of the material flow in re lation to a focal
organization has led to c reate several subfields of Logistics including procurement
Logistics, distribution Logistics, site Logistics or disposal Logistics (Gu dehus, 2012).
These diff er in the spe cific activities, but the high-level tasks of planning , controlling,
execution and monitoring are releva nt for all of them.
As indica ted in the definitions above , to accomplish logistical tasks, the lines of busine ss
units and organizations have to be crossed. Baumgarten (1980 ) explained t he thought of
Logistics being a discipline mastering material flow s insi de and outside of the own
organization in physical, infor mational and organi zational regards in the be ginning of the
1980s. While he does so in the prologue of the book and while todays ‘ holistic ’ view of

24
Logistics concerning the flows across organizations is already envisioned in the substance
of his book, which represents the state-of-the-art of Logistics at the t ime , the rest of the
book addresses matters of internal material flow . In pr actical application, cross
organizationa l logistics networks we re widely established in the 1990s (Straube, 2004) .
The holistic way of thi nking about Logistics, a core concept in the Logistics literature
(Baumgarten and W alter, 2001; Kopfe r and Bierwirth, 2003; Pfohl, 2010; Straube, 2004 )
that has been introduced as ea rly as 1974 (Pfohl, 1974), manifests the concept of
interdisciplinary and int erorga nizational management of material flows by promoting to
understand logistics networks as systems ori enting their a ctions on customers’ demands
and needs, no matte r whether the system entails severa l organizatio ns or is distributed
globally. This way of thi nking demands instruments of exchanging information (Straube,
2004).
2.1.2 Supply Chain Management
The second term of relevance, S upply Chain Management, has similarly to Logistics no
uniformly accepted definiti on. Reoccurr ing e lements in leading Supply Chain
Management literature a re integrated and collaborating organizations, establishe d
relationships betwe en these orga nizations, and approaches leveraging the re lationships to
organize business activities of these organizations and flows of assets, products,
information and funds such that products are produced and d elivered to the customer
efficie ntly and with minimal costs (Bowersox et al., 2007; Chopra and Meindl, 2016;
Christopher, 2011; Hugos, 2011; S imchi-Levi et al., 2003). The collaborating
organizations are suppos ed to be all orga nizations upst ream from the customer, which are
necessary fo r designing, making, distributing and using products (and services) such as
suppliers, manufacturers, warehouses, and stor es (Hugos, 2011; Simchi-Levi et al., 2003) .
Their collaboration is supposed to increase the su rplus of the supply chain such that all
supply chain members benefit (Chopra and Mei ndl, 2016) and the cust omer value is
superior (Christopher, 2011). This pe rspective is sometimes sim ilarly to Logistics
literature l abeled as ‘ holistic ’ (Christopher, 2011; Hugos, 2011; Sim chi-Levi et al., 2003 ).
The term S upply C hain Manage ment was introduced by consultants of Booze Allen
Hamilton in the beginning of the 1980s as a result of several projects in logistics. The
concept is supposed to di ffer f rom classical material flow and manuf acturing concepts i n
a varie ty of aspects. According to Oliver and Webbe r (1982), the supply chain is
recognized as single entity including the several business functions inv olved in the

25
material flow in an organization. It represents strategic decision -making, since the
functionality of the supply chain becomes a shared obje ctive. Further, inventory is
considered as b alancing mechanic and, finally, the supply chain should ultimately be
considered as an integrated syst em and not as conjunctions via int erface s. However, the
article of Oliver and W ebber (1982) does not introduce the concept as replacement,
opposition or subordinate to Logistics. It rather describes it as completing t he concept of
logistics. By considering supply chain management as novel approach to Logist ics, their
conception can b e int erpre ted a s uplifting the function of Logistics , which was
underrecognize d for strategic purpose to this point , or incorporating a Logistics focus into
strategic decision-making.
2.1.3 Synopsis
While the previous introduc tions present two parallel existing terms with resembling
objectives, focus and requirements of interorganiz ational thinking , which seemingly were
not intended to exist in conflict or competition, a dispute about the terms relationship to
each other has developed. The dispute goes so far that the relationship of th ese ter ms has
been the subj ect of researc h. L arson and Halld orsson (2004) d evelop four different
perspec tives about how the two terms are related. With their research based on a survey
of edu cators, they show the existence of different perspectives of the relationship of the
two terms and some aspects be ing more likely to be associated with one or the other term.
However, their research does not show any value in the existence of t wo terms. The
aspects they use to distinguish the terms, which ar e practices, methods and tasks, are all
eventually r elevant for orga nizations with non -simple material flow s. Rather, an
implication of their researc h is that the two terms for the same thi ng but with differe ntly
understood relationship i ncrea ses the complexity between organizations to align,
communicate and achieve optimal performa nce of the Logistics system or Supply Chain .
Consequently, having tw o terms might be a barrie r to achieve either term’s objective of
the Logistics system or Supply Chain ac ting a s a system for increased efficiency, reduced
cost and, most importantly, superior customer value.
Concluding, while there remains to be a controversy about the relationship between
Logistics on the one hand and Supply Chain Ma nagement on the other, the discourse
about this relationship does not seem to provide any value. Both claim to focus on
executing the operational, tactical and strategical a ctions to ensure provision of goods and
services with the goals of approp riate effectiveness, e fficiency, quality and sustainability

26
under holistic thinking concerning inte rdisciplinary and interor ganizational perspec tives.
Value is, as argued by th e author of this thesis, provided in science for org anizations by
developing insights and means to improve the executed actions towards their goals –
improve effectiveness, ef ficiency, quality a nd sustainability and guide to holisti c thi nkin g
– a nd not by a rguing a bout how to label a field. T hus, for the remainder of this thesis, the
two terms are treated as equal and fairly labeled as Logistics and S upply Chain
Management (LSCM). Regarding this thesis’ focus on process, as the workin g defini tion
of LSCM , it is comprised of the processes that e nable the flow o f materials and related
assets, information and funds from the initial supplier to the final customer by actions of
planning, controlling, executing and moni toring. Holistic thinking, eff iciency and cost
minimization are understood as quality criteria fo r LSCM, but not necessarily as part of
the definition.
2.2 Analytics and related terms
As one of the interviewees in the research designs present ed below mentioned “terms
describing the field [of Analytics] change f aster th an the actual methods” . In this thesis,
to consider the phenomenon of data analysis for decision-making as will be explained
below, the term Analytic s has been chosen as central term. The term is more explicitly
associated with management issues, and the author of this thesis perce ives Analytics as
start for an essential wave of d ata analysis for d ecision-making in organizations leading
it to be widespr ead toda y and during th e condu ct of th is thesis’s r esearch. Further, the
term Analytics is perceived by the author as most s table, while press, public a ttention and
conversa tions on other terms left a n impre ssion of hype-inflated a nd unsubst antiated
expectations. In this section, Analytics is defined and put in relation to the terms Big Data,
Data Science and Artificial I ntelligence by highl ighti ng diffe rences and similarities. In
addition, following common phrasing in literature using the term s Analytics initiative or
Data initiative for describing goal-oriented Analytics activities, this thesis will use the
term Analytics initiative as well (Davenport and Harris, 2007; Holsapple et al., 2014;
Marcha nd and Peppard, 2013; Ransbotham e t al., 2016). Espec ially reflecting La Valle e t
al. (2010), as working definition of Analytics initiatives , it comprises all actions over
the full life-cycle of an Analytic s solution that contribute to its sustainable impact and
continuity, including the de termination of the objectives with Analytics, the execution of
analytical activities, the development of Analytics s olutions, the deploymen t of Analytics
solutions, and their active and re gular maintenance and performance preservation.

27
2.2.1 Analytics
There is a v ariety of def initions and explanations for Analytics. Davenport and Harris
(2007), who initiated the broader recognition of Ana lytics with their famous book
“Competing on Analytic s ” , describe An alytics as “extens ive use of data, statisti cal and
quantitative analysis, explanatory and predi ctive models, and fact-based management to
drive decisions and actions”. This description is focused on activities, which include
management in a ddition to the use of analytical methods and makes the purpose of
decision-making and subsequent actions immanent – the idea of An alytics n ot be ing done
for the purpose o f Analyt ics is made part of the d escription. This idea was made central
to a meta definition Holsapple et al. (2014) h ave derived from considering several
definitions, which is recognized as the wo rking definit ion of Analytics for thi s thesis .
They describe Analytics as being concerned with “ evidence-based problem recognition
and solving that happen within the context of business situations”. Thereby, evidence -
based is supposed to emphasize that decision-ma king from Analytics is n ot (just) based
on data or f acts, but also on justified estimates, well -reasone d approximatio ns, or credible
explanations.
A common way to subc ategorize An alytics is the distinction of Descripti ve Analytics,
Predictive Analytics and Prescriptive Analytics ( Bedeley et al., 2018; Ca o and Duan,
2017; Davenport, 2013; Holsapple et al., 2014; Ransbotham et a l., 2015) . Descriptive
Analytics is described as the backward looking form of Analytics, identifying trends,
describing context and p roviding aggregations, which are evident from the data and can
be reported from it (Bedeley et al., 2018; Cao and Duan, 2017; Davenport, 2013). I n some
cases, D escriptive Analytics is also explained to look for cause s and “ why ” things
happened (Ransbotham e t al., 2015; Spiess et al., 2014). Predictive Analytics is focused
on estimating and fore casting future events or be havior based on models that consider
what happened in the past (Bedeley et al., 2018; Davenport, 2013; Ransbotham et al.,
2015). Thus, the basic assumption is that patterns in the data will sustain. Finally,
Prescriptive Analytics is providing guidance by assessing the best – ‘ optimal ’ – actions
regarding diff erent scenarios of future events and behavior (Bedeley et al., 2018;
Ransbotham et al., 2015). While this could be easily compared to the field of Operations
Researc h, Prescriptive A nalytics is opposingly supposed to be integrated and employed
in business processes requiring t he c reation o f solutions in close collaboration with
experts from other areas, the adoption of a wide scope, the integration o f problems, and

28
the flexibility of solution, what makes Op erations Research a fi eld Analytics leverages
from as on e o f several components (Liberatore an d Luo, 2010; Phil lipps and Davenport,
2013).
A focus t hat is repeatedly addressed in the li terature concerning the term Analytics is
manageria l issues. Providing means to managers to set up the organization s to gain the
aspired value from Analyti cs and en abling organization s to deploy An alytics is the co re
to pic of Da venport and H arris (2007). The ex ecution of initiatives, opera tionalization and
influencing fac tors on value generation ha ve bee n addressed including factors which ca n
omit the realization of the value after an alytical methods have been conducted such as
communication and consumability of the results ( La Valle et al., 2010; Ransbotham et al.,
2015; Seddon et al., 2017; Wedel and K annan, 2016; Wixom et al., 2013) . Thereby, the
focus diverges from the analytica l meth ods to the broader int egration of the methods’
results into the value creation process. Further m anagerial issues that h ave been add ressed
are organizational culture and skil l gaps, including the skill gaps of managers that need
to be filled, the fit of Analytics into the structure of an organization and the applic ation
area s of An alytics in organizations (Acito and Khatri, 2014; Beer, 2018; Marchand and
Peppard, 2013).
2.2.2 Big Data
The second term of interest, Big Data, c an be traced back to an a rticle describing
necessary dev elopments in data management principles to keep pace with requirements
to harness the infor mation from organizational activities, especially in e-c ommerce
(Laney, 2001). To suppo rt businesses, IT o rganizations were demanded to implement data
management architectures which ca n handle an increasing data volume, the velocity d ata
is created and the v ariety of formats that must be collected. At that point, the three ‘V’s’
focused on ba ckend technologies to e nable analytical approaches but not on the analytical
part. As a simple way t o describe Big Data, th e V’s were carried on a nd e xpanded,
especially in scientific literature, to lists somewhe re between three and t en V’s (Brinch et
al., 2018; Sivarajah et al., 2017; Tsai et al., 2015; Wang and Alexander, 2015) . This more
rece nt characterization of Big Data expands the focus beyond data m anagement and
architec tures to the exploitation of data by analytical mea ns. However, scholars
repea tedly comment on Big Data as concept and field of res earch t o be not well
established and the im pact of the V’s to be impr ecise (Brinch et al., 2018; Gandomi and
Haider, 2015; Sivarajah et al., 2017). It w as fu rther emphasized that the exploitation of

29
value from Big Dat a re quires Analytics (Carill o, 2017; Gandomi and Haider, 2015;
Troester, 2012 ). In th is regard, th e terms Big D ata and Big Data Analytics are used
synonymous to Analytics (Akter et al., 2016; Debortoli et al., 2014; Hopkins and
Hawking, 2018), as it is understood in this thesis.
The characteristics of d ata emphasized with th e Big Data term change the analytic al
approac hes to store and ana lyzing data, since processing is more challenging, has to
happen in short time for larger and more complex streams of data and has to handle
differe nt and incomplete types of data (Tsai et al., 2015) . Commonly a ssocia ted with Big
Data are forms of dist ributed processing, which dist ributes the workload of storing and
analyzing data across different machines to be handled in paralle l (Philip Chen and
Zhang, 2014). Different forms are specialized for different purpose such as batch
processing, like the quite famous Hadoop, or for stream processing, like S plunk or
Apache Kafka. Analytics Solut ions that re quire such distributed proce ssing usually
display a high degree of technical complexity as presented by Markl et al. (2013).
2.2.3 Data Science
Data Science is the thi rd term of interest. While the term exists for a longer ti me, larger
popularity can be traced back to an article abou t Data Scientists, describing it as the
“ sexiest job of the 21st century ” (Davenport and Patil, 2012). The t erm describes an
advanced type of analysts, who can handle la rge amounts of data, can code, employ
advanced quantitative techniques and can comm unicate the results in understandable
manner, often with a background in a scientifi c field that uses complex qua ntitative
methods (e.g., a PhD in physi cs, social science o r ecology). However, the article backfired
and created an ambiguous term that could mean almost anything and is applied to a variety
of jobs, frustrating the authors and leaving the Data Science field sim ilarly undefined
(Davenport, 2014a). Sev eral scholars have provi ded a short summary or definition for
Data S cience. Dh ar (2013) describes it as “the study of the gene raliza ble extraction of
knowledge from data” and in the discussion that follows, describes a fo cus on more
complex analytical and quantitative methods as well as on d ecision-making, primarily
automated. O’Neil and S chutt (2013) present an extensive discussion about the ambiguity
of the t erm and ev entually prese nt Data Science as the activities executed by data
scientists, which a re the e xtraction of mea ning and interpretation of da ta with a va riety of
analytical methods and integrating a variety of skills. The Essential Knowledge series of
the MIT Pre ss, d efines it as “ encompassing a set of principles, problem definitions,

30
algorithms, and processes for extracting nonobvious and useful patterns from large data
sets ” in the book on Data Science ( Kelleher a nd Tierney, 2018) . In the considered
literature, the emphasis is usually on ‘ e xtraction ’ of insight from data with f e w a ttentions
to subsequently c reating value from the insights . Concerning the relation of Da ta Science
and Analytics, the terms are used synonymous for insight extraction from data with the
differe nce that Data Sc ience comprises more complex methods but less focus on
practica lity in business (Larson a nd Chang, 2016; Marc hand and Peppard, 2013; Viaene,
2013).
The Data Scientists a re the foc al point of the discussion about Data Science . In the
discussion on the D ata Scientists, it is agre ed that they execute the analytical tasks. Apart
from that, their responsibility and capability is argued between having a large variety of
skills and executing the larger pa rt of ini tiative s (Debortoli et al., 2014 ; Dhar, 2013;
Grossman and Siegel, 20 14) and them taking a limited role in ini tiatives and working with
a tea m that contributes co mplemental skills and capabilities, a rgued with strong re jection
of an omnipotent Data Scientist (Carillo, 2017; Viaene, 2013; Vidgen et al., 2017).
Whatever their role is, Data Scientists are explained to need a sense for Business
(Davenport, 2013; Marchand a nd Peppard, 2013 ). They are fur ther described to use more
advanced methods, such as machine learning and skills for using B ig Data, which a re
traditionally not taught in statistics courses (Dhar, 2013; Larson and C hang, 2016;
McAfee a nd Brynjolfsson, 2012). But “ just ” hiring Data Scientists is not the guar antee to
crea te value from data (Carillo, 2017; Marchand and P eppard, 2013).
2.2.4 Artificial Intelligence
Fourth a nd final, AI is another ter m currently used in context of creating value fr om data.
The scientific field of AI has a long history with a primary focus on the challenge of
crea ting intelligen ce for machines, with the creation of solutions for business as s econd
order. Russell and Norvi g (2016), and Boden (20 16) display a variety of definitions and
approac hes to define AI and discuss the ambiguity about expectations in the field.
Summarizing their consi derations, an AI is a virtual machine that is dependent on a
physical machine, where by AI is understood as t heir combination either a s computer, a
program on a computer or an Agent, which is a c ertain program that c an operate
autonomously, perceive i ts environment, persists over time, adapts to changes and creates
and pursues goals. The capabilities that display intelligence are further und er discussion.
While capabilities of visions, rea soning, language, learning and further are usually used

31
to describe intelligence, the necessity of displaying human -like understanding and
grasping of the meaning of these capabilities for being an AI is seen differently. B oden
(2016) explains the visionary goal of the f ield to be the c reation of an “Artificial General
Intellige nce” with this human -like capacity. Sinc e there is sti ll no full un derstanding of
what human intelligence is and how it works, this is estimated to be unachievable by some
scholars in the field of AI .
In a business context, ac hieving intelligence is rarely in scope. R ather the capabilities –
typically labeled as “ tools ” – and the techniques to achieve them a re in foc us in a business
context (Ak erkar, 2019) . The techniques are distinguished in knowledge-based syst ems
and machine le arning. The knowledge -based system/expert systems a re based on
programmed rules by exp erts the AI h as to follow, which brought a fi rst wav e of industrial
applications of AI in the end of the 1970s and beg inning of the 80s but f ailed to deliver
on their ambitious goals (Alpaydin, 2016; Russell and Norvig, 2016) . Ma chine learning
extracts these rules from data – learns from data/trains on data – by using mathematical
techniques such as Bayesian probabilities and neura l networks, dominating the current
attention and progress in AI (Akerkar, 2019; Alpaydin, 2016; Boden, 2016; Russell and
Norvig, 2016). D eep learning is a sub form of machine lea rning originating from more
complex neura l networks, technically named deep neural network s for their model
structure. Especially the techniques of mac hine le arning, now usable due to t echnologic al
advances, are argued to provide advanced opportunities as analytical methods to exploit
value from data (Beer, 2018; Davenport, 2013; Vidgen et al., 2017). However, the use of
AI in organization has a wider scope than analytical issues. From a g eneric perspective,
AI is dist inguished in three forms (Rao, 2016). Fi rst, Assisted AI , which simplifies and
accelera tes tasks while humans make the decisions. Second, Augmente d AI provides
extensive input for the humans’ decisions and resultingly shar es de cision rights. Third, i n
the Autonomous AI case, the machine act s and d ecides autonomously. Davenport and
Ronanki (2018) took a use case perspective of A I employed by organizations and
distinguish in one use case group of Analytics – r eferring to it as “Analytics on steroids”
– and other groups of automating repe titive processes and communication tasks. Thus,
Analytics is one area which is empowere d by methods from AI (Akerkar, 20 19; Ga ndomi
and Haider , 2015; P hilip Chen and Zha ng, 2014).

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2.2.5 Synopsis
In summary, the literatur e behind these t erms above simil arly describes th e concept of
exploiting data for be tter decision-making. However, the foc al points of this exploitation
differ. Big Data t ends to f ocus on technological aspects along with opportuniti es to exploit
data with character istics t hat require thes e technologies. The focus of Data Science is on
execution of the ever mor e powerful methods and the resulting opportuniti es of exploiting
insights from data. The machine lea rning techniq ues and methods provided by AI focus
on learning from data , which provides advanced opportunities for analytical issues but is
only one of several usages of AI , what would make the use of the term AI misleading for
this thesis. Finally, man age ment aspec ts, execution of initiatives, the organiza tional
objectives of analytical approac hes and other aspects of im plementing the methods in
organizations are in focus of Analytics. For this research, the practical and scientific
insights associated with all terms are relevant. However, fo r the reason of the management
focus associated with the term Analytics and the further r easons explained in the
introduction to this section, Analytics is used as central and comprehensive term in this
thesis.
2.3 Analytics in different Domains
In this section a deeper l ook will be taken into curr ent research in a variety of domain -
specific Analytics to illustrate the differences of Analytics in different domains. For this
purpose, different data- rich domains have been chosen, which encounter different
challenges (LSC M, Marketing and healthcare), one that seems trailing behind (public
sector) and, at last, one fie ld that is seemingly diffe rent to traditional orga nizational
processes, to show similarities in using Analytics (Sports).
2.3.1 Supply Chain Analytics
LSCM is suitable to use Ana lytics, a s it is a data-driven and data ric h environment and is
seen as an early adopter o f quantitative Methods fr om Operations Res earch (Chae, Olson,
et al., 2014; Souza, 2014) . The use is driven by the overall objective o f ma tching supply
with demand, since demand may not be post poned and the supply is pe rishable, as usual
for service industries (Souza, 2014). I n addition, supply and demand become increasingly
uncertain due to fast changing customer expe ctations, process variations, inconsist en t
suppliers, and ever-increasing networks (Chae, Olson, et al., 2014; K ache and Seuring,
2017; Wang et al., 2016) . Th e objective of mat ching is accompanied with a multitude of
objectives pursued with Analytics in the literature, which are basically the objectives

33
LSCM pursues anyway. Popular are the objectives of reducing costs, increasing
efficie ncy and effectiveness, and incr easing customer orientation, while further objectives
are improving quality, r educing time/increasing agility and flexibility (Chavez et al.,
2017; Kache and Seuring, 2017; Schoenh err and Speier-Pero, 2015; Souza, 2014; Wang
et a l., 2016). Subsequently, the benefits organizations aspire to achieve with Analytics to
meet these objectives are of better integ ration with suppl y chain partners and improved
planning input (Ka che an d Seuring, 2017; Wang et al., 2016) . However, the most notable
emphasized b enefit aspir ed, which might not require the most complex me thods but can
be technologically and organizationally challenging, is visibility (or transpare ncy).
Visibility is stressed to contribute to the objectives above by improved capabilities to
handle variability, uncertainty, environmental influences, mark et conditions, supplier and
customer needs, and sud den problems, by assessment of progress to achieving goals and
need for adjustments, by determining compliance of suppliers to quality and regulations,
by gaining insights on suppl y cha in wide inventories and activities, by dete cting the nee d
for corrections of poor performa nce to ultimately make better and faster decisions,
especially in rea l ti me. (Chave z et al., 2017; Kache and Seuring, 2017; Sande rs, 2016;
Wang et al., 2016) .
Due to the scope of LSCM, the Analytics a pplications a re countless. Along different ti me
frames and diff erent organizational levels, organizations cre ated applications on the
demand side (e.g., as planning input) for demand forecasting, analyzing purchasing
patterns, a nd product assortment optimization. On the supply side applications have been
crea ted for supplier segmentation, supplier selection, supplier evaluation, risk detection,
risk manage ment, definition of auc tion mechanisms, design of negotiation strategies, a nd
analysis of spend pro files. Further, dist ribution and manufa cturing applic ations include
location optimization/network design, scheduling, segmentation of routes, vehicle
routing, forecasting of est imated- time -of-arrival ( ETA), predictive maintenance, capacity
planning, reduction of shrink, reduction of ma terial waste, labor scheduling, labor
efficie ncy optimi zation, opti mization of fuel efficiency, optimization of driver behavior,
material requirements planning, master productio n planning, support product design and
identification of bottlenecks (Cha e, Olson, et al., 2014; Sanders, 2016; Souza, 2014;
Waller and Fawcett, 2013; Wang et al., 2016). Th ese applications are fueled by internal
data fr om ERP and other Systems and the transactions, static da ta ca ptured and Analytics
results, which go into ot her applications, as well as increasingly the data from several

34
dispersed entiti es of the suppl y chain li ke suppliers, carr iers and point s of sale including
inventories, cost, process times, demand and their forecasts (Souza, 2014; Wang et al.,
2016). Larger data volu mes and more pr ecise data becom e available due to sensor
technology li ke RFID, G PS or IoT capturing temperature, light intensi ty and vibration,
which are used in the sup ply chain (Kache and Seuring, 2017; Sanders, 2016).
However, thi s multiplicity of applications, data a nd data sources c omes at a price.
Organiza tions suffer from fragmented efforts without systemization and coordination.
This makes the aspiration of organization and supply chain wide objectives extremely
challenging and defies th e benefits achieved (Sanders, 2016). A study on cloud logistics
has emphasized that logistical objects and resources lack stand ardized categorization and
ontology including their informational representation (Glöckner et al., 2017), which
magnifies the issue. Further challenges highlight ed for LSCM are the cost of Analytics,
the unwillingness of partners to shar e information, what hinders suppl y chain wide
approac hes, and the complex ity especially of o ptimization problems (Sanders, 2016;
Schoenherr and Speier-Pero, 2015; Wang et al., 2016). Further, rather common barrier s
are reported as well including lack of experienced personne l, data security issues, lack of
integration of systems, change management issues, lack of data for th e applic ations
intended and abundance of data without applications determined (Kache and S euring,
2017; Sanders, 2016; Schoenherr and Speier-Pero, 2015).
2.3.2 Marketing Analytics
Marketing is a hist orically data-ri ch domain, whi ch started professional d ata colle ction
with organizations like Nielsen in the 1920s and advanced data analysis as early as th e
1960s (Wedel and Kannan, 2016). The use of Analytics in Marketing aims at harnessing
the customer for better decision-making (Germann et al., 2013), with two overall
objectives: personalization and ef fective resource allocation. Detailed d ata about the
customer and analysis of the data enables p ersonalized marketing which leads t o the
growth of more profitable customers with better customer relationships (Leeflang et al.,
2014; Rust and Huang, 2014; Wedel and Kannan, 2016) . This contrasts the mass market
centered and transaction driven marketing, and eventually displays a para digm change to
the customer as profit c enter with opportunities for additional sales (Leeflang et al., 2014;
Rust and Huang, 2014 ). Personalization aspires even to address contextual needs, e.g.,
based on the customers position (Rust and Huang, 2014; Wede l and Kanna n, 2016). Even
though personalization has become less resource demanding, complete pe rsonalization

35
might not display the best use of resources (Wedel and Kannan, 2016), leading to the
second objective of r esource alloca tion. More detailed knowledge about the customer, or
his customer journey, enables to dire ct resources to more profitable c ustomers and limits
resources to others (Leeflang et al., 2014; Rust and Hua ng, 2014; Zhao, 2013) . The
benefits for Marketing from Analytics are improve d decisions due to more decision
consistency, exploration of broader decisions, abi lities to assess the relative impact of
decision va riables and more granular decision-making by exploiting customer s’
heterogeneity and nee ds, which they provide voluntarily by communication or
unknowingly by online behavior (Germann et al., 2013; Rust and Huang, 2014; Wedel
and Kannan, 2016). Thereby, the impact of Analytics on Marke ting positive ly depends
on competition strength and frequency of customer preference changes (Germann et al.,
2013; Leeflang et al., 2014).
Analytics applications in Marketing are either decision supportive or decision automating
(Germa nn et al., 2013). T his includes segmentation for personalized Marketing via Email,
estimation for click-through rates for automated bidding on online advertisement space,
survival analysis for churn prediction, lead scoring and severa l predictive te chniques for
direct-marketing opportuniti es like next best offer, cross selling /up selling campaigns or
win-back campaigns (Leeflang et al., 2014; Leventhal, 2015; Zhao, 2013) . However, the
estimation of the custom er lifetime v alue with A nalytics stands out (Rust and Huang,
2014; Wedel and Kannan, 2016; Zhao, 2013) . The data en abling these appli cations are
now coming from all sorts of data producing sour ces: smartphones, sma rt TVs, internet
clickstreams and click-through behavior, blogs, twee ts, or other social media-based
customer content and interactions (e.g., videos, c omments, likes), or the purcha se history
with marke ting eventually knowing more about c ustomers than their friends (Leeflang et
al., 2014; R ust and Hu ang, 2014; Wedel and Kannan, 2016; Zhao, 2013) . The value in
these data comes f rom the possi bility to observe customers of competitors inst ead of
solely observing the own customers (Wedel and Kannan, 2016).
Challenges are priv acy issues from all the data flood (Wede l and Kanna n, 2016) and
usually very low accuracy for future predictions, despite all the data (Leventhal, 2015) .
Further, with the growth of data and the various Marketing channels, the question for
accountability of the different Marketing measures increase s (L eeflang et al., 2014). This
leads to subsequent challenges, since mass Marketing will still be necessary as Marketin g
channel and an organizat ion must track and acc ount for v arious ch annels and touching

36
points of customers with advertiseme nts a nd spillovers across c hannels (Leventhal, 2015;
Wedel and K annan, 2016) . The emphasis on accountability and data heavy decision -
making in Marketing is further argued to slow down de cisions as well a s hindering
crea tivity and innovation s and thus reduce Market ing performance ov erall ( the so-called
“data - innovation dilemma”) (Germann et al., 2013; Leeflang et al., 2014).
2.3.3 Healthcare Analytic s
Healthcare is another da ta rich environment, but th ese data are usually stored as hardc opy
and get increasingly digi taliz ed opening the opportunities for Analytics (Raghupathi and
Raghupathi, 2014). Thereby, a variety of actors is interested in th e Analytics enabled
potential of these data including physicians, patients, policy makers, insur ances and the
genera l public (Shneiderman et al., 2013; Sriniva san and Arunasalam, 201 3). Due to the
differe nt ac tors, a multitude of objectives has e volved. An overall objec tive is to improve
health of pa tients a nd quality of healthca re (K ohn et a l., 2014; S hneiderman et al., 2013) .
Considering the different actors, sub-obje ctives are to enable p articipation of patients in
decision-making and care fo r their health, personalizing treatment accounting for
individual pa tient characteristics, creating a ccess to healthca re, ke eping the hea lth syst em
economically sustainable (e.g., pr eventing fraud ), directing the knowled ge growth of
healthcare to guide d ecision-making, im proving all ocation of resources inclu ding medical
specialists, and optimization of the care p rocess (Belle et al., 2015; Kohn et al., 2014;
Raghupathi and Raghupathi, 2014; Shneiderman et al., 2013; Srinivasan and A runasalam,
2013). All these objectives are accompanied by the aspired reduction of costs (Raghupathi
and Raghupathi, 2014; Shneiderman e t al., 2013).
Resulting, a varie ty of a pplications using healthca re dat a e xists including assessment of
national health status or trends (e.g., obesity or spreads of dise ases), improved aler t
systems for critical patient conditions, pre diction of critical conditions, pattern
recognition in drug interactions, pre dictions of treatment or healthcare habit to adjust the
respec tive (Kohn et a l., 2014; Shneiderman et al., 2013) , and genomics, which is
supposed to deliver th e necessary insight for personalized care and treatment
development for comple x diseases (Belle e t al., 2 015). The data comes from a magnitude
of point-of-care data sour ces including clinical sen sors, images, electronics health records
or written notes (Kohn et al., 2014; S hneiderman et al., 2013). However, increasingly
external sources crea te relevant data for He althcare Analytics including personal sensors,

37
social media, pharmacies, or laboratories (Raghupathi and R aghupathi, 2014;
Shneiderman et al., 2013).
Yet, the data is a major challenge for h ealthcare A nalytics. First, th ere is a magnitude of
data types such as different imaging techniques, physiologica l signals or textu al records.
Second, coming from different d evices, data exist in different data formats , images have
differe nt resolutions or dimensions, and signals must be geospatially and temporally
aligned and are highly context dependent (Belle et al., 2015; Shneiderman et al., 2013) .
Third, data is freque ntly stored in sil oed systems with inefficient sha ring at care providers
or in personal devices pr eventing a holistic view of a patient’s medical co ndition (Belle
et a l., 2015; Kohn et al., 2014). Fourth, using this data raises privacy issues (Viceconti et
al., 2015). Fifth, even with all issues resolved, there is still variability in the target
variables since humans differ, complicating the cr eation of accurate and ro bust models.
However, highl y accurate models are needed because the costs of an error are very high
since they influence pote ntially lifesaving decision (Raghupathi and Raghupathi, 2014;
Srinivasan and Arunasalam, 2013).
2.3.4 Public Sector Analytics
Governments and agencies historically store d ata for several reasons including legal
reporting or administrative usage (Fredriksson et al., 2017), while they provide a myriad
of applications and services: tax, health, defens e, public safety and n ational security,
social services, transportation, disaster management, agriculture, energy, government
finance, fire and police services, educ ation or w aste collection ( Daniell et al., 2016;
Gamage, 2016; Malomo and S ena, 2017) . However, with this range comes
decentralization with a resulting immature state of progression in Analyt ics (Da niell et
al., 2016; Desouza and Jacob, 2017; Gamage, 2016; Malomo and Sena, 2017).
Due to its fundamental t ask, a major obj ective of applying Analytics is a more efficient
allocation of public re sources. Analytics-based, thi s might be achieved by understanding
curre nt needs and p references to direct resources to the most ne eded areas as well as
predicting future n eeds, acting proac tively and designing pre vention measures (D aniell et
al., 2016; Fre driksson et al., 2017; Malomo and S ena, 2017). This is ac companied by the
objectives of cost reduction of public services (Ga mage, 2016; Malomo and Sena, 2017)
and increase d tra nsparency of public decision-making (Fredriksson e t al., 2 017; Klievink
et al., 2017) with subse quent increased involve ment in and acceptance of poli cies by
citizens (Da niell et al., 2016).

38
Consequently from th e v ariety of se rvices, th ere i s a multi tude of ex ample applications:
route opti mization for waste collection, transparency initiatives about commissioned
services, optimized infrastructure expansions, res ource allocation after env ironmental or
humanitarian disasters, improvement of a mbulan ce fleet dispat ch, pattern recognition for
city inspector allocation (Daniell et al., 2016; Desouza and Ja cob, 2017; Malomo and
Sena, 2017). However, these examples are limited in range and usually bound to loca l
authorities with rare spillover to other cities. Further, some authorities create digital
channels for service delivery with little known gene rated benefits (Malo mo and Sena,
2017). While data in th e public sector is usually structured and st atic with missi ng
granularity for Analytics, increasingly real-time sensor and camera data are used to
monitor traff ic or identify needed infrastru cture investments (Gamage, 2016; Malomo
and Sena, 2017). In addition, the use of social media data has been attempted but has
revea led several iss ues including the unequal access of socio economic gr oups to digital
communication technologies (Desouza a nd Jacob, 2017).
The public sec tor is facing a va riety of cha llenges which are disruptive to many Ana lytics
initiatives. Da ta acc ess ac ross different a uthorities i s a major c hallenge due to t heir siloed
organizations r esulting in different IT systems and infrastructure, with dif ferent standards
(or rather a lack of stan dards across authorities) , and different methodologies of data
collection (Desouza and Ja cob, 2017; Malomo and Sena , 2017). Further, there are
uncertainties a mongst authorities of what ca n legally be share d since rules a re inacc urate
and concerns of br eaching privacy regulations an d consequent loss of tru s t are high, as
well as, in case of commissioned services, agreements about data sharing might even be
missing (Gamage , 2016; Malomo and Sena, 2017). This is magnified due to unsolved
questions of privac y a bout persona l data ra ising ethica l conc erns about using and sharing
of data (Malomo and Sena, 2017) and in so me cases the prohibition of public
organizations to perf orm tasks outsi de their statutory tasks since they are funded for
respec tive tasks (Klievink et al., 2017 ). If all th ese issues would be r esolved, the public
sector still experie nces a skill gap since the pri vate sec tor can pay higher salaries
(Gamage , 2016). And e ven if this gap could be overc ome, the g eneral operating principal
of the public sector would complicate the us e of Analytics, since d ecisions are made for
society at large, the poli cy making is driven by a non -monetary public value and this
public value is dependent on the poli tical value system of the elected r eprese ntatives,
which might be driven by short (or rather shortsighted) time horizons wit h reelections in

39
mind (D aniell et al., 2016). Efficient or ef fective resource alloc ation thus becomes vague.
Overall, the public sector has a high degree of uncertainty about where and how to use
Analytics (Klievink et al., 2017).
2.3.5 Sports Analytics
Sports is a field craving for quantified data (Hutchins, 2016) and most professional Sports
teams nowadays use Analytics, including the use for on-field decisions (Davenport,
2014b). Sports is seemingly differe nt from other domains , since Sports are usually limited
in time, space with rules for behavior, a predetermined objective and, in many Sports,
very intense interactions between opposing individuals and teams, which t hus compete
on the field while cooperating in leagues which mi ght even share reve nues
(Gudmundsson and Horton, 2017; Stein et a l., 2017; Troilo et al., 2016) . In t erms of
markets, Sports and their professional teams compete with other forms of entertainment
(Miller, 2015), resulting in Sports being quite similar to other conventional industries
with stakeholders demanding the cre ation of value (Caya and Bourdon, 2016).
The objectives of using Analytics va ry with the stakeholder level. In professional Sports,
competitors are organi zed in leagues and federations which strive to attract and retain
fans and sponsors (Caya and Bourdon, 2016; Miller, 2015) . The team stakeholders like
managers and coaches are interested in increasing revenues and financial performance
from selling tickets and mer chandise as well as to im prove their perfor mance in the
respec tive Sport by d evelopin g tactics, player preparation, ev aluation and r ecruitment or
identifying weaknesses of opposing teams (Caya and Bourdon, 2016; Stein et al., 2017;
Troilo et al., 2016). Th e individual athletes de sire – in self-service or with specialists – to
improve their athletic performance by enha ncing understanding of on-fi eld perfor mance,
training, diets, general health and inj ury prevention (Caya and B ourdon, 201 6; Davenport,
2014b).
Consequently, different stakeholders demand various applications. To engage fans,
entertainment produc ts are created from da ta, Analytics and metrics (Caya and Bourdon,
2016). To increa se revenue, dynamic pricing or sponsorship measurements are performed
(Caya and Bourdon, 201 6; Troilo et al., 2016) . Coaches want to explore, which tactics
and lineups work. They d o so by identifying promising on-field positions for actions, by
classifying styles of athle tes and their most likely b ehaviors, by understanding patterns in
successful and unsuc cessful attacks, o r by assessing the importance of players to teams
(Davenport, 2014b; Gudmundsson and Horton, 2017; S tein et al., 2017) . Athletes and

40
coaches ar e int erested in performance metrics of competitions and trainings reflecting
productivity and effectiveness of players, which are constantly advanced to include the
context of players perfor mance. This context might include the presence or absence of
team members (plus/min us analysis), diff iculties of perf ormed actions including distance
to goal or proximity of opponents, weather changing field positi on o r athl etes’ stamina
(Davenport, 2014b; Gudmundsson and Horton, 2017; Stein et al., 2017) . The applications
are possible due to optic al tracking with camer as from TV providers or many installed in
stadia as we ll a s device tr acking with wearable devices collecting loca tion a nd movement
GPS-based as well a s biometric data. Thes e tra ckers provide trajectory data which can be
combined with, often manually collec ted, eve nt data to enable advanced Ana lytics
(Davenport, 2014b; Gudmundsson and Horton, 2017; Stein et al., 2017).
The re sulting demand for analytica l talent and budget usually lea ds to professional sports
organizations cooperating with third-party vendors (Caya and Bourdon, 2016; Davenport,
2014b). The financial power for such invests and the potential benefits is concentrated to
the very popular men sports and leaves behind lower level professional sports, semi -
professional sports, most wome n sports and amateurs, eventually crea ting a digital divide
(Hutchins, 2016). It further requires an open mindset and a positive reception towards
usefulness from coaches and athletes not always available in “old - line coaches” (Caya
and Bourdon, 2016; Davenport, 2014b). In a ddition, applica tions often have only a niche
of user be cause o f differences in Sports (Caya and Bourdon, 2016; Gudmundsson and
Horton, 2017). How ever, while the spe cific appli cations might not be copied from one
sport to another, the idea and concep t of an application might be transferred and adapted
to another sport. Likewise, ideas and concepts of applications of one domain could be
transferred to other do mains, such as th e inv estigation of plus/minus patterns in
treatments could provide input to healthcare or t he context -based analysis of delivery
vehicles in different delivery areas could provide insight s to LSCM.

Submitted version. Published as: Herden, T. T. (2020). Mapping d omain cha racteristics influencing
Analytics initiativ es: The exam ple of Supply Chain Analytics. Journal o f Industrial Engineerin g and
Managem ent, 13(1), 56 -78. h ttps://doi.org/10.3926 /jiem.3004 (published by omniaScien ce, CC BY - NC 4.0)
41
3 Mapping domain chara cte ristics influencing Analytics initiatives - The example
of Supply Chain Analytics
Purpose: Analytics research is increasingly divided by the domains Analytics is applied
to. Literature offers li ttle understanding whether aspects such as success f actors, barriers
and manage ment of Analytics must be investigated domain-specific , while the execution
of Analytics initiatives is simil ar across domains and similar issues occur . This article
investigates characteristics of the execution of Analytics initi atives that are dist inct in
domains and can guide future research collabo ration a nd focus. The research was
conducted on the example of Logistics and S upply Chain Ma nagement and the respective
domain-specific Analytics subfield of Supply Cha in Ana lytics. The fi eld of Logistics and
Supply Chain Management has been recognized as early ad opter of Analytics but has
retracted to a midfield position comparing differe nt domains.
Design/methodology/approach: This research us es Grounded Th eory bas ed on 12 semi-
structured Interviews creating a map of domain characteristics based of the paradigm
scheme of Stra uss and Corbin.
Findings: A tot al of 34 characteristics of An alytics initiatives that distinguish domains i n
the execution of initiatives were identified, which are mapped and ex plained. As a
blueprint for further r esearc h, the domain -specifics of Logistics and S upply Chain
Management are prese nted and discussed.
Originality/value: The results of thi s research stimulates cross domain research on
Analytics issues and prompt research on the identified characteristics with broader
understanding of the im pa ct on Analytics initiatives. The also describe the status -quo of
Analytics. Further, r esults help managers control the environment of initiatives and
design more successful initiatives.
3.1 Introduction
Analytics has b een prai sed to hav e a tremendous impact on th e world economy by
changing the basics of c ompetition and providing leading organizations with an edge in
operations improvements and new business models (Henke et al., 201 6). This has
attracted p rofessionals and researchers alike, creating a v ariety of domain -specific
subfields of Analytics. However, researchers u sually do not work across domains

42
(Holsapple et al., 2014), while they usually not explain how the specific characteristics
of their domain alter the use of A nalytics.
One of these subfields is Supply Chain Analyti cs (SCA) (Chae, Olson, et al., 2014 ;
Sanders, 2016; S ouza, 2 014) or SCM Da ta Science (Waller and F awcett, 2013) , which
concerns the appli cation of Analytics in Logisti cs and Supply Chain Management
(LSCM). Scholars off er little explanation about differences of executing Analytics
initiatives in LSCM as co mpared to other domains. While scholars investi gate the effects
of Analytics on LSCM (Chae, Olson, et al., 2014; Chavez et al., 2017) , they do not
elaborate on the domain -specific execution of An alytics initiatives. Meanwhile, LS CM
research demands education programs for data scientists designated to LSCM (Wa ller
and F awcett, 2013), but while LSCM theor y may help ana lysts to understand the c ontext,
benefits to the understanding of a speci fic pr actical problem are unknown. In addition,
scholars dem and training of personnel in the LSCM domain in Analytics as well
(Schoenherr and Speier- Pero, 2015), while Analytics research argues for t he benefits of
domain independent analysts collaborating with domain experts such as in cross
functional-tea ms inst ead of crea ting designated an alysts (Bose, 2009; Harris and Craig,
2011; Lava lle et al., 2011). Scholar s present opportuniti es and c hallenges of Analytics in
LSCM (Kache and Seuring, 2017; Sanders, 2016) , while opportunities do not impact
LSCM processes and ch allenges are not described for their domain speci fics. Further,
scholars have not prese nted research on challenges being domain -specific or cross
domain.
It is not the purpose of thi s resea rch to ca ll the adva ntages of domain-specific re search on
Analytics int o question . Domain-specific researc h is advantage ous for addressing use
cases from a domain. It i s argued to be mo re meaningful and have increased impact of
Analytics solutions, due to incorporated domain knowledge (Waller and Fawcett, 2013).
However, goal-oriented exchange between domains with similar iss ue s may crea te
benefits in spillovers, as it does in collaborating business units in organizations
(Grossman and Siegel, 2014). Coll aboration across domains on domain-independent
issues can provide benefits due to improved understanding of the issues and broader
solution search and dir ect domain -specific r esearch towards issues critically demanding
domain knowledge. However, there is no good b asis for distinction such as characteristics
of Analytics initiatives displaying potentially differentiating effects and issues in different
domains. Mapping these characteristics entails t he potential to explain maturity and

43
adoption differe nces in exec uting Analytics initiatives across the various domains. For
instance, while LSCM displays an early adopter of Analytics (Davenport, 2009), this
forward-thinking position did not permeate through the field with few organizations
keeping up with implem enting more advanced approaches (2017) but the field regarded
as laggard concerning Analytics (Bange et al., 2015; Thieullent et al., 2016).
Summarized, literature differentiates Analytics by domain with little necessity (Carillo,
2017) besides a more target-ori ented addressing of an audience. Domains advance
differe ntly in applying Ana lytics and research lack explana tions. A clea rer u nderstanding
of dist inctions can direct domain-specific e fforts t owards critical domain -specific iss ues
and stimulate cross-domain research and exchange on domain-independent issues. Thus,
this research pu rsues the mapping of characteristics of A nalytics initiatives poten ti ally
differe ntiating domains. As such, it follows the call for more investigation of differences
of domains in Analytics (Cao et al., 2015). In t his effort, this research focuses on the
domain of LSCM and the Analytics subfield of SCA. Consi dering Ma cInnis (2011), this
work contributes by sketching and delimiting SCA. Consequentially, the researc h
question addressed is: w hat are characteristics of Analytics initiatives setting domains
apart in executing them and which spe cifications of these characteristics exhibits the
domain of LSCM?
This resea rch concerns the increasing use of data to influence and transform businesses
(Carillo, 2017), which is assumed with the label of Analytics. The dist inction to terms
such as Da ta Science and Business I ntelligenc e is vague, leading to constant mix by some
scholars (A garwal and Dhar, 2014; Chen e t al., 2012; Larson and Chang, 2016; Song and
Zhu, 2016). While Data Science is understood as tool for Analytics (2015) and Business
Intellige nce as technolog y focused (Larson and C hang, 2016), managerial issues of both
are trea ted as c oncerning Ana lytics as well. For th e purpose of this research, a distinction
based on methods and technologies does not provide any v alue, while the distinction will
be revisited later .
The remainder o f the paper is orga nized as follo ws: Section 2 provides the theoretical
background. Section 3 focuses on the methodology. In section 4 the res ult ing map o f
character istics of Analyt ics initiatives is presented and discussed. Section 5 concludes
this research and pr ovides impli cations and limitat ions.

44
3.2 Theoretical background
In this article, differences between Analytics initiatives resulting from diff erences relating
to the domain ar e investi gated. Thus, this section presents theoretical considerations on
the domain, practical impact and the incorporation of domain knowledge into Analytics.
3.2.1 The matter of domain in Analytics
The domain refers to the context (Ke nett, 2015), subject field (Holsapple et al., 2014),
area (Mc Afee and Brynjolfsson, 2012) or busin ess function (Be deley et a l., 2018; Carillo,
2017) in which Analytics is applied. Analytics can be applied to a variety of business
processes and industries (Davenport and Harris, 2007) and no limitations of domains to
use Analytics has been identified. This has resulted in an abundance of do main-specific
subfields including Marketing Analytics, Supply Chain Analytics, Financial Analytics
and more, while there is little excha nge between these subfields (Holsapple et al., 2014).
Scholars considera tion o f the domain’s influence on executing Analytics initiatives is
more of a side note. However, the domain is the subject of dat a analysis and solution
deployment a nd its role in an Analytics initiative is e ssential c onsidering that the domain
and its issues are the overall reason the ini tiative exists. An ini tiative does not come out
of the void and is supposed to be based on a bus iness need or opportunit y of a domain
(Grossman and S iegel, 2014; Lavalle et al., 2011; Watson, 2014). The value Analytics
can generate by providing solutions and insights is correspondingly related to the domain,
in which the insights and models/algorithms are deployed to and applied in ( Anant Gupta,
2014; Bedeley et al., 2018; Gupta and G eorge, 20 16). This essential link to the domain
might be fr agile if the do main re presentatives a re not convince d Analytics will meet their
needs. Thus, intensive communication, excha nge and knowledge int egration of analysts
and domain re presentatives is necessar y such that the domain will buy-in and the solution
deployment is not destin ed to fail (Dutta and Bose, 2015; Grossman and Siegel, 2014;
Wixom et al., 2013). A fter all, the domain is typically the sponsor of an ini tiative
(Grossman and Siegel, 2014), including the inv estment of time from d omain experts
(Viaene and Bunder, 2011).
Besides creating a g ateway to purpose, spons oring and subje ct of d eployment of
Analytics solutions, the domains knowledge influences th e search for ins ights. Section
3.2.3 discusses further details on incorporating domain knowle dge into an Ana lytics
initiative. This domain knowledge includ es among others knowledge about an
organizations mission, goa ls, objectiv es and strategies, about organizational policies and

45
plans and the understanding of the potential im pact of Analytics initiatives on
organizationa l performance. F urther, it includes knowledge enabling the interpr etation of
business problems and appropriate solut ions (Ransbotham et al., 2015; Watson, 2014).
Scholars argue for the criticality of this knowledge in the success (or rather
meaningfulness) of Analytics initiative s (Chen et al., 2012; Debortoli et al., 2014; Harris
et al., 2010; Janssen et al., 2017; Wixom et al., 2013).
In detail, dom ain knowledge provides guidance for the ana lytical process b y determining
subsequent steps and a course o f action, identifying challenges, giving directions for
decision point s, and validating results (Ittoo et al., 2016; McAfee and B rynjolfsson, 2012;
Ransbotham et al., 2015; Viaene, 2013; Wixom et al., 2013). I t enhances the identification
of the most valuabl e opportunities and needs or th e best way to apply analytical skills to
provide value to the organization (Grossman and S iegel, 2014; Harris et al., 2010;
McAfee and Brynjolfsson, 2012). The understanding of the business problem can be
improved by domain knowledge, as well as the assumptions behind business ideas and
the objective b ehind applying Analytics (Chiang et al., 2012; Viaene, 2013 ). Regarding
data, domain knowledge leads to choosing the right data and data sources, better
understanding of the d ata as well as potential so urces o f measurement a nd collection
inaccuracy of the data (Harris et a l., 2010; Kenett, 2015). I t helps to mak e sense of result s
of analyses and patterns found (Debortoli et al., 2014; Richards, 2016) and therefore the
crea tion of more valuable models and solut ions and especially t o avoid finding insi ghts
already known to th e domain expert but new to the Analyst (Chiang et al., 2012;
Grossman and Sieg el, 2014; Ha rris and Craig, 20 11; Viaene, 2013 ). In rel ation to this,
domain knowledge is in dicated to improve Analysts effectiveness and th us the fit of
solution to proble m (Carillo, 2017; W ixom et al., 2013), Ana lyst eff iciency and Analysts
engagement (Harris a nd Craig, 2011 ). Finally, domain knowle dge improves
communication of re sults (Chiang et al., 2012; Debortoli et al., 2014).
Of course, the domain is not the sole factor indicated to influence Analyti cs initiatives.
Authors have named internal factors including com pany size (Cao et al., 201 5; Davenport
et al., 2010), the data -savviness of employees and a data-driven culture (Acito a nd Khatri,
2014; Carillo, 2017; Gupta and George , 2016; Ransbotham et al., 2016), exec utive
support, prior successes and available expe rtise (Acito and Khatri, 2014; Ransbotham et
al., 2016). Another moderating factor is the fit of Analytics to organizati onal strategy,

46
structure, and processes (Cao and Duan, 2017 ). This underlines that one Analytics
approac h of an organization cannot simply be transferred to another.
3.2.2 The impact of domain on Analytics
A study on Analytics in different business functions shows that some domains are more
likely to be supported by Analytics than others. Domains that attract most attention are
finance, LSCM, strategy and business development, as well as sales and marketing
(Lavalle et al., 2011). These domains are m ore experienced with statis tical and
quantitative tec hniques, are conside red historically da ta-d riven, and are expected to have
a high paybac k (Acito and Khatri, 2014; Anant Gupta, 2014; Kiron et al., 2012) . This
section investigates the impac t of domains by considering objectives and challenges.
The objectives of collecting and analyzing dat a across domains differ and Analytics
solutions cannot be transferred from one domain (or organization) to another with the
expectation of sim ilar results (Kambatla et al., 2014; Lavalle et al., 2011). This results
from domains pursuing different business objec tives, working differently and having
differe nt iss ues. Considering different industr ies, dom ains differ in regulations,
competitiveness, technological change and standards, Analytics standards, time -
sensitiveness, or their p ublic importance (Acito and Khatri, 2014; Trieu, 2017) . T o
exemplify, medicine and aviation aim to reduce the cognitive load of the d ecision maker
in highl y stressful environments, in which they have high information demands with
inadequate ti me to sort out the most vital information beyond the simple filtering or
aggrega tion (Richards, 2016). In l ess stressful environments but with req uirements for
broad oversight and real-time availability, monitoring is pursued by retail and LS CM
(Watson, 2014). In contrast, marketing or r etail applications target the detection of
changes in behavior potentially presenting new opportunities with a completely diff erent
time horizon (Shuradze and Wagner, 2016; Tr ieu, 2017). Another marketing objec tive of
capturing opinions is sim ilarly pursue d by politics. But while Marketing req uires insights
for personalization, political candidates require i nsights guiding a colle ctive political
agenda to all voters (Gandomi and Haider, 2015; Shuradz e and Wagner, 2016). Predicting
behavior (e.g., demand ) is an essential objective in LSCM and utilities but caters
subsequent objectives s uch as optimal resource a llo cation (Acito a nd Kha tri, 2014;
Watson, 2014). I nsurances want to gain deeper understa nding of why behavioral c hanges
happened to preve nt fraud (Watson, 2014). Finally, domains with high frequencies of

47
reocc urring verbal and written interactions, such as tourism, aspire automation of these
processes w ith Analytics (Gandomi and Haider , 2015).
Different domains also bring different cha llenges for Analytics. Considering the
examples below, these challenges result from complexity of cond ucted analytical
methods or from internal and exter nal organizational matters. Challenges closely linked
to methods and t echniques appe ar in complex a nalytical tasks like environmental studies,
which demand the c ombination of spatio -temporal scaled inputs of sate llite imagery,
weather data and terrestrial monitoring (Kambatla et al., 2014). Domains intending to
understand the structur e of social networks must control dynamic evolution of
connections between entities and dynamic inte ractions via these connect ions. Further
challenges arise from methods generating large vol umes of data output during an analysis
requiring storage, like an astro -physical sim ulation (2014). Additionally, the cost of
inaccuracy o f the Analy tics solut ions differs suc h that false positives (or rather fals e
negatives) o f a diseases or fatal condition in healthcare have a differe nt impact as
compared to customer prefe rences in marketing ( Kambatla et al., 2014).
Technica l challenges can be more frequent and relevant in domains with complex data
integration needs. F or example, in healthca re data is capture d in heterogenic f ormats and
collection is widely distributed over points-of-care (Acito and Khatri, 2014; Kambatla et
al., 2014). Further technical challenges f rom data including data growth, data quality or
the degree of unst ructured data (Chen et al., 2012; Kambatla et al., 201 4), which a re,
however, hard to connect to certain domain characteristics.
Organiza tional challenges concern domains w orking with person- related data. Th e
challenges of securing privacy and subsequent data security a re pressing in domains like
healthcare, e-commerce or e-government and can trigger ethical issues, w hich create th e
need to ethically justify the use of the data (Acito and Khatri, 2014; Chen et al., 2012;
Kambatla et al., 2014). Further organizational challenges are the creation of data without
any or adequate collection and storage, such as domains that do no t store event logs, suc h
as LSCM, or produce l oads of handwritten not es with valuable info rmation, such as
healthcare (Chen et al., 2012). Special organizational challenge s arise in business
domains, since the increase of self-se rvice Analytics create the challenge of inadequate
knowledge of users leading to subverted effectiveness of the decisions made (Richards,
2016). In addit ion, business organizations tend to deploy several m odels and algorithms
at once with different data, requirements of speed, different sourc es of data and data

48
structure, while models, algorithms and their out put are not integrated for consistency
(Kambatla e t al., 2014).
The difference s of objectives a nd c hallenges across the domains aff ect various aspec ts of
Analytics. The consideration above suggest that different domains have different
requirements for Analytics, have different influence o n org anizational aspects of
Analytics, and integra te Analytics diff erently into processes (Cao et a l., 2015; Da venport
et al., 2010; Janssen et al., 2017). Fu rther, domains ha ve different spending on Analytics,
while the organizational performance is impacted differently (Cao et al., 2015; Trieu,
2017).
Considering the practical impact of domain chara cteristics, industry reports give
appropriate insight and show substantia l differences in the most freque nt use cases,
adoption rates, main c hallenges, and data-b ased business models potentially disruptive in
differe nt dom ains (Henke et al., 2 016; Toon en et al., 2016). Striking differe nces in
tendencies for data-driven decision-making as opposed to int uition are reported as well
(Erwin et al., 2016). Howeve r, reports show similarities in challenge s and recurring use
cases recur ac ross domains as well (Bange et al., 2017; Toonen et al., 2016).
Concluding, domains diff er in objectives and c hallenges and further s how different
experiences with Analyt ics. As indicated, these differences result in and from altered
organizationa l, technical, data related or methodological characteristics.
3.2.3 Modes of incorporating domain knowledge in Analytics initiatives
Two aspects ar e explored regarding the incorporation of domain knowledge into
Analytics initiatives: the domain knowledge hold er and the inter action of analysts with
the domain.
Considering the knowledge holder, the necessity of analysts to hold domain knowledge
and be proficient in numerical disciplines specific to the domain they work in has been
argued (Chen et al., 2012; Debortoli et al., 2014; Grossman and Siegel, 2014; Harris and
Craig, 2011). S upposedly, this is ke y to successful ana lysts able to communicate with the
domain representatives. The skills list of the ultim ate breed o f analysts, the data scientist,
usually includes domain knowledge (Carillo, 2017; Debortoli et al., 201 4), as p art of
portraying a jack-of-all- tr ades. In the contrasting second mode, the domain knowledge
holder is a domain expert, supporting analysts to understand data, patterns, results and
their implications because analysts lack the knowledge (Ittoo et al., 2016; Janssen et al.,

49
2017; Richards, 2016; Watson, 2014). This promotes cross-functional t eams in which key
personnel f rom different functions represent the needs of their respective function and
communicate the p rogress and result to it (Bose, 2 009; Dutta and Bose, 2015; Rothberg
and Erickson, 2017 ). A hybrid version of these tw o modes argues for analysts to rece ive
the domain knowledge on the fly during an initiative. They take p art in the data collection,
get sense of v ariations, visit premises, and ha ve focused conversatio ns to gain a
comprehensive and holisti c view, as well as c reate condi tions for cooperative work on the
solution with domain e xperts (Kenett, 2015; Via ene, 2013). This hybrid counters missi ng
communication on important features of the do main by experts, whi ch take them for
granted, requiring scrutiny of analysts.
Regarding interaction, two idiosyncratic mod es have been identified with two hybrids.
The first mode is the centralization of analysts in a separate unit as center of excellence ,
which deploys analysts into domains on demand (Debortoli et al., 2014; Grossman and
Siegel, 2014; Lavalle et a l., 2011). Advantageously , an org anization’s Analyti cs expertise
is shared in that unit, including governance, tools, methods and specialized expertise
crea ting a more c onsistent leve l of effectiveness a cross domains (Kiron et al., 2012;
Lavalle et al., 2011) . This is especially useful for predefined questions reoccurring across
domains (Debortoli et al., 2014). However, it creates distance between analysts and
domains and potentially reduces analysts’ unde rstanding about domains and awareness
of their needs (Grossman and Siegel, 2014), and is vulnera ble to organiza tional politics
(Kiron et al., 2012). In contrast, analysts can be organized domain -specific and
decentralized (Carillo, 2017; Grossman and Siegel, 2014; W edel and K annan, 2016;
Wixom et al., 2013) . The popularity of this approach can be observed in t he richn ess of
domain-specific job post ings (Carillo, 2017; Debortoli et al., 2014). When the need fo r
Analytics is initially re cognized, closeness is desired, and this mode cr eates close
collaboration between analysts and domain representatives, leads to tailored solutions to
domain requirements, and provides analysts with freedom to explore and experiment
(Grossman and Siegel, 2014; Kiron et al., 2012; Lavalle et al., 2011). However, thi s siloed
approac h ignor es the commonalities of tasks across domains, which would allow
exchange with potential benefits, eliminate the need fo r tailored solutions, and sav es
resources. I t can result in skill ga ps, isol ated expertise, and a la ck of leadersh ip to harness
an d develop analysts, resulting in domains left behind (Carillo, 2017; Grossman and
Siegel, 2014; Wixom et al., 2013). It delays the development of broad ex pertise across

50
the organiza tion with flexibility to respond quickl y to emerging issues with out excessive
overhea d (Wedel and Kannan, 2016).
Two distinct hybrid modes are discussed below, while a multi tude of gradations is
imaginable. First, analys ts can be rotated through several domains b y assignment
exposing them to several domains and facilitating their interaction with key stakeholder
(Harris et al., 2010; H arris and Craig, 2011; Wixom et al., 2013) . Th at w ay, analysts are
more aware of the organizations main activit ies, challenges and processes and develop
more understanding o f t he organization overall including strategy and v alue creation
potential from Analytics solutions (Harris et al., 2010). Thereby, the fit to strategy is
indicated as a distinguishing factor betw een low and high performers (C ao and Duan,
2017). R otation further creates exchange between domains and stimulat es the adoption
of Analytics across domains (Lavalle et al., 2011). The rotation can be vice versa, such
as domain experts being deployed to Analytics functions as support as w ell (Wixom et
al., 2013) . The sec ond hybrid organizes analysts by deploying some Analysts in domain s
and keeping som e centralized (Debortoli et a l., 2014; Grossman and Siegel, 2014;
Watson, 2014). This hybrid accounts for problems which can be p erformed by generalists
and for business proble ms requiring highly specialized Analysts, whi ch should be
strongly familiar with th e domain (Debortoli et a l., 2014; Grossman and Siegel, 2014) .
For example, problems without predefined solut ions or of an experimental nature that
might include innovative technologies and c oncepts.
3.3 Methodology
This research aims to e xplore characteristics of Analytics initiatives setting domains apart
exemplified on the LSCM domain. This aim requi res a r esearch design facilitated in
empirical data. Since the rese arch a bout these individual aspec ts is limited and incidental
in existing literature , this re search is e xploratory. Thus, a Grounde d Theory approac h has
been chosen using semi-structured inte rviews for data collection (M anuj and P ohlen,
2012; Strauss and Corbin, 1998).
Grounded Theory comb ined with semi-structured int erviews was used previously in
LSCM research to map phenomena and develop dist inctions. Grounded Theory was
employed to map themes and properties o f enhanced communication that have
explanatory value for dif fere nces in business performance in the employee - to -employee
relationships between su pply chain organi zations (2012), benefit categories of supply
chain clusters hav e been identified with detailed re asoning of distinction (R ivera et al.,

51
2016), and d efinitions of supply chain complexity and supply chain d ecision -making
complexity have been designed, and antecedents, moderators, outcomes and interrelations
have been identified (Manuj and Sahin, 2011). Summariz ed, Grounded Theory generates
depth and understanding of research topics in LSCM when litt le is known about the
research subject and is resultingly suitable for this re search.
3.3.1 Sample and data collection
Initially, experts on SCA were c ontacted for interviews, but these e xperts expressed their
concern a bout their inability to make state ments ab out diffe rences of LSCM as compar ed
to other domains, since t heir experience is li mit ed to one domain. Consequently, experts
with experience in executing Analytics initiatives in different domains we re sought by
approac hing “ Data Analytics Companies” (Beer, 2018). Specifically, experts in these
organizations were contacted and asked about their experience with different domains
and their experience with LSCM. Experts that signaled knowledgeability about LSCM
and several other domains were asked for inter views. For this purpose, a list of top
solution vendors and inte grators for Analytics w as extracted from ma rket reports. A list
of 110 “Data Analytics Companies” was compiled and pa rticipants from managerial and
senior posi tions were chosen for establishing contact. An initial sample of interviewe es
has been sought b ased on experience, job title, p rofile and willingness to participate.
Subsequently, theoretica l sampling was used in accordance with the Grou nded Theory
approac h (Manuj and Pohlen, 2012; Mello and Flint, 2009; S trauss and C orbin, 1998).
Thus, the choice of contacted experts was determined by the emerging theory from
analyzing the conducted interviews. With emerging theory, interviewe es were recruited
with the objective to d evelop further und erstanding in certain aspects . Theref ore, personal
and company p rofiles we re taken int o focus, while job title and experience requirements
were rel axed. Later interview requests targ eted more technology focused organiza tions
as well as expe rts in Prescriptive Ana lytics topics. Eventually 13 interviewees have been
recruite d resulting in twelve interviews including one interview with two intervie wees.
For reasons of anonymity, position and organiza ti ons are presented as lists:
• Positions: Head of Analytics (2), Director Analytics (3), (Se nior) Manager
Analytics (3), Consultant Analytics (2), Solution Arc hitect Analytics (3)
• Organiza tion: Solution Vendors (5), Solution and Service Vendors (2),
Consultancy (1), S olution Vendor and C onsultancy (2), Integrator (1), Service
Vendor and Consultancy (1)

52
Figure 9 summarizes the years of experience distributed over interviewees as well as the
interview duration. Interviews were conducted via telephone and VOIP conference
systems. During the int erviews, handw ritten notes were taken for the purpos e of recording
and guiding the interview. The interviews were audio-recorded if permission from the
interviewee s was granted . Audio-records were transcribed a nd deleted afte rwards.

Figure 9: (left) Duration of Interviews with Experts, ( right) Experts Experience in Analytics
Following the rec ommendations for Grounded Theory (Strauss and C orbin, 1998) ,
interviews were started with grand tour open-end questions (McCrac ken, 1988). First,
interviewee s were aske d about their understanding of the terms “Analytic s” and “Data
Science” . Second, th ey were openly asked about the differences of An alytics initiatives
executed in LSC M compared to other domains. S ubsequently, the open questions were
extended by focused qu estions. To provide a systematic exa mination o f the interviewee s’
experience and knowledge, interviews were structured on cause categories from the
Ishikawa diagr am – a tool for identifying ca uses. Eight cause categories were used in this
approac h (2016). Th ese o riginally generic cause categories were adjusted to Analytics by
referenc ing the cause categories to more specific topics from Analytics. The approach
was used to provide a systematic and broad fo cus of differences to discuss with the
interviewee s. The ca tegories are as follows: People (users and domain experts), Methods
(analytica l and initiative management), Machines (hardwa re and softwa re), Material
(data), Measurement (metrics of success, objectives), Environment (pa rtners and external
data), Management (organizational man agement), and Maintenance (solution
maintenance).

53
Interviewees reported the differe nces of domains in executing Analytics initiatives to be
nuances. However, th ese nuances corresponded to the characteristics aspired to identify.
Thus, the characteristics, or rather phenomena (1998), and how they influence Analytics
initiatives were mappe d as prese nted in section 3.4. I nterviewees e lucidated the nuances,
they perceive, b ased on Analytics initiatives they ha ve c ontributed to. The systematica lly
semi-structured interviews lead to three forms of characteristics: (1) interviewe e s
presented distinguishing characteristics of LS CM from other domains, (2) interviewee s
ex plained characteristics with differe ntiation potential through examples that distinguish
other domains and commented that LSCM does not differ from the majorit y of domains,
(3) intervie wees explained distinguishing cha racteristics that were pre viously differe nt in
domains but not curre ntly . The latter was primarily influenced by th e current hype -level
of Analytics ca using changes in the characteristics across domains.
3.3.2 Data Coding and Analysis
Data was analyzed in accordance with the guidelines of Grounded T heory as described
by Strauss and Corbin ( 1998). In the first step of the analysis of interview trans cripts,
open coding was pe rformed following each inte rview with the int ention to i ncorporate
new aspects int o the subsequent interview. In open coding, the int e rviews were
conceptualized on a sent ence-by-sentence b asis b y labeling them with sho rt explaining
phrases or terms. Similar statements were given the same label. The labels, and concepts
they represent, were used to identify “categories” in subsequent steps, which reflect
phenomena such as events, conditions or actions/inte ractions. After twelve int erviews,
theoretica l saturation w as attained suc h that incre mental interviews were not expected to
yield additional information. The analysis was p erformed using the ATLAS .ti software
and resulted in 90 l abels. After all Interviews were conducted, following Strauss and
Corbin (1998), the labels we re reevaluated to discover the categories. Thus, the concepts
were grouped under high er ord er categories with a n improved ability to explain or pr edict
phenomena. For this p urpose, a category by category comparison was condu cted.
Resulting higher orde r c ategories were subsequent ly given na mes with explana tory value
and these categories were developed into ph enomena by using the interview chunks to
derive e xplanations describing the phenomena and delineating them from other
phenomena. The se steps eventually conc luded in 34 categor ies.

54
In the second phase of axial coding, links betwe en categories were s ystematically
developed by using the p aradigm sch eme and it s components (Strauss and Corbin, 1998) .
The paradigm scheme co mponents re commended by the Grounded Theory guidelines are
conditions, actions/interactions and consequences as illustrated in Figure 10 . Conditions
are divi ded into c ausal conditions, which influence othe r phenomena, contextual
conditions, which have their source in ca usal conditions and create circumstances or
problems to which persons re spond through ac tions, and intervening conditions, which
mitigate or alter the im pact of causal conditions and must be responded to by actions.
Actions represent strategies devised to manage or respond to a phenomenon such as
causal conditions. Consequences are outcomes or results of actions.
In the final phas e o f selective coding, c ategories and components w ere i ntegrated and
refined (Str auss and Corbin, 1998). Thereby, more detailed and comprehensive
explanations on phenomena w ere de rived by rev ising them to indicate connections to
other phenomena according to the data. Appropriate to this purpose, the components from
axial coding have been spli t such that connec tions could be revised to represent the links
in accorda nce with the data to form a well-developed map of c haracteristics of Analytics
initiatives potentially setting domains apa rt.
3.3.3 Trustworthiness
Following previous studi es using Grounded Th eory in LSCM research (Gligor and A utry,
2012; Manuj and S ahin, 2 011) and studies reviewing Grounded Theory appr oaches (Denk
et al., 2012; Manuj and Pohlen, 2012), multiple criteria for trustworthiness were collected.
These crite ria a re credited to the Straussian School of Grounded Theory , which was
followed closely in the research. The following criteria were addressed: ( 1) Credibility
was addressed by p roviding a summary of the phe nomena with descriptions and links to
the participants for feedb ack and reflection; (2) Transferability was ensured by applying

A ct i ons C on sequ ences
C ontextual
con di ti o ns
I nter veni ng
con di ti o ns
C aus al cond i t i on

Figure 10 : The paradigm scheme of components

55
theoretica l sampling; (3) Dependability was addressed by following the guidelines of
Strauss and Corbin for Grounde d Theory (Strauss and Corbin, 1998) and McCra cken for
interview design (McCr acken, 1988 ); (4) Confirmabilit y was aspired by a te chnique using
an altered form of bracketing (Kvale, 1983) as des cribed by Manuj (2011 ), w hich requires
the a uthors to write down the essential points known about the research subject. The pre-
existing knowledge was afterwards compared to the results. P henomena th at overlapped
in pre-existing knowledge description and results we re r eviewed f or existence in the
transcripts; (5) Integrity was established by maint aining anonymity of the interviewees;
(6) fit was ensured by the methods for cre dibility and dependability; (7) Understanding
was also addressed by the summary provided to interviewees and the inquiry to fee dback
and reflect them; (8) Control was given to interviewee s who had some control to direct
the interview to topics th ey perceived as important; (9) G enerality was aspired with the
length and open questions of the interviews and the subsequent systematic structure
intended to cover as many area s as possible.
3.4 Results and discussion
This section explores the results by pr esenting the characteristics of An alytics ini tiatives
differe ntiating domains and specifics of LSCM.
3.4.1 The map of characteristics of Analytics initiatives differentiating domains
The characteristics derived from the data analysis have be en map according to the
paradigm scheme of S trauss and Corbin (1998). Due to subst antial difference s of
character istics allocated to components, the components have been further segment ed.
This resulted in eleven sub -components with 32 characteristics of Analytics initiatives
differe ntiating domains. A twelfth component has been crea ted describing the c oncept of
Analytics, which is independe nt from the domain. The components and their
character istics, which are explained in the upcoming section, are illustrated in Figure 11.
3.4.1.1 The concept of Analytics
Two domain independent characteristics were identified, which describe the
interviewee s’ conceptualization of Analytics a nd di stinct roles and a ttributes of Analysts.
The term degrees of Analytics hints at the degr ees of business intelligence of Davenport
and Harris (2007) and addresses different levels of complexity of analytical methods.
However, they rep resent contempora ry complexity levels, which are subject to change
over ti me. Analytics has been recognized as most complex degree of analytical methods

56
to the overall conc ept of Business Intelligence twelve ye ars prior (Davenport and Ha rris,
2007). However, the inter viewees of this study recognized Analytics as overall term, with
Business Intellige nce as least complex degree, An alytics, with a second function for the
term, as label for the m oderate degree, and dat a science as most complex degree of
analytical methods. I n the introduc tion, the ter ms were expre ssed as be ing hard to
distinguish and interviewees explained this as somewhat artificially created. The methods
and technologies constantly evolve, but distinct labels advertised as innovations help to
draw attention to the topic. This attention helps to either mark et evolved analytical
concepts to more m ature organizations for n ew use cases or present interesting
opportunities to organizations less experience d in Analytics. Resultingly, thi s leads to
confusion but helps to increase the popularity of analytical methods. In regard to thi s, a
label sim ilar to “c ognitive intelligence”, an invent ed term to describe the business ve rsion
of artific ial intelligence (Maissin et a l., 2016), might be a plausibl e candidate for the next
label. In short, Business Intelligence is currently understood to c omprise methods of
manually and experience- or int uition-driven analysis of structured data, mo stly from data
warehouses, vulnerable to human bias and with les s adv ance d methods. Analytics is
understood as comprising more a dvanced methods on structured data resulting in model-
and algorithm-driven insight relying on human intuition and experience to a lesser degree.
Data Science was underst ood by interviewe es to refer to the most complex and a dvanced
analytical methods (machine learning, adv anced sta tistics) supposedly minimizing human
bias and applied to unstructured data as well, of ten with a more experimentation and
proof-of-concept focus as opposed to driving business decision-making.
Four Analytics roles w ere extracted, which ar e justified to exist in parallel in an
organization. First, business users use embedded analytical functions from software
accessible to them. Second, controllers aggregate and group d ata and numbers and must
assure correctness of data for the purpose of reporting higher management as well as lega l
authorities. Third, business analysts are business function -specific analys ts familiar to
some advanced methods for stru ctured data in terms of purpose and application with the
intend to produce consumable insi ghts for management or other non -analysts. Fourth,
data scientists are application developer s with a full range of knowledge a bout methods
and tools at hand, from simple methods in graphical user interfaces to a dvanced methods
applied “at the command line”, who have deep technical and analytical knowledge and

57

M ap of Dom a i n-s pec i fi c s of Anal yt i cs Ini t i a t i ves
Ex tr a -o r g a n i z a ti o na l
c a u s a l c o nd itio n
in f lu e n cin g p r o c e s se s
▪ De v el o p m e n t o f d ata
An al y t i c s term s
▪ Hy p e -l e v el o f A n a l y t i c s
▪ P a i n P o i n t s
▪ Re g u l a ti o n s
Intr a-p r o c e s s ua l c a u sa l
c o nd itio n in f lu e ncin g Ini ti a tiv es
▪ S t a te o f p ro g re ssi o n
Ana ly tic a l A c tio n s in Ini tia tiv e s
▪ De s c ri p t i v e A n al y t i c s
▪ P re d i c ti v e A n a l y t i c s
▪ P re sc ri p t i v e A n al y t i c s
(Aim e d) Co nse q u enc e s o f
Ini ti a tiv e s
▪ F i n an c i a l o b j e c ti v e
▪ Ac c u rac y o b j e c ti v e
▪ Effi c i e n c y o b j e c ti ve
O r g a n iz a tio n a l c o nte x t
c o nd itio n f o r Ini tia tiv e s
▪ Bu d g et
▪ To p M a n ag em en t su p p o rt
Ana ly tic s In i tia tiv e s l i f e c y c l e
Ac tio ns
▪ Op era ti on a l i z a ti o n
▪ M a i n t e n an ce o f So l u t i o n
Th e c o nce pt o f A na ly tic s
▪ De g re e s o f An al y ti c s
▪ Da ta An al y t i c s Ro l e s
App licati o n c o nte x t c o nd i ti o n f o r
Ini ti a tiv e s
▪ p ro b l e m -so l vi n g a p p ro ac h
▪ S el f-Serv i c e An al y t i c s
▪ Da ta a b u n d a n ce
Intr a -o r g a n i z a ti o na l
c a u s a l c o nd itio n
in f lu e n cin g pr o c e s se s
▪ Da ta d ri v en Cu l tu re
▪ P ri o r K n o wl e d g e o n
An al y t i c s
O r g a n i z a ti o na l in te r ve n i n g
c o nd itio n s o n I n iti a tiv e s
▪ Cro ssi n g fu n c ti o n a l
b o u n d ari e s
▪ Da ta o w n ers h i p i ss u es
▪ Da ta se c u ri ty Is su es
App lica ti o n (pr o c e ss u a l) in ter v e n in g
c o nd itio n s o n I n iti a tiv e s
▪ In h e re n t An a l y ti c s U se Ca s e s
▪ i n h ere n t A n al y t i c s p ro c e s se s a n d m e th o d s
▪ Da ta sh o rta g e
▪ Da ta q ual i ty Is su e s
▪ He te ro g e n e i ty o f u s e d d ata
▪ Us i n g e x tern al d ata
▪ Us i n g m ob i l e se n so r d ata
App lica ti o n (te c hn ic a l )
in te r v e ni n g c o n d i tio n s o n
Ini tia tiv e s
▪ In t e g rate d s y ste m s l a n d s c a p e
▪ S tan d a rds fo r d ata e x c h an g e
Ca u s a l c o nd itio ns
Co n te x t c o n di tio n s
Inte r v e n in g c o n d i tio ns
Ac tio ns
Co n s e qu e nc e s

Figure 11 : Map of domain-specific aspects of Analytics initiatives

58
skills and try to stay up to date on methods. Their jack-of-all-trades-image was rejected
in the interviews and they were rather criticized for their tendency to produce non -
consumable results for n on-analysts and to develop applications, which are not scalable,
reinvent existing concepts and do not addre ss business needs.
3.4.1.2 Causal conditions
Causal conditions repre sent sets of characteristics influenc ing other characteristics and
conditions that explain why persons, or organizations, respond as they do (Strauss and
Corbin, 1998). Due to the interviews, three sets hav e been identified: ext ra-organizational
causal conditions, intra-organizational causal conditions and conditions influencing
Analytics proce sses in an organiza tion and subsequently the Analytics initi atives.
The first characteristic of e xtra-organizational causal conditions is the mentioned
patterns of development of data Analytics terms leading to new taxonomy about “every
5- 7 yea rs or so ”. The new and unheard-of conc epts usually highl ight aspects that were
already used to a lesser degree in pre vious it erations. These c hanges mobilize ne w groups
to use Analytics or exist ing user groups to identify new use cases domains differently.
Second, as infa mously repre sented by the Gartner hype-c ycle, technologies a nd c oncepts
undergo cycles of temp orary publicity, leading to changing Hype-levels of Analytics.
This results in an eruption of projects to create b enefits from data in a sort of “gold rush
atmosphere” with exaggerated expectations on prof itability and ea se of applying
Analytics. At the moment of this study, organiza tions are eager to create Analytics
subsidiaries (e .g., Da ta Labs, Data Factories) with high top mana gement attention, fa st to
perish if they fail. This kind of adoption based o n momentum of other adopters and
success stories is also termed bandwagon behavior, which can l ead to a min dless adoption
as opposed to mindful and thus wary and appr opriate to the organization (Fiol and
O’Connor, 2003; Swanson and Ra miller, 2004), with domains displaying this be havior in
differe nt degrees. Third , interviewee s described the rather abstr act phenomenon of
external pain points that create different stimul us to interest and need for Analytics in
differe nt domain. This characteristic w as r etained a s vivo code (Strauss and Corbin, 1998)
to express the recurring inability of int erviewe es to explain it more tangible. This p ain
point could be som ething like competitive press ure, reducing environmental impact,
regulations or customers demanding Analytics solutions. Forth, a specifical ly mentioned
external stimul us to use Analytics are regulations. The regul atory dem and to repo rt
various aspects of organi zational operations and a ctions is a strong motivation to deploy

59
Analytics, especially if fu ture prognos es are demanded such as for th e banki ng domain to
avoid market crashe s and monetary deva luation.
For intra-organizational causal conditions , one characteristic with different impact in
differe nt domains is the data-driven culture , which supposedly has a plethora of effects
on the applic ation of An alytics in organizations, as discussed by scholars (Holsapple et
al., 2014; Kiron et al., 2012; McAfee and Brynjolfsson, 2012) . This culture positively
influences th e coop eration of users and experts within Analytics initiatives, and
acceptance and use of the Analytics solution but requires suff icient change management.
Otherwise users show unwillingness and devalue solut ions (“ this is a one - time eff ect”,
“data have been flawed and antiquated”). Another characteristic of this causal condition
is the prior knowledge on Ana lytics. This prior knowle dge paves the way for the
application of An alytics, colla borative initiatives a nd the use cases that ca n be addressed.
However, interviewees highlighted the background knowledge being less important than
the willingness and interest to ac hieve a successful improvement of pr ocesses using
Analytics. But this motivation is often depende nt on knowledge.
Finally, the causal condi tions above influence th e in tra-processual causal condition ,
repre sented by the characteristic of state o f progression of Analytics. This characteristic
refers to maturity, advancement of use cases and adoption rate, that differs across
domains.
3.4.1.3 Context conditions
Context conditions describe conditions originating in causal conditions and creating
circumstances and iss ues for Analytics initiatives to which people respon d to through
actions and interactions (Strauss and Corbin, 1998). In contrast, intervening conditions
moderate the effect of c ausal conditions. The id entified context conditions have been
grouped into conditions concerning org anizational processes and conditions concerning
the application of Ana lytics.
Regarding organizational conte xt conditions , th e first identified characteristic is budget
to execute Analytics initiative s, which might be additionally allocated, reallocated from
IT or not allocated at all. It can be allocated goal oriented to create in nova tions or
“ halfhearted ” by hiring “ some Data Sci entist s” without any ideas for use cases due to
hype. I n contrast differe nt behavior of domains in the past, orga nizations across domains
are currently allocating budgets into Analytics i n magnitudes surprisin g and unseen by
the interviewees, while organizations without financial means wait for technology

60
providers to develop ap plicable solut ions. S econd, long -term value from Analytics
requires str ategic Top Management support , as discussed by scholars (Davenport and
Harris, 2007 ). I nterviewees explained strategic vision easily lacking in either IT and
business unit s, with the former prioritizing te chnical spec ifications and standardization
over functionality and displaying protectionism, and the latter being stuck in da ily
business or conce rned a bout increase d workload. Top management support is require d
for a goal-oriented course of actions with Analytics, encourage change and create
visibility of the value of Analytics – a v alue that is re cognized dif ferently across domains.
An interviewee described: "if nobody recognizes the value of an initiative, it will not have
success".
Concerning application context condition s , it must be recognized that Analytics has low
sole standing self-purpose and requires a problem -solving approach to add ress business
problems or cases – “something with a user story behind” – at the core of ini tiatives, as
emphasized by schol ars (Herden and Bunz el, 2018). The problem needs to be clearly
defined and it s solution promise valuable returns, whe ther for d ata a ggregation of r eports
or for strategic enterprise-wide analytics initiatives. This business problem was expressed
to be more relevant than superior algorithms or models, with timely available solution s
“ put on the road" bein g more v aluable than non-deployable and d elayed supe rior
algorithms. This problem- solving approach proliferates with incre asing experience with
Analytics but organizations across domains are sti ll performing Analytics initi atives
without a problem. These are unlikely to address business nee ds and result in abandoned
pilot s, undeployed solutions, or mi ssing users for d eployed solutions. A second
character istic and a str ategy to ensure to address business problem is to give busi ness
users means to apply Analytics by themselves – so called self-s ervice An alytics.
However, this re quires users’ abilities to apply quantitative methods, while access to data
and tool s must be provid ed with only some domains putting it to the test. Third, due to
the promised value fr om data and the tec hnological ease of data collec tion, orga nizations
across domains experience a data abundance level ing the varying data access in the past.
This does not imply access to all the data required for their ini tiatives. Organizations
collect and store d ata wi thout a specific purpose to harvest the value at a later point in
time, while the number of data sou rces increases constantly, and collection is becoming
cheaper. Consequently, they try to harve st value from da ta for cefully while contra dicting
the problem-solving approach with spora dic success.

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3.4.1.4 Intervening conditions
Intervening conditions mitigate or alter the impact of causal conditions on Analytics
initiatives and are respon ded by a ctions (Strauss a nd Corbin, 1998). Th ey contrast context
conditions, which are triggere d by c ausal conditions. The identified interven ing
conditions were grouped into organizational conditions, conditions conce rning the
process of executing A nalytics initiatives and conditions concerning the required
technologies.
The first or ganizational intervening c ondition was mostly rec ognized as spe cific to the
LSCM domain, which is the crossing of functional boundaries. The characteristic
describes data being collected, stored and own ed by partners and An alytics solutions
required to b e deploy ed a cross boundari es to these partners as we ll. However, b oundaries
can already o ccur in the same organization between business functions, w hich are rarely
crossed in some domains. A closely linked se cond characteristic is da ta own ership issues .
Thereby, as opposed to the previous cha racterist ic data owners with no business
relationship are considered, which possess relevant data. Data collected b y a third party
or using a technology of a thi rd party is often owned by that third party resulting in
additional agreements. These a re increasingly used in some domains as source of revenue
– “most data own ers have recognized the revenue potential by now” – and increase the
cost of Analytics initiative s. Unwilli ngness of this third party can further prevent access
to necessar y data for a n init iative. This issue is interrupting orga nizations across all
domains, but interviewees suggested that organizations could accept Analy tics solutions
from data owners instead, while saving resources by buying (decision-ready) insi ghts.
Third, the character istic o f data security issues is us ually a major concern in domains with
highly se nsitive data re quire d to protect privacy of individuals. This induces steps to limit
access to data or to anonymize them complicating their use in analytical methods and
demands additional infrastructure in hardware and software to increa se protection against
unauthorized access.
The category of ap plication (pr ocessual) intervenin g conditions is the lar ge st. The first
character istic, intervi ewees unanimously agreed u pon to be t he main and most tangible
distinction coming differe ntiator of domains, is the inherent Analytics use cases. This is
self-evident, since the business tasks, processes, objectives, roles, p eople fil ling the roles,
their knowledge a nd their voc abulary are different. Thus, metrics, d ata, a nd requirements
of Analytics S olutions are different resulting in various use cases inherent to every

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domain. However, some use cases recur in numerous domains. Sec ond, interviewee s
agree d un animously upon the lack of domain inh erent Analytics pro cesses and methods.
Analytics is chara cterized by the transferability to any domain. This includ es the proc ess
of executing initi atives, the process management techniques and, in particular, the “ very
transferable" analytical methods. However, c hoice and adjustments of a methods a re
dependent on the specific use case resulting in some methods being used more often in
certain domains. Third, while there is some abundance o f data as explai ned above, for
certain problems and use cases a shortage of data can occur in some domains, since not
everything interesting is currently collected or col lectable. The required data collection
technology may not exist or is not available for a reasonable resource commitment . Thus,
the deve lopment of a technology or its re duced price can spont aneously e nable a ra nge of
organizations to execute certain initiatives such was with internet - of -things (IoT) s ensor
data as discuss ed below. Fourth and closely linked to data shortage are d ata quality issues ,
which have been indicated as cross domain issues (Hazen et al., 2017) but to varying
degree s in different domains. They result from false entries, mi ssing entries, conflicting
entries and unst andardized data entries and p revent integrated analysis and more complex
Analytics initi atives, which can even oc cur in the same organization. He nce, resources
are redi rected from Ana lyti cs initiatives to initiative s to integra te data. Fifth, as discussed
above, fo r complex analytical approaches, usu ally seve ral d ata sour ces must be com bined
crossing boundaries o f o rganiza tion s, business u nits or process steps le ading to issues
with heterogeneity of data as a character istic. Even compara ble processes may entail
differe nt machines or diff erent people in charge of proc esses and, thus, create diff erences
in data (e.g., data collection fr equency, da ta availability, data structure, or data
granularity). As a r esult, integration binds resources otherwise used for insig ht generation
in some domains. S ixth, a contemporary sti mulus for adopting An alytics is the use of
external d ata, du e to wide applicability and increa sed availa bility. One interviewee
explained that “a s of no w, using externa l data is common sense ” and most domains use
them for im proved results (e.g., for LSCM, data on infrastructure, weather, traffic, natur al
disasters, political conditi ons, and regional customer characteristics and prefere nces ).
Finally, the use of mobile sensor data has increased due to advances in their technology
and especially integrabili ty and remote data acce ss ability. In particular, the c haracteristic
is becoming quite relevant in some domains, and resultingly distingui shi ng it from other
domains, due to IoT sens or devices, transmitting data via GSM or other mobile signals.
These sensors cr eate access to new kinds of m obile data (e.g., ambience, vibration,

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brightness, sound level, movement, image-based condition, position), while data becomes
available in a higher gra nularity and frequency.
Concerning ap plication (tec hnical) intervening cond itions , some domains experience
issues in these conditions that increase the im pact of issues discussed in previous
categories. The fi rst technologi cal characteristic a ltering organizations abil ity to apply
Analytics is an integra ted systems landscape, which enhances Analytics if present and
obstructs otherwise. The replace ment of outdated systems, which lack the performance
of modern syst ems, can be too gre at of a risk for organizations depe ndent on these
systems’ functionalities and worrying about l osing them. S ystem landsc apes grow
naturally and so are th eir data structures resulting in established organizations los ing
overview of their systems in terms of func tionality and operating method as well as in
inappropriate or missi ng updates to the systems. Onc e deliberately e mployed tailored and
task- specific syst ems lack scalability and integrability in focus of tod ay’s syst ems
landscapes and, nowadays, the effort of orchestrating these systems is ch allenging and
resource consuming . Start-ups and younger organi zations are usually spared from these
challenges but most organizations in established domains cope with them and must
redesign their systems landscape. A second prominent characteristic that creates
challenges for organizations in execution of An alytics ini tiatives is standards for data
exchange. Internal data e xchange standards ar e averted from leg acy systems in the
systems landscape or ov erturned b y merger and acquisition. Externally, some domains
developed standards for certain data exchange processes such as the EDI ( Electronic Data
Interchange) standard, but these are usually barely sufficient for Analytics requirements.
The currently used interface landscape created t o enable data ex change is argued by
interviewee s to be sufficient for current needs and the development of a standard would
lack a ne cessary autho rity such that scholars’ demand for a common shared understanding
on the definition of standa rds a nd interf aces (K ache and Seuring, 2017) might not be me t
any time soon.
3.4.1.5 Actions
Actions repre sent strategi es devised to manage, handle, carry out or respond to conditions
(Strauss and Corbin, 1998). Thus, the analytical actions identified in this study are
initiated or altered by the various identified causal, context and intervening conditions.
Distinguished are Analytical actions in initiatives that represent the use cases of analytical
methods and actions re lated to the lifecycle of Analytics ini tiatives.

64
In accordance with a wid ely recognized perspective, the analytical actions in initiatives
are distinguished in Descriptive, Predictive and Prescriptive (Holsapple et al., 2014;
Souza, 2014; Wang et a l., 201 6), which were e xplained by interviewees to repre sent
complexity leve ls but only to a limited extent. All a pproaches employ (relatively) simple
and complex methods a nd initiatives usually demand the combinations of diff erent
approac hes. R egarding t he fi rst approach of Descriptive Analytics, the methods are
predominantly less analyti cally complex with r ule-based data aggr egation analysis.
However, they can become technically complex when several h eterogeneous data sour ces
are supposed to become i ntegrated. Currently, interviewee s experience high demand for
such initiatives from organizations in some domains attempting to create “a s ingle version
of truth” of their complex operations in likewise co mplex organizational stru ctures, whi ch
are not manageable by intuition anymore and requ ire data-driven de cisions and control.
The created insight embodied in reports, key performance indicators and dashboards is
usually post -opera tional and provides tr ansparency a nd visi bility of the status of the daily
business, m ismatches of results to expectations, weak spots, benchmarks for different
decisions, a nd ne eds for actions – not nece ssarily which actions. I t wa s c redit ed as “ good
entry level Analytics approach ” by int erviewees but creates meaningful insi ght,
nonetheless. The se cond charac teristic, or rather approach, is predictive Analytics, which
is curr ently broadly requested across domains, w hile some domains took time to catch
on. Famous due to demand forecasting, predictive Analytics provides use cases for most
domains, while it is deployed in higher or lower analytical complexity. Third, prescriptive
Analytics mostly consists of the application of o ptimization methods. While more
complex optimization use cases are concentrated to few domains, the methods are
genera lly used in most domains. F urther, the applied methods a re used sometimes applied
to simpler repetitive problems and as such provided as features to software tools without
further individualiza tion leaving potential for improvement.
Regarding the lifecycle actions , the identified characteristics c oncern th e bene fits of
analytics initi atives in the short and long term and issues in these characteristics c an
eradicate any productive ac tivities in the previous steps of the ini tiative. F irst, a curr ently
major issue in many domains is the operationalization, the so -called de ployment, of
Analytics solutions. There is a shift towards p roviding more Analytics solu tions directly
into operational proc esses to improve decision-making at the operational le vel instead o f
the managerial lev el only. The insights are used faster and the users at that level work

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naturally with insights since it is based on their tasks and de cisions. Further, they are
incentivized to collect and insert data more carefully beca use they get bet ter insights or
better processes in re turn. However, this phase is prone to be underestimated in planning
of the ini tiative, and challenges, overlooked user requirements and the h eavy resource
consumption can result in aba ndon ed pilots. Second and similar, the subsequent
maintenance of developed Analytics solutions, such as algorithms and models, is
supposed to ensure corr ectness, adaption to th e proce ss, pe rsistence of accuracy o r
adjustment to new pattern s in newer data. As scholars indicated, this requires a continuous
monitoring and evaluation of even prov en useful analytics solution (Leventhal, 2015) .
However, while us ers are familiar with updates for softwa re, maintenance of An alytics
solutions is in some domains alien to them that lack maturity in Ana lytics.
3.4.1.6 (Aimed) Consequences
Finally, conseque nces are the outcome of ac tions and as such the outcome of the
investigated Analytics initi atives. Corresponding to the research method, the
consequences below refer to intentions and aims.
The first and foremost aimed consequence is the characteristic of aspiring the financial
objective, whereby short-term costs savings and revenue increase must be distinguished.
Analytics tends to provide direct ben ef it s (impr oving proc esses, increasing revenue),
which induce indirect monetary payoffs as co st savings. An ini tiative must be cost
effective in this indi rect way, since it displays a n i nvestment that is supposed to c reate an
output higher valued than its in put li ke any other investment. The financ ial objective,
which is pursued in some domains, stands outside of this cost -effectiveness and refers to
direct cost savings and increase rev enue. However, interviewees usually ad dressed non -
monetary objectives. Second, one non-mon etary objective is the a ccuracy objective
referring to t he need for h igh accuracy of Analytics solutions due to criticality of business
process es . Criticality can result fr om domain-specifics such as possible ha rm (e.g.,
pharma, aeronautics), adherence to l aws (e.g., ta xes), or costs of inaccurate decisions
(e.g., consumption of lo w margins in retail). Consequentially, users must communicate
reasona ble requirements on the accuracy, since it influence s the dimensions of Analytics
initiatives. Third, anothe r non-monetary objectiv e is the efficie ncy objective regarding
process es by identifying and eradica ting inefficiencies. This may concern the
identification of sources of lost time, insufficient qua lity, or waste and c reation of
monitoring solutions that support control of these inefficiencies.

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3.4.2 Specifics of the LSCM domain
LSCM shows several differences in the mapped characteristics in all components except
context conditions, which are discussed b elow. This implies benefits from domain -
specific re search with extensive domain knowledge on the issues. However, the majority
of characteristics, not discussed b elow, represent characteristics o f Analytics initiatives
that allow cross-domain research for improved approaches or to create measures to
overcome barr iers.
3.4.2.1 Specifics in causal conditions
Interviewees attested a certain scarcity of p ain points in LS CM leading to low perceived
external pressured to use Analytics as compared to other domains. Orga nizations in
LSCM are usually driven by the internal needs to handle and control the daily business
and opera tions motivating the use of Analyt ics if this control is perce ived as
unsatisfactory. Customer s may create an indirect stimulus by demanding more efficient
services, but only few customer requirements specifically demand Analytics and,
especially, few were rep orted to demand Analyti cs solut ions beyond market available
solutions that necessitate Analytics maturity.
Considering regulations in particular, LSCM was also reported to have fewer and less
complex regulations, but still has to report things like journey times of drivers or
compliance to customs, taxation or customer requirements. Environmental regulations
were spe culated to potentially increase the use of Analytics in LSCM but the current
influence of regulations on the state-of-adoption of Analytics in LSCM is low compared
to other domains.
Interviewees reported to perceive LS CM as more directed towards an in tuition -driven
culture as opposed to a data-driven cultur e, which was, however, described in aspects to
comparable to a lock -in effect to solutions. LSCM is an early us er o f analytical methods
in certain processes and these solutions are trusted with hesitance to use other, al legedly
more advanced, methods. Thus, the culture is less da ta-driven relative to newer Ana lytics
approac hes and the issue is one of change manage ment.
Respondents experie nced the people in LSCM, relative to other dom ains, as less
imaginative in the use of data, having a lower d egree of experience in working with data
in comparison, and a higher need for explan ation – having less p rior knowledge.
Considering the range of activities in LSCM, the domain has an appare nt demand for

67
workforce without the requirement for a formal education in statis tics or higher
mathematics, while m embers of this workforce, on the condition of showing a satisfactory
performa nce and due to their “floor experience” (Rivera et al., 2016) , can rise to
management positions. H owever, people in LS CM are p erceived as int erested (and proud)
in improving their processes and finding soluti ons for their problems leading to the
flexibility to test several solut ions with a ha nds-on mentality. Thus, interviewee s
observed two outcomes o f this: if a solution has b een found to which people have be come
accustomed to, they are harder to convince to change course. otherwise, the y are open to
new solution attempts including Analytics, but the problem to be solved is resultingly
intense.
Regarding the state of progression, LSCM is perceived to occupy a stable midfield
position. In contrast, other domains are perceived as more volatil e – sometimes leading,
sometimes trailing. Respondents report to execute Analytics initi atives now in LSCM,
they have executed decades ago in domains like banking and telecommunications. This
curre nt state was reflected to be caused by missing data and t echnology which is now
available and can give LSCM a momentous potenti al to catch up with some orga nizations
already exploiting the potential. Howe ver, thi s pote ntial requires int erest or pain point s to
become e xploited.
3.4.2.2 Specifics in intervening conditions
In accordance with the foundational idea of LSCM of creating a conjunction betwee n
differe nt actors to transform raw material a nd distribute resulting products to consumers,
LSCM organizations have a substantial number of links to customers, supp liers, service
providers, other business units and other partners. Thus, LS CM constantly crosses
internal and external fu nctional boundaries on physical processes and would greatly
benefit from doing so an Ana lytics initiatives in a more natural wa y a s compared to other
domains (e.g., new bu siness models betwee n wearable technology providers and
insurance o rganizations). However, issues arise from data collection or d istribution of
Analytics Solutions cros sing functional bound aries. First, due to global dist ribution of
partners and organizational distance, a different need for c ollecting or e xchanging da ta is
perce ived or resulting transparency is feared as lo ss of power and in fluence, even in the
same organization. Sec ond, cultures differ in a ttitudes towa rds collec ting an d exchanging
data. Third, technological infrastructure and systems differ complicating data exchange.
The organizational distance increases further wit h requests for d ata excha nge cascading

68
to organizations with indirect business relationships (partners of partn ers). In reverse,
insights from Analytics solutions might be necessary for partners leading to deployment
across functional boundaries . This increases scalabilit y and adaptability r equirements of
the solution, which increase development time and r educe the interest of solution
sponsors unwill ing to p ay for ben efits outsi de the ir area of responsibility. Lastly, due to
limited contract duration, exchange of p artners and changing customer pr eferences, the
Supply Chain network is in constant motion such that cross functional Analytics may
have a short durability.
In contrast, interviewee s did not observe demanding re quirements in terms of d ata
security in LSCM, since for most use cases organizational assets and processes are
analyze d as opposed to individuals. Of course, customer preferences analyzed for demand
prediction entail privacy concerns, but such concerns a re far mor e regular in other
domains.
This study further speci fically inquired data quality iss ues, since scholars indi cated the
considerable im pact of human data collection errors (Wang et al., 2014 ). This has been
confirmed by some int erviewe es but was e valuated as minor component of the data
quality issue and its eff ect comparable to any domains. Further, data collection is
increasingly becoming automated such that this impact is era sed in the long run.
In conformance to the crossing of organizational boundaries, the heteroge neity of data is
natural to LS CM as well, coming from diverse business functions and partners. In LSCM,
this binds resource s for cr eating interfaces such that interface s are created to pa rtners with
reasona ble importance and longer expected partner ship lifetime. P ut differently, the effort
is not invested for every partne r hindering potentially interesting initiatives.
Finally, LSCM is a favo rable candidate to use mobi le sensor d ata and has an affinity for
using it from mob ile assets (e.g., ships, trucks, airplane s, trains, eleva tors, manufac turing
machines) and shipments (e.g., containers, packa ges, work- in -process). This innovative
technology represents a paradigm shift in LSCM from collecting event -based data at
stationary points to a constant monitoring, which p rovides value by reduc ed re action time
on incidents. The integration of I oT data is complex and cr eates l arge effort in wide sc ale
implementations but is already technological ly manageable. Hence, organizations are still
pioneering with th e te chnology such as a few LSC M organizations that start to monitor
and control their, ideally, permanently movi ng goods and assets such as in real-time status
visualization. However, organizations struggle wi th ini tiatives to extract higher forms o f

69
insights and few attempt more complex use cases like ETA -Prognosis, (dynamic) route
optimization, and incident-based product alloc ation or product reordering. Other domains
certainly ha ve use cases for this technology, which are, however, l ess apparent.
3.4.2.3 Specifics in actions
Since it is strongly related to the use cases, LSCM shows clear domain -s pecifics in the
differe ntiating characteristics. Regarding descriptive Analytics, LSCM shows an above -
avera ge demand for aggregated data from widely dispersed dat a sources , i ncluding IoT,
in real-time such that operational pro cesses can b e fine-tun ed and adjust ed based on th e
most appropriate d ecision to even complex issues , if nec essary. LSCM opera tions have
been stre amlined and us ually include few buffers, which demand p recise real -time data
to react to short term inc idents and ch anges. Res ultingly, current Descriptive Analytics
problems in LSCM display high technical complexit y, while some remain to ha ve aspir ed
solutions but not achieved them.
Regarding Predictive Analytics, LSCM was indica ted to trail behind other domains.
While scholars (Waller and Fawcett, 2013) have emphasized the potential of use ca ses
such as forecasting of de mand, delivery time or c ustomer behavior, interviewees barely
experienced these use c ases from LSCM. They observed that these us e c ases are either
on the long-term agenda due to missing data or are input s for Prescriptive Analytics,
whereby th e developmen t focus is on the Pr escrip tive part with acceptance for standard
solutions for the Predictive pa rt (e.g., pre dictive maintenance of assets focused on
resource efficient repairs scheduled into operations).
For the Prescriptive Ana lytics part, interviewees perceive an extraordinaril y position of
LSCM, since there is a natural association between Prescriptive Analytics methods and
LSCM opti mization problems, which “ are so beau tifully tangible ”. LSCM has complex
planning problems of go ods and assets to be allocated or moved through the network
against its capacities. However, it was also observed that these problems are solved with
standard features of som e software, which are not further individualized a nd leave high
potentials for improvement. Interviewees described further aspects of complexity. First,
LSCM is eager to exploit Prescriptive Analytics solutions for identification of alternatives
and impact of what-if scenarios to develop superior reac tions in beforehand, including
dynamic adjustments of operations, which were already initiated according to the
previously optimal solutions, to sit uational changes with as little effort as possible.
Second, LSCM problems tend to b e mor e complex due to c haracteristics of problems and

70
numerous restrictions, w hich additionally change along the supply chain. As scholars
noted, the idea of holistically optimi zed efficie nt n etworks leads to opti mization problems
in LSCM getting very large very fast (Blackburn et al., 2015).
Considering the mainte nance o f An alytics solutions, s ome domains experience an
extensive need for adjust ments and veri fication due to f ast degrading mo del quality or
high impact of small degradation. In cont rast, domains like LS CM with high efforts for
data collection or deployment of updates tend to maintain solutions less frequently .
LSCM was perce ived by int erviewe es to have low need for maintenance, favoring the
complete replacement of solutions in the long run.
3.4.2.4 Specifics in consequences
Interviewees indicated less demands regarding accuracy from LSCM. They have
observed that certain dec ision -making processes a re often well-supported by tendencies.
LSCM was observe d to f ocus particularly on the e fficienc y objective, what overlaps with
the extensive development of tools to incre ase efficiency (e.g., le an, c ontinuous
improvement). Respondents emphasize d that results from Analytics solution in LSC M
usually lead to decisions on physical operation s, o f which th e resource consumptions is
supposed to be minimized.
3.5 Conclusion and directions for further Research
Researc h on Analytics is often li mited to one do main, while it is a t ransferable tool that
can benefit from cross-domain development e fforts. To identify promisi ng aspects of
Analytics to cooperate research on, characteristics to set domain -specific and independent
issues apart are necessary but have not yet been provided by resea rch. This study has
investigated these characteristics and identified specifications of the LSCM domain based
on Grounded Theory. The derived map displ ays a theoretica l model of character istics
potentially differentiating domains principally, contemporary or have in the past. This
map displays antecedents influencing procedure and success o f Analytics initi atives and
can guide Analysts and manage rs for prioritization of issues.
3.5.1 Theoretical Implications
Relating to the purpose of this research, the mai n contribution are th e ch arac teristics of
Analytics initiatives, their connection – th eir mapping – and their us e to differentiate
LSCM from other domains executing Analytics initiatives. The map provided by this
research distinguishes the characteristics in diffe rent conditions, ac tions and

71
consequences. The mapping of characteristics provides explanatory value on differe nces
of Analytics i nit iatives’ success and performance far beyond th e Analytics method and
approac hes itself.
This research adds to the limi ted literature on the e ffect of the dom ain on An alytics
initiatives and provides an extensive overvi ew on effects contributing to procedure,
success, a nd users attitude towards it , and therefor e characterize a n initiative. These
character istics can be used for further quantitative research on issues of Analytics.
Concerning the LSC M literature, the theoretical model emerging from thi s research
provides antecedents of Supply Chain Analytics. It emphasizes the potential of the LSCM
domain to advanc e in Analytics due to re cent technological progr ess enabling further use
cases which should be su pported and monitored by research efforts. In p articular, r esearch
is needed on the exploitation of IoT data and the individualization of pre scriptive
Analytics solutions. For both, research is required to simplify the adoption of the results
for organizations. Further research is required to fac ilitate change management towards
more advanced a nalytical methods and presentation of benefits from Analytics.
This rese arch also hig hlights organizations coping with issues far off from the
consideration of research. Easily said recommenda tions to advance in Analytics, such as
standardiza tion and inve stments in IT, pose maj or challenges for organizations with
implications and iss ues unconsidere d by r esearch. Thus, by highlighting the complexity
of Analytics with this research embodied in th e variety of mapped charac teristics,
research sha ll be cautioned not to bypass the practitioners needs.
Concluding, this study provides a novel app roach to understand the execution and success
of Analytics initi atives and provides a multitude of new areas dem anding deeper
investigation and further research. Thus, this r esearch makes a valuabl e contribution to
the LSCM and Analytics literature .
3.5.2 Managerial Implications
This research accumulates a vast number of recomm ended actions and behavior for
managers executing Ana lytics initiatives. Before starting an ini tiative, managers should
identify technical and organizational challenges and prerequisites on the map of
character istics to avoid later iss ues. Managers shoul d further avoid hype -triggered
in itiatives but rather create well-thought ini tiatives comprised of a valuabl e problem to
be solved, a potential us er meaningfully contributi ng to the solution’s de velopment, a

72
sense for th e user story of the solut ion providing implications for the dep loymen t, and
maintenance n eeds for long -term performance persistence. To gain the users help, trust
and willingness to use the solut ion, managers must crea te visi bility of the ini tiative’s
value. Users must make sure to state their needed degr ee of a ccuracy or q uality of
solutions in orde r to induce the right eff ort. Based on the map of charac teristics provided
by this resea rch, managers can g rasp the big picture of an initiative and understand
success f actors, potential hazards, and key areas t o monitor such tha t corrective actions
can be taken.
While these implications are verbalized towards the manager executing the initiative,
there are c haracteristics which are hard for him to re ach and g et information about. Thus,
any member of an initiati ve’s project team i s enc ouraged for awarene ss of charac teristics
on the map and to point out potential fallacies. This emphasizes the necessit y of domain
knowledge in Analytics initiatives and the immediacy to assure knowledge exchange
between Analytics and domain experts.
In addition, thi s research ra ises a ttention to the complexity of Ana lytics, which ca nnot be
mastered by “hiring so me data scientists”. Further, while Analytics initi atives require
short organiza tional distance between Analytics experts and application domai ns of
solutions, they may not require collecting a ll data from partners if Analytics s olutions c an
be collected instead. Collaboration of thi s kind, and the sharing of own Analytics results
with partners might induce benefits such as the pa rtners recognizing the val ue of sharing
such information.
Finally, challenges and issues reappear across d omains since many organizations are
curre ntly working on sim ilar topics. Managers might consider innov ation collaborations
and mutual assistance on Analytics across domains with non-competing o rganizations,
which can also induc e n ew use cases. In particular, LSCM managers sh ould explore
collaborative use cases beyond op erational ef ficiency, which could facilitate new
business models and new sources of reve nue.
3.5.3 Future rese arch and Lim itations
This research provides p otential for future rese arch to validate the model – the map of
character istics – with quantitative methods to get more a ccurate insights on the domains’
conditions. In accordance to that, other dom ai ns could be investigated for their
specifica tions of the characteristics.

73
Further res earch d emands arise from the specific characteristics. For example, the need
for ac curacy in Analytics models and algorithms and factors influencing thi s need could
be studied further for break-even points of investment versus utility. This rese arch could
help managers to make better decisions by avoiding too high acc uracy without utility
from it but immense re source consumption or, in reve rse, too little accurac y with seri ous
consequences. Further, i f hype and p ain points release budget in larger organizations,
research is neede d on ho w to support organizations with limited budget such as small and
medium sized orga nizations. If these organizations perish due to their limited investment
potential, competition is sustainably altered. Additi onal re search potential lies in
overcoming the barriers such as c hanging to a data-driven cultur e, non -integrated I T
landscape, or unwillingness for da ta exchange between partners.
Further, thi s research has limitations. The deployed method of semi-structured interviews
results in the theoretical model being subject to the individual experience of the
interviewee s and the initiatives they indi vidually conducted and took part i n. While this
study has been limited t o the perception of s aturation and its re sults should thus be
genera lizable, a larger sample size could allow stronger conclusions. Th e diversity of
interviewee s could furt her be increa sed in two manners. First, the study included
in terviewe es f rom Germany and the USA. While the chara cteristics are e xpected to be
similar globally, interviewees from mor e countries could become invol ved. Second, this
study intentionally covers an informed outside vie w on severa l domains by inquiring Dat a
Analytics Companies but thus excludes the domain insight view, which experts avoid to
share due to inability to compare their domain to others.

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Accepted version . Published as: Herden , T.T. , Bunzel, S. (2018). Arch etypes of Supply Chain Analy tics
Initiatives — An Explor atory Study. Logistics, 2(2), 10 . https://doi.o rg/10.3390 /logistics2020010
(pub lished by MDPI, CC BY 4 .0)
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4 Archetypes of Supply Chain Analytics Initiatives – an exploratory study
While Big Data and Analyt ics are arguably rising stars of competitive adv antage, their
application is often presented and investigated as an overa ll approach. A plethora of
methods and technologies combined with a variety of objectives creates a barrier for
managers to decide how to ac t, while researchers investigating the impact of Analytics
oftentimes neglect thi s complexity when generalizing their results. B ased on a cluster
analysis applied to 46 case studies of Supply Chain Analytics (SCA) we propose 6
arche types of Initiatives in S CA to provide orientation for managers as means to
overcome b arriers and build competitive advantage. Further, the de rived archetypes
present a distinction of SCA for researchers seeking to investigate the effects of SCA on
organizationa l performan ce.
4.1 Introduction
Even b efore data got their mainstream reputation of being the “new oil” of the 21 st century
predestined to shap e th e digital economy (Keen, 2012), the potential competitive
advantage s through data ana lytics had already be en recognized (Davenport and Harr is,
2007). To rem ain with this a nalogy, data ana lytics re presents the refine ry process turning
raw d ata into competitive stre ngth. Analytics in itself is not a leading-edge invention, but
increased attention was recently triggered by d evelopments in information technology
(IT) providing new acce ss to data, organizations’ need for better and f aster decision-
making a nd rece nt big data and machine learning tools enabling new levels of insight for
decision make rs (Cao et al., 2015). The fie ld of Logistics and Supply Chai n Ma nagement
(LSCM), has been identified as one early adopt er and a long -term use r of Analytics
(Davenport, 2009) . LSCM has been employing operations research a pproache s for
decades und uses pu rpose-specific analytics tools for very particular probl ems. As it is
concerned with e ffectively integrating suppliers, manufacturers, warehouses, and stores,
such that mercha ndise wi ll be produced and distributed to the customer with a satisfying
service level and with m inimal costs in the right manner concerning time, location and
quantity (Simchi-Levi et al., 2003), the necessity for analytical approaches to achieve
these efficiency goals is inevitable. A re cent industry re port underlines the long-term data
affinity of the LSCM sector from a practical point of view and the potential of logistics
operations generating the amounts of data needed to create value by using Analytics

76
(Jeske et al., 2013). Ho wever, the report emphasizes untapped potential in improving
operational efficien cy, customer experience or crea ting ne w business models in the
sector. The fit of LSCM and Analytics is fa vorable since the satisfaction of customer
needs and requirements is a leading theme in LSCM (Christopher, 2011, p. 12) and the
meaningful insight provided by Analytics is used to ensure rules and workflows
strengthening satisfac tion of needs and requirements (Bose, 2009).
Scholars and practitioner s alike have provided evidence that advantages to performance
can be achieved from the domain specific use of Analytics, which was termed “Supply
Chain Analytics” (Holsapple e t al., 2014; Souza, 2014). I n re search, several authors have
investigated the effects of Supply Chain Analyt ics. Information syst em supported
Analytics capabilities have been indi cated to im prove LSCM performance (2010) and
Analytics tends to have a posi tive impact on LSCM performance with high dependen cy
on the fit of Ana lytics investment and LSCM process maturity (2012). The positive effec t
of Analytics on LS CM was further suggest ed as context contingent, especially on
planning processes (2014). Schoenherr and Speier-Pero (2015) provide a wide variety of
perce ived benefits inclu ding improved supply chain eff iciency a nd decreased supply
chain c osts. Furthermore, seve ral scholar s call for more research on the topic (Waller and
Fawcett, 2013; Wang et al., 2016).
On the practitioners’ side, indus try reports ha ve shown high expectati ons towards
Analytics in LSCM, espec ially for reducing inventory, risk and improving batch sizes.
Among others, better customer service, highe r efficiency and faster reaction to supply
chain issues ba sed on investments in Ana lytics have be en reported (Pearson et a l., 2014) .
Furthermore, the development of more custom er-oriented value chains with lower
logistics costs due to the rise of data and Analytics has been suggested (Opher et al.,
2016). However, investments are somewhat reluctant (Sc hmidt et al., 2015) and industry
reports show that only a few firms achieve excellent performance in Analytics concerning
LSCM (Ma rchese and Dollar, 2015) . The majorit y of firms are lagging or s truggling wit h
Analytics in LSCM (Thieullent et a l., 2016) with managers r eporting missing experie nce
and lack of knowledge on how to apply Analytics (Ransbotha m et al., 2016).
In the studies summ arized above, Analytics is customarily considered as one overall
concept while making conclusions on it or deriving potential value and utility although
single examples with ind ividual issues and div erse analytical techniques are considered.
Especially single ex amples a re used to highl ight Analytics providing bene fits (Trkman et

77
al., 2010) or savings (Thieullent et al., 2016) thereby projec ting the exemplary benefits
to Analytics as a general concept or overall approach. What remains unknown are the
differe nt out comes of dif fere nt approaches in r elation to their intentions and execution.
Analytics is usually not subdivided further although it prese nts a wide fi eld with different
objectives, orientations, and perspectives without a unified definition (Holsapple et al.,
2014). I n our view, it is too wide of a field to assume that all reported effects can e qually
be applie d on different Supply Cha in Analytics I nitiatives, a ll I nitiatives having the same
potential of providing value for an orga nization or all barriers a ppearing could be
overcome in a single manner. The sole attempt to subdivide Supply Chain Analytics in
extant res earch can be found in the f ramework fo r Ana lytics applications in LSCM (Hahn
and Packowski, 2015), w hich focuses on of f-the-shelf IT Systems and therefore ignores
important aspects of An alytics as well as Initiati ves which ar e not system-specific. To
derive more sophisti cated and re liable res earch conclusions on the eff ects of Analytics on
LSCM, a distinction of Supply C hain Analytics approaches is needed. We propose a
distinction of how organizations apply Supply C hain Analytics, by using clustering on 46
case studi es on Supply Chain Analytics Initiatives conside ring intended problem to be
solved, execution, techniques, and the resulting Ana lytics Solut ion. Thus, this research
investigates patterns in t he activities of o rganizations applying Analytics to business
problems in LSCM and explores their endeavors and motivation to form archetypes of
Initiatives with e xclusive characteristics. The outcome of the Initiatives as well as
alignment of outcome and intention is out of scope for research. Regarding MacInnis
Framework of conceptual contributions, the goal of this study is diffe rentiation
(MacI nnis, 2011). Thus, we will indicate how the i dentified ar chetypes are different, why
this differentiation matte rs, and how they can b e used further. The study is based on
publications about manufacturing firms, re tailers and logistics servic e providers applying
Analytics in LSCM. The research questions therefore states: How can Supply Chain
Analytics Initiatives be distinguished?
Th e obtained archetypes can provide guidance to managers for their individual issues,
and points of references of other organizations’ previous activities, and thus, reduces
barriers to adopt Supply Chain Analytics in their own organization. The archetypes
repre sent types that a re designed to be most different fr om eac h other to support lea rning
of manag ers and students about S upply Chain Analytics. However, the combination of
character istics o f different arc hetypes in the creation pha se of a n ew In itiative in a n

78
organization is not relegated but rather encourag ed with an individual and specific goal
and appro ach to be desig ned by the executing manager. For researchers, t he archetype s
form a framework to investigate the diff erent effects of varying approaches.
The remainder of the art icle is structured as follows: Section 2 provides a theoretical
background with the objective to explain the cha racteristics chosen to form the
arche types. Section 3 presents the methodology on how archetypes are formed using
cluster analysis. Section 4 explains the suggested archetypes and discusses their impact.
Section 5 concludes the article a nd section 6 provides final remarks.
4.2 Theoretical Background
In this section, we will summarize Analytics, Supply Chain Ana lytics a nd characteristics
of Supply Chain Analytics Initiatives.
4.2.1 Analytics
Due to its novelty and evolving nature, a wide var iety of definitions of Analytics exists.
Holsapple et al. (2014, p. 134) reviewed many of t hem to develop a collective definition
stating that Analytics is “conce rned with evidence -based problem r ecognition and solvi ng
that happen withi n the context of business situations”. This definition highlights two
specific asp ects of An alytics. The first aspect, problem recognition, indicates the
experimental p art of Analytics to achieve a go al which is uncertain and unclear in th e
beginning requiring furth er exploration (Viaene an d Bunder, 2011) , and thus identifying
what the actual problem is. The second aspect, problem solving , indicates that the value
of Analytics is solely provided if a model or application is deployed and used (Viaene
and Bunder, 2011). This aspect of Analytics is emphasized prominently in the literature,
often specified as makin g decisions and taking actions (e.g., Barton and Court, 2012;
Bose, 2009; Chen et al., 2 012; Davenport and Harri s, 2007) . Both aspects establish a clear
distinction from data aggregation Initiatives like dashboards and reports.
Davenport and Harris (2 007) pres ented the benef its of applying Analytics to im prove
internal proce sses or a n organization’s comp etiti ve position. They i llustrated that
achieving success with Analytics is not based on deploying software but rather on three
categories of factors: organiza tional, hum an and te chnological capabilities.
Organiza tional capabilities consider analytical objectives and processes, h uman
capabilities consider skil ls, sponsorship and cu lture, and technological capa bilities
consider data availability and Analytics a rchitec ture. While the mod els and software are

79
often in the focus of researc h in Analytics due to appare nt pre sentation of insight into the
specific oppo rtunities of Analytics, scholars hig hlight all three stated c apabilities as
critical to develop and su ccessfully use mod els and software (Bose, 2009; S anders, 2014).
The models and softw are used in Analytics a re commonly distingui shed as being
descriptive, predictive o r prescriptive (e.g., D as, 2014; Hahn and Packowski, 2015;
Holsapple et al., 2014). T he meaning of descriptive analytics is two fold. On the one h and,
it presents the summary of data to repo rt and monitor (Hahn and Packowski, 2015). On
the other hand, it describes root c ause a nalysis use d to gain insights about the underlying
phenomenon or process (Provost and Fawcett, 2013; Spiess et al., 2014). Predictive
analytics estimates unkn own values based on kn own examples. Prescript ive analytics
determines and, in some cases, subsequently automates a ctions or decision s to achieve an
objective given curre nt and projected data, requirements and constraints.
Due to recent technological advances, An alytics gained additional interest as “Big Data
Analytics”, referring to Analytics performed wit h Big Data, which has b een reported to
have a posi tive impact o n firm pe rformance (Akt er et al., 2016) . Big Data originates in
data manage ment issues with technology in the e arly 2000s due to high volume, velocity
or va riety of da ta (Laney, 2001) , whic h for med the original three “ V’s” of Big Da ta. Big
Data is mom entarily under frequent academic i nvestigation including an increase of
“V’s” (e .g., Akter et al., 2016; Fosso W amba et al., 2015; Sivarajah et a l., 2017)
considering seve ral issue s with Big Data beyond the aspects o f data m anagement and
without the need for advanced technologies like distributed storage and processing, li ke
Variability, Veracity, Visualization or Value. Ho wever, thr ee “V’s” is a leading theme
(Chen et al., 2012; Dutta and Bose, 2015; Sanders, 2014; Schoenherr and Speier -Pero,
2015; Spiess et al., 2014; Waller and Fawce tt, 2013; Wang et al., 2016).
4.2.2 Supply Chain Analytics
Similar to Analytics, no unified definition of Supply Chain Analytics (SCA) exists, while
rare ly one is propo sed. Souza (Souza , 2014, p. 595) describes it a s “ focus[ing] on the use
of information and analytical tools to make better decisions regarding material flo ws in
the supply chain”. Waller and Fawcett (Waller a nd Fawcett, 2013, p. 79) propose a
definition while describing the field as [L] SCM data science: “ […] is the application of
quantitative and qualitative methods from a variety of disciplines in combination with
[L]SCM theory to solve relevant [L]SCM problems and predict outcomes, taking into
account data quality and availability issues.” Incorporating aspects of both descriptions

80
and the definition of An alytics (Holsapple et al., 2014), we p ropose to d efine SCA as
follows : SCA is concerned with evidence-based probl em recognition and s olving within
the context of logistics and supply chain management situations .
Consequentially, SCA is neither a single and clear step-by-step approach to solve supply
chain problems nor limited to certain tasks a nd processes in LSCM. Souza (2014 )
systemizes and distinguishes several techniques b y the type of Analytics and the SCOR
processes affected. The origin of the list of techniques is not explained and it is not
exhaustive. Furth ermore, another attempt on systemization results for an investigation on
in -memory technology u sed in LS CM by grouping in -memory softwa re applications for
LSCM and designing a framework for a nalytical applications (2015). By c onsidering the
type of Analytics applied, whether the con cept is data driven or model driven and
methodological requirements, off-the-shelf software applications wi th analytical
capabilities used for LS CM functionalities were grouped into monitor-and-navigate,
sense-and-respond, p redict-and-act, and plan-and-optimize. However, this categor ization
ignor es objectives, organizational aspects and human aspects. Finally, examples of
potential applications of Analytics in LSCM were summarized from the perspectives of
the user and the tasks ( 2013). In summ ary, s cholars hav e str etched a wide range o f
applications of SC A with various use cases for different functionalities and users,
providing evidence that SCA is too complex to evaluate its impact as a general concept.
The ge neralization has fu rther im pact on managers by c reating barrier s, which wewant to
address with this study. Thus, this research focuses on barriers related to a missing
understanding of how t o apply SCA on individual problems of an organization and
substantiate relevant SCA Initiatives. Sanders (2014) provides an extensi ve overview on
barrier s of Analytics in th e context o f LS CM and presents s everal barriers of which the
following a re related to the interest of this researc h. First, managers, espe cially in
leadership positi ons, may not see the value provided by Analytics resulting in missing
vision, understa nding of the full capacity and how to change the organiz ation to apply
Analytics successfully. Second, so called analysis paralysis hinders organizations from
applying Analytics because they cannot handle the overwhelming oppo rtunities, the
speed of technologic al change what r esults in the inability to define a starting point .
Organiza tions may thus try to randomly analyze data for some eventual causation, some
business units may optimize their sub processes w ith little global effect or organizations
try to measure everything at once without understanding what to focus on. Third, instead

81
of experiencing a lack of data, many organizations drown in data. Besides technological
issues to handle these amounts of data, organizations do not know how to leverage the
existing data capa bility and how to base decisions on it.
4.2.3 Dismantling Supply Chain Analytics Initiatives
This subsection describes the ch aracteristics used to analy ze SCA Initiatives to form
arche types. W e identified 34 characteristics in an extensive review o f Analytics literature
which are presented in six categor ies. The charact eristics and categories ar e presented in
Figure 12. Drawing on Chae et al. (2014), we consider SC A I nitiatives as (one time)
projects aiming to achieve supply c hain objectives using evidence-based problem solvi ng
and recognition with a fo cus on inducing process r edesign, tool development or long -term
process changes like a utomation or continuing decisi on support.
First, the reasoning behind a ny Initiative should be a sho rtcoming in a supply chain
objective (SCO). Either because there is a defi ciency in comparison to the theoretic
potential or because higher performance is aspired. I n the literature, seve ral frameworks
of performance dimensions indi cating supply chain objectives ar e propos ed (Ambe, 2013;
Bowersox et al., 2007; Chan, 2003; Gunasekaran et al., 2004) without one being
unanimously accepted. Several operational metrics reappear in most frameworks we
investigated, but the categoriza tion differs tremendously. The following objec tives have
been elaborated based on a review of th ese frameworks: cost (SCO1), qu ality (SCO2),
time (SC O3), flexibility ( SCO4), sust ainability (SCO5), innovativeness (S CO6), customer
relationship (SCO7) and supplier reliability (SCO8).
Second, we r eturn to t he con cept of core capabilities of an analytics competitor:
organization, humans, and technology. The organi zational aspects can be represented by
the analytics objec tive (AO) of an analytics I nitiative. In ac cordance with Manyinka et al.
(2011) and Holsapple et al. (2014) we identified six analytics obje ctives. Based on
evidence-base d approaches these include th e creation of trans parency by d emocratizing
data (AO1), th e identification of root causes by experimentation (AO2), the evaluation
of busi ness performance and environment (e.g., efficiency o r risk assessment) (AO3) , the
segmentation of populations (including products and services) (AO4), the support and
replaceme nt of human d ecision-making (AO5), and the development and innovation of
new sources of revenues (e.g., business mod els, products or services) (AO6). In a given
Initiative, the fulfilment of one objective can be necessary to pursue a consec utive

82
objective. For example, a transparent business process may be necessary for further
analytics approache s leading t o supported decision-making.
Third, going forward with analytics capabilities, the human involvement (HUM) in a
specific Initiative c an be incorporated by distinguishing the business functions bringing
expertise into the Initiative. Considering Davenport and H arris (2007), Bose (2009) and
Dietrich et al. (2015) w e identified several specific roles in analytics Initiati ves (e.g.,
severa l roles from providing acce ss to data to the data management as w ell as business
function from user to sponsor of the Initiative) which can both be internal to the
organization or externally contracted. We aggregated the roles leading to three groups
and six roles: internal analytics expert (HUM1), external analytics exp ert (HUM2),
internal IT expert (HUM3) , ex ternal IT expe rt (HUM4), internal business process expert
(HUM5), external business process expert (HUM6). As we hav e seen i n our analysis,
external and inte rnal expertise is not mutually exclusive. Depending on the comple xity of
the Initiative, organizations combine available expertise in various form s to achieve
success.
Fourth, for the technological (TEC) aspects and final capabilities the infrastructure can
be a major bar rier (Sanders, 2014). However, Davenport and Harris (Davenport and
Harris, 2007) direct the focus on tools and analyti cs arc hitecture. They identify small and

S u pp ly Ch a in O b j e c tiv e
(S CO1 ) Co s t
(S CO2 ) Qu a l i ty
(S CO3 ) Ti m e
(S CO4 ) F l e x i b i li ty
(S CO5 ) S u s tai n ab i l i ty
(S CO6 ) In n o v a ti v e n e ss
(S CO7 ) Cu sto m e r re l a ti o n sh i p
(S CO8 ) S u p p l i e r re l i a b i l i ty & ri s k
Ana ly tic s O bj e ctiv e
(AO 1 ) Tra n sp a re n cy
(AO 2 ) Ro o t c a u se i d en ti fi c a ti o n
(AO 3 ) Ev a l u a ti o n o f p e rfo rm a n c e a n d
e n v i ro n m e n t
(AO 4 ) S e g m e n tati o n
(AO 5 ) S u p p o rt o f h u m a n d ec i s i o n -m a k i n g
(AO 6 ) N e w s o u rc e o f re v e n u e
H u m a n in v o lv e m en t
(HU M 1 ) In te rn a l A n a l y ti c s e x p e rts
(HU M 2 ) Ex te rn al A n a l y t i c s e x p erts
(HU M 3 ) In te rn a l IT e x p e rts
(HU M 4 ) Ex te rn al IT e x p e rts
(HU M 5 ) In te rn a l p ro c e s s e x p e rts
(HU M 6 ) Ex te rn al p ro c e ss e x p e rts
Te chn o lo g y
(TE C1 ) Sp re a d sh e e t s o ftw a re
(TE C2 ) St a ti s ti c a l so ft wa re
(TE C3 ) Sp e c i fi c a l g o ri th m s
(TE C4 ) P u rp o s e-b u i l t d ata s to ra g e
(TE C5 ) N o n -p u rp o se -b u i l t d a ta s to ra g e
(TE C6 ) V i rtu a l i z a ti o n
Ty pe o f An a ly tic s
(TA1 ) De s c ri p t i v e A n a l y ti c s
(TA2 ) P red i c ti v e An a l y t i c s
(TA3 ) P re s c ri p t i v e A n a l y ti c s
Da ta M a n a g e m ent
(DA T1 ) H i g h V e l o c i ty
(DA T2 ) H i g h V o l u m e
(DA T3 ) H i g h V a ri e ty
(DA T4 ) In t e rn a l d a ta
(DA T5 ) Ex te rn a l d a ta
S u p pl y Ch a in A n a ly tic s in itia tiv e

Figure 12 : Characteristics of a Supply Chain Analytics Initiative

83
short Initiatives done with spreadsheet software (TEC1) which can be applied by
analytical amateurs. Analytical professionals howe ver will either use statistical software
(TEC2) for experimental purpose or define and refine specific analytical al gorithms
(TEC3) building new and ofte n purpo se-specific tools. We distinguish the last two, since
this algorithm might be b ought f rom a third-p arty vendor. On the other han d, Davenport
and Ha rris (2007) discuss the importance of data st orage and a ccess. However, since their
initial work, the field has seen signi ficant developments. Opposed to s tatistical software
models gathering data f rom existing systems or sending it to a third -party to execute the
analytics methods, the classical mode is a newly purpose-built data sto rage (TEC4).
Recently, the conc ept of a non-purpose-built data store to gather data fr om (TEC5) is
emerging following the idea of creating a single storage for analytic al purposes to be
defined later. This concept is often called “Da ta La ke” (Fang, 2015). F inally, the c oncept
of virtualization, as prominently known due to cloud computing, allows acce ss to
analytical methods and re sults disconnected from the ac tual data infrastructure (TEC6),
e.g., with mobile devices (Kambatla et al., 2014).
Fifth, data management (DAT) for analytical p urposes can face serious challenges
demanding suppl ementary effort (Laney, 2001 ). As explained above, the big data concept
repre sents serious data m anagement challenges, which we have thus incor porated into the
evaluation. This includes a high velocity of data (DAT1) b eing analyzed or collected, a
high volume of data (D AT2) to be included in Analytics and a variety of data sources,
data structures and data semantics (DAT3 ). Further, besides internal data (DAT4),
managers a re supposed to be creative about the inclusion of external data (DAT5) sources
(Barton and Court, 2012).
Sixth, rec onsidering the works of Ha hn and Pa ckowski (2015), and Ho lsapple e t a l.
(2014), the type of analytics (TA) can be d escriptive (TA1), pr edictive (TA2),
prescriptive (TA3) or severa l of them at once due to chaining or combination .
As mentioned in the int roduction, the I nitiati ve’s outcome has been n eglected in the
analysis process. The outcome, which is presented in a perc entage or absolute value for
savings or improvements in a monetary value, ti me or qua ntity is highly de pendent o n the
individual case, organi zation and industry. Thus, it was not considered meaningful for the
derivation of archetypes. Additionally, this research aims to recognize what organizations
aspire, the intention and t he consequential execution in a qualitati ve manner. Quantitative
outcomes do not fit this aim. We further omitted firm size and organizational form, since

84
our intere st is in the I nitiati ves pre sented by single projects, whic h c an be a relative small
size compared to the size of the org anization du e to the int ention to solve a small problem.
No cha racteristic de scribed above is mutually e xclusive and some will co rrelate since the
presence of one characterist ic may likely demand the pr esence of another.
4.3 Methodology
To identify SCA I nitiati ve archetypes, we us ed the machine learning method of
clustering. Clustering is a descriptive or explorative data an alysis techniqu e which relies
on interpretation by the analyst based on insight int o the original data ( Kaufman and
Rousseeuw, 2005). This fits MacInnis (MacInnis, 2011) requirement to use analytical
reasoning for facilitating the aspired differentiation. Below, we pre sent the data
collection, ana lysis, and evaluation process.
4.3.1 Data Collection
Since this research considers case studies from organizations, research databases did not
provide a sufficient source. Based on the insight we gained from the publications
presented above we use d key words and synony ms of Analytics

1

as well as Analytics
Objective (se e section 4.2.3) in combination with LSCM

2

to conduct an ext ensive sea rch
via the google search engine (with customized search results deactivated). Besides case
studies fr om orga nizations, we identified several third-party websites, software a nd
solution vendors and organizations applying An alytics, as well as news websit es, expert
websites and blogs which we further used for snow ball sampling. Finally, w e approached
organizations for cases. In total, we id entified a shortlist of 49 I nitiatives with promisi ng
information richness to evaluate the ir c haracteristics.
4.3.2 Data Analysis
To identify archetypes, we looked at previous research similar to our inten t. [L]SCM
arche types aimed at pr oviding manage rs with understanding about o rganiza tional
adaptation and p erformance eva luation have been identified by non -hierarchica l
clustering on supply chain I T and o rganizational structure variables (2008). The variables
were collect ed via a sur vey including variables s uch as B2B e -commerce suppl y chain
integration, ERP applica tions, operational centralization as well as market and financial

1

(“Data Science ”, “Business Intelligen ce”, “Big Data ” or “Data Minin g”)

2

(“transpo rt*”, “operation manag ement”, “deliver*”, “value ch ain”, “warehous*”, “supplier”, “resource
plannin g”, “inventory ”, “material flow”, “prod uct handling”, “distribut*”, “sh ipping”)

85
performa nce. Supply chain integration arc hetypes were investigated to understa nd the
relationship between integration and performance and to provide parsimonious
descriptions useful for di scussion, research, and p edagogy as well as to r eveal insi ght into
the underlying structure (2010). S urvey based collection of variables of customer
integration, supplier integration, int ernal inte gration, business performance and
operational pe rformance and the data analysis with hierarc hical and subseque nt non -
hierarchical c lustering determined the archetypes. The authors brie fly describe five
arche types with three balance d integration archetypes and two customer -leaning
integration archetypes in different nuances. L SC M job type archetypes were deviated
from colle cted job descri ptions fr om a major e mployment we bsite to provid e suggesti ons
on how training and professional development should occur (2010). Text analysis was
used to mechanically code the job descriptions and hierarc hical cluster analysis using
Ward’ s method was ap plied to identify eight arche types. Concluding, clustering has
proven as research meth od in LSCM to identify archetypes with the method adapted to
the individual dataset. T hus, focusing on clustering as the method for our research is
supported by previous LSCM researc h.
We used the 34 characteristics of SCA Initiatives discussed above as binary measures to
systematically describe the found I nitiatives. Two researc hers coded the cases
independently. The cases were coded from the per spective of the analytics result’s final
user and with the supply chain objective fo cused on the value creation process. If a case
provided inconclusive evidence for a variable , the information was sou ght from the
organization or the case was rejected. Thus, three of the shortlisted case studies were
rejected. Both re searchers discussed the coding regularly to align the int erpre tation of the
variables. Afte r coding, each diffe rence was discussed and re solved by consensuses. The
researchers calculated Cohen’s Kappa as 0.65 ( Cohen, 1960), indicating a substantial
agree ment on the scale of Landis and Koch (1977).
As the collec ted data is binary, common methods to determine dissimilarities between
two objects such as computing the Euclidean or Manhattan distances cannot be employed.
To deal with thi s type of the data, the approach proposed by Kaufman & Rousseeuw
(2005) building on an a dapted version of the simi larity coefficie nt defi ned by Gower
(1971) was used to calculate the dissimilarity matrix. All variables were treat ed as
asymmetric binary as they did not represent the presence or absence of a characteristic

86
but the presence and non -prese nce due to missing evidence fo r p resence (Faith, 1983 ),
thus leading to ad aptions of the distan ce m easurement. We us ed the statistical software
‘R’ to perform the clus tering and its evaluation.
We decided for hierarchical clustering due to the advantage of visually inspecting the
agglomerations via a dendrogram. W e tested UPGMA , Ward’s method, compl ete linkage
and WPGMA and evaluated the dendrograms by outlier influence due to late
agglomerations of single obse rvations. The most promising method in our evaluation ha s
been W ard’s Method vi sualized in Figure 13. Dur ing the pro cess, w e ha d to omi t
(DAT_4), sin ce e very case re lied on internal data. As Hair et a l. (2010) point out, there is
no completely obje ctive way to d etermine the number of clusters and the final choice
remains to the rese archer. However, the res earcher shall be guided by his researc h
objective. Since we wa nt to create reasona ble clust ers while keeping them conceptionally
knowledgeable and insightful, we limi ted our considerations to a maxi mum of eight
clusters and d ecided for the best number of cluster s in that range b ased on several criteria:
The change in agglomeration distance s b etween merging clusters p eaks at the changes
from three to four clusters and f rom five to six clusters (non - consecutive i ntersec tions).
Based on the d endrogram, six clusters represent a reasonable cut-off. The Dunn index
(Dunn, 1973) is maximized for five clusters, while the index remains constant for six and
seven clusters. The Silho uette index (Rousse euw, 1987) suggests seven clu sters with six
clusters as se cond best and five clusters as thi rd best choice. As sho wn in Figure 14, the
differe nce betw een five and six cluste rs is mor e pronounced than that between six and
seven clusters. We d ecided for six clusters, since it is the visually most reas onable, once
amongst the best and o nce the second best considering the indexes. In the visual

Figure 13 : Dendrogram of Cluster Analysis with War d's method

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evaluation, we took into account the chan ge s in distance from five to six cluster or six to
seven not being consecutive intersections. The res ulting six clusters were i nterpreted by
considering the Initiatives in each cluster and by comparing the avera ges of variables
amongst clusters.
4.4 Results and Discussion
In thi s section, we describe the six found clusters by highlighting chara cteristics and
combinations of characteristics of the Initiatives in each cluster in comparison with the
Initiatives in the others. The findings therefore present the researchers’ inter pretation. All
Initiatives within a cluste r were then ana lyzed together to extra ct commonali ties. Clusters
have be en named to underline their major tr aits. The results are visuali zed in Figure 15
with key points for every cha racteristics category in the order presented in section 4.2.3.
4.4.1 Cluster 1 – Educating
The Initiatives in the Educating cluster focus on gathering data (sources) new to the
organization and proce ss, that are used a s more advanced input in the decision -making of
the LSCM process to improve output but will not lead to process redesign. This cluster
contains mainly fo recasting I nitiatives with the spe cific goal of providi ng the right
amount of goods withou t storing excess inventor y or having sto ck -outs s uch that the
consumer of goods is se rved best (e.g., the use of data on weather, income, h istorical sales
or regional mark eting campaigns to determine inventory allocation to individual stores).
Thus, the objective is to im prove process quality and customer relationship. The t ools
used in these Initiatives are dominantly predictive and aim to produce more precise input s
for decision-making in consecutive processes of inventory allocation. In so me I nitiatives,
no evidence on how the consecutive processes are further affected by the tool can be

Figure 14 : Cluster Evaluation (be st evaluated in grey)

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found. The report on one Initiative sp ecifically states that this is intended, sinc e
employees shall us e the output of the model – the prediction – and not q uestion it. The
focal organization usually buys a custom - built or customized advanced tool or system
extension developed by external Analytics experts , which includ es external data sou rces
new to the forecasting organization and combines it with internal data from existing
systems to integrate it with these systems.

Ed u c a tin g th e LS CM p ro c e s s b y
i n cl u d i n g n e w d ata (so u rc e s) a s p ro c e ss
i n p u t fo r i m p ro v e d p ro c e s s o u tp u t . ..
S CO to i m p ro v e q u a l i ty o f p ro c e ss o u tp u t
a n d s u b se q u e n t c u s to m e r re l a ti o n s h i p
AO by i m p ro v i n g h u m a n d ec i si o n su p p o rt
HUM wit h e x te rn al A n al y t i c s ex p e rts a n d
m i x ed IT a n d p ro c e ss te a m s , wh o
TEC i m p l e m e n t m o d e l s o r sp ec i fi c
a l g o ri th m s i n e x i sti n g s y stem s (e x i s ti n g
i n th e u n c h a n g e d p ro c e s s), and
DAT i n t ro d u c i n g (n e w ) e x t ern al d a ta
TA u s in g p red i c ti v e te c h n i q u e s
S CO to (i n si g h tfu l l y ) i m p ro v e q u a l i ty o f th e
p ro c e ss a n d i ts c o s ts
AO by i d en ti fy i n g (d ata -b a se d ) c a u s e s o f
q u a l i ty d efi c i e n ci e s a n d p ro v i d i n g
e v al u a ti o n m e a n s b as e d o n c a u se s
HUM wit h m i x ed tea m s o f e x p e rts, wh o
TEC u s e sta ti s ti c a l to o l s t o c re a te p u rp o s e -
b u i l t to o l s (attac h e d to e x i s ti n g sy ste m s
o r s tan d -al o n e ), by
DAT a n a l y z i n g i n t e rn a l d ata w i th h i g h
v o l u m e an d v a ri e ty
TA u s in g p red i c ti v e te c h n i q u e s
O b s e r v in g o f LS CM p ro c e ss c o n d i ti o n s
i n d i c a ti n g c a u se s fo r p ro c e s s d efi c i e n c i e s
b a s e d o n n e wl y g a i n ed p ro c e s s i n si g h t…
S CO to i m p ro v e v a ri o u s o b j e c ti v e s
AO by p ro v i d i n g (k n o w l e d g e -b as e d ) m e a n s
o f (a u t o m a ti c ) e v al u ati o n , tri g g e ri n g
d e c i s i o n s u p p o rt o n a l e rt
HUM wit h m i x ed tea m s o f e x p e rts, wh o
TEC c re a te p u rp o se -b u i l t to o l s (attac h ed to
e x i s ti n g sy ste m s o r s tan d -a l o n e ), f o r
DAT h i g h v el o c i ty d a ta a n al y s i s
TA u s in g d e sc ri p ti v e a n d p red i c ti v e
tec h n i q u e s
Ale r tin g LS CM p ro c e s s o wn e r
a u t o m a ti c a l l y o n i n d i c a to rs o f p re -
d e fi n ed c ri ti c a l co n d i ti o n s a n d e v e n t s…
S CO to c h a n g e a n d th u s i m p ro v e th e p ro c e ss ,
a n d i ts q u a l i ty a n d c re a te i n n o v a ti o n s
AO by p ro v i d i n g (d a ta - a v ai l a b i l i ty -b a s e d )
tran sp a re n c y a n d c a u se s to e n ab l e
(fo rm erl y u n a v a i l a b l e ) e v a l u ati o n a n d
d e c i s i o n s
HUM wit h (m o stl y ) i n tern a l e x p erts, wh o
TEC c re a te (sta n d-a l o n e ) p u rp o se -b u i l t to o l s
c o n n ec te d to d ata sto ra g e s w i th o u t
p re d e fi n ed p u rp o se (“ d a ta l a k e s” ), by
DAT a n al y z i n g d a ta w i th h i g h v e l o ci ty ,
v o l u m e an d v a ri e ty
TA u s in g d e sc ri p ti v e te c h n i q u e s
Adv a ncin g t h e LS CM p ro c e s s a nd /o r
b u si n e ss m o d e l b as e d o n n e wl y g ai n e d
p ro c e ss d a ta a v ai l a b i l i ty a n d i n si g h t…
S CO to i m p ro v e c o sts a n d re d u c e ti m e o f
a c ti o n s b as e d o n p ro c e s s i n n o v a ti o n s
AO by p ro v i d i n g h u m a n d ec i si o n s u p p o rt
HUM wit h m i x e d t e a m s o f e x p e rts b u t s tro n g
i n t e rn a l e x p e rti s e , wh o
TEC c re a te p u rp o se -b u i l t to o l s co m b i n e d
wi th n ew sto ra g e s y stem s , f o r
DAT a n al y z i n g d a ta w i th h i g h v e l o ci ty a n d
v o l u m e
TA u s in g p re sc ri p ti v e te c h n i q u e s
Re f in in g LS CM p ro c e s s b y fa s ter,
b ro a d e r, a n d m o re fre q u e n t g u i d a n c e fo r
wo rk e rs o r d ec i si o n m a k ers to a c t o n …
S CO to i m p ro v e co sts a n d q u a l i ty o f
p ro c e ss e s a n d a ss e ts
AO by i d en t i fy i n g c a u s e s
HUM wit h m i x e d tea m s b u t s tro n g e x ternal
An a l y t i c s ex p e rti s e , wh o
TEC a p p l y s tati sti c a l to o l s , f o r
DAT a n al y z i n g i n t e rn a l d ata
TA u s in g d es c ri p ti v e te c h n i q u e s
Inv e s tig a ti n g L S CM p ro c e s s a n d a ss e t
d e fi c i e n c i e s to i d e n ti fy c a u s e s a n d
e n ab l e c re a ti v i ty a n d e n g i n ee ri n g d es i g n
b a se d s o l u ti o n s e a rc h …
S C O – S u p p ly Ch ain Ob j ectiv e
AO – An a ly tic s Ob jectiv e
H U M – Hu m an in v o lv e m e n t
TEC – T ec h n o lo g y
DAT – Dat a M an a g em e n t
TA – T y p e of An a ly tics

Figure 15 : Proposed Supply Chain Analytics archetypes (no c hronology or sequence intended)

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4.4.2 Cluster 2 – Observing
The Initiatives in the Obse rving cluster c oncentrate heavily on predicting LSCM process
deficienc ies in the short -term or medium-term future with a newly developed tool
observing and monitoring the processes gain reac tion time to either prevent the
deficienc ies or enable counteraction. The predi ction is based on process conditions
indicating the definitely identified in the In it iative. Thus, observing indicates wa tching
with knowing wha t to pay a ttention to. The supply chain objectives are mixed but tend to
focus on cost reduction a nd quality im provement. The process is sought to be improved
by observing and avoidin g identified cause s of quality deficienc ies but not essentially by
changing or re designing the proc ess. The I nitiatives in C luster 2 de scribe a variety of data
experiments to im prove process a ccuracy and quali ty (e.g., identifying production quality
indicators that have to be monitored, e stimation of product weight based to package
weights to seque ntially use package we ight as qua lity indicator for correc t items , identify
influencing factors on pu nctuality of arrival to adj ust plans when factors ar e pres ent). In
a distinct proportion of Initiatives, the maintena nce process of an asset, machine or
vehicle, was under invest igation. The ac tors in the analytics team are mixed fr om internal
and external experts f or Analytics and IT. Process experts are usually in house. The
software used includes s preadsheets – the cluster contains the only I nitiati ve the authors
could identify using spreadsheet softw are (in combination with statistical softwa re) – and
statistical software but no specifically designed algorithm. The technique s used are
primarily predictive and analytically aimed at ant icipating the behavior and evaluating
the perf ormance of a pro cess as well a s understanding the ca uses of the process behavior.
Thus, they usually exploit patterns in the data opposed to process knowledge as in
Alerting Initiatives. Data is either stored in purpose-built data storages o r gathered from
existing systems. In addi tion, cloud computing was used in some Initiatives to provide
access to the automatically evaluated processes to facilitate monitoring . External data is
barely used, but internal data comes in high volum es and variety to create sophisticated
process insight tools.
4.4.3 Cluster 3 – Alerting
The Initiatives in Cluster 3 use sim ilar approaches as the Initiatives of Clust er 2. However,
the product of the Initiative diverg es with Clust er 3 Initiatives aiming to produce a support
system for process owne rs. These are supposed t o call for attention or alert in certain,
especially critical, pro cess conditions or ev ents, w hich are mostly predefine d rather than

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identified. Critical refers to negative process effects or possible loss of revenue. The on -
demand attention contributes to meet various suppl y chain objectives including cost
reduction, quality improvement, fl exibility increase o r customer r elationship
improvement. The LSCM proc ess is usually un touched, as opposed to Advancing or
Refining I nitiatives, while the monitoring task of the process is re duced from active
checking to passively get ting alerted on a ctions required ( e.g., by providing alerts when
delivery vehi cles do not progress on route as has been estimated whi ch demands
modification of routes, informing re ceivers or parallel deliveries with fa ster delivery
time). To achieve this, the Initiatives repeatedly describe teams of internal process exp erts
teaming up with mixed experts in analytics and IT . The need for external assistance in
these Initiatives may be attributed to the desired output , which is to develop a dedicated
software tool in all Initiatives in Cluster 3. These tools pe rform monitori ng tasks of a
specific process and recommend a ctions for i mprovements (e.g., to lower energy
consumption, to increase fle xibility, to reduce cost, to improve utilization of c apacity) as
well as reque st nee ded ac tions (e.g., change routes, change active supplier, maintain
machines, to adjust prices). Thus, high velocity of data analysis is in focus. The
organizations in some c ases used the tool to offer new se rvices to their custom ers. The
tools usually need their own purpose-built data s torage with virtualization technologies
commonly used for improved access. Further, they are likely to include external data
sources n ecessary for ri sk assessments of suppl iers, sources of delays on routes or
condition evaluation supported by additional manufacturer provided data. As opposed to
Observing I nitiatives, these Initia ti ves are commonly driven by process knowledge of
critical conditions and therefore focus on finding data-driven ways to a utomate what
process own ers were a ctively monitoring before. The Initiatives use and combine
descriptive and predictiv e techniques to summarize data for monitoring and e xtrapolating
future conditions.
4.4.4 Cluster 4 – Advancing
The Initiatives in Cluster 4 focus on advancing a process, or even the organization by
developing new busi ness models. Thus, as opposed to Observing Initiatives, which
optimize reacting on process conditions potentially leading to process quality deprivation,
or to Alerting Initiatives, in which known conditions require actions, these I nitiatives
usually introduce process change s in the form of adjusted process steps th at were
identified based on process data made available to crea te this transparency – the large

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scale data availability is often seen as major pr ogress and benefit with resulting tool
focused pro cess execution. This cluster unites Initiatives of organizations with a certain
maturity in Analytics. These usually bring interna l expertise into the Initiative from all
relevant a reas and only occasionally require external expertise. In particular, the
Initiatives aim to achi eve data ava ilability and transparency and subs equently unde rstand
the focal process ( e.g., by equipping assets with a v ariety with sensors and m obile devices,
collecting data, and ana lyzing data from similar assets together to deter mine the life cycle
process of a machine and its components or rout ines of transport vehicles performing
deliveries and consequentially creating a new form of predictive mainten ance contract
with customers). I n this context, “understanding” not only refers to the continuous
evaluation of processes, but also to the us e of descriptive techniques for causal analysis
to identify process parameter settings and combinations ca using loss es in proce ss quality,
and to provide a decision support to counterac t these losses. The I nitiatives integra te data
with high velocity, high volume and high varie ty. To ac hieve this, purpose - built software
tools are dev eloped. The results of the ana lysis lea d to tools for process monitoring
combined with decision support systems. These I nitiatives fu rther emphasize the
collection of data without predefined purpose, with prospective use of these centralized
data in future an alyses. T he tools and select data are regularly mad e available to customers
to create a c ompetitive advantage for their own products.
4.4.5 Cluster 5 – Refining
The Initiatives in cluster 5 aim to squeeze the last bit of untapped efficiency out of a
system by refining processes with assisting or rather guidance fun ctions for human
operatives to reduce cos ts and save ti me. Additionally, these Initiatives have a strong
focus on creating an innovative advantage ov er competition. To achieve thi s, presc riptive
techniques are used to determine the best cour se of ac tion – redesigning the process with
manifold interactions with the system to guide d ecision -making by human operatives .
This includes the transfer of routing algorithms to pickers and the stops of their carts in
distribution centers, augmenting delivery vehicle drivers with routin g algorithms
dynamically using real-time traffic conditions to re-optimize while the vehi cle is already
on the road or extending manufacturing pro cesses with real -time quality evaluation to
change the produ ction sequence. Thus, as opposed to the arche types above, the focus
diverges from understanding and monitoring a proc ess to continuously adjust (or refine)
it. To achieve the aspired goal, a high volume of high velocity data must be analyzed by

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purpose-built tools. The highly customized tool s as well as the systems developed to
execute these tools are d eveloped in -house with a combination of internal and external
expertise.
4.4.6 Cluster 6 – Investigating
The I nitiatives in cluster 6 are united by the inv estigation of c auses of deficiencies or
rather major pro cess flaws. Thus, the objective of these Initiatives is to increase proc ess
quality and re duce costs by identifying and mitiga ting the root causes of process flaws or
finding proxies to enable monitoring them (e.g., identify shelf replenishment flaws by
monitoring check-out p atterns in super markets to identify patterns of lost sales or
identifying critical sensor signals presenting factors causing quality issues in production
processes). The im portant aspect to distinguish this cluster from the others is the
consequences taken whe n a root cause is found. H andling the root causes in the I nitiatives
could not be done by automating decision-making or more sophi sticated moni toring.
Rather, the cause of proce ss reliability or deficien cy ha s to be handled by changes in ne w
product development, major process redesign or changes in materia ls demanding
crea tivity and engineering design. These Initiatives usually combine external Analytics
with mixed external and internal I T and process es expertise. Ide ntifying causalities is
supposed to start a solution search instead of automating evaluation or continuous
decision support, as compared to Observing , and Alerting or Refining Initiatives. The
results of the Initiatives are bas ed on statistical software with a focus on internally
available da ta. Purpose-buil t data storage systems are created.
4.4.7 Discussion on Archetypes
The clusters presented above present archetypical Initiatives of An alytics in LSCM. The
identification and interpretation of core charact eristics of clusters was conducted to
highlight the uniqueness of each archetype and present archetype diversity in int ended
problem to be solved, execution, techniques, and product. S ingle Initiatives forming the
clusters and therefore determining the a rchetypes differ in som e characteristics from the
arche type. Thus, n ew Initiatives may be crea ted with differences in single features but
with a c learer understand ing of archetypical feature c ombination. I n addition, while ther e
is some (expe cted) overlap of clusters, we consider it as new insight that e.g., the same
Analytical objec tive may be used to pursue different S upply Chain objectives.
Considering the identified archetypes, as well as t he characteristics formin g the clusters,
we observed th at the typ e of Analytics did not domi nate. While the Refining archetype

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consists solely of optimi zation Initiatives, optimization techniques could be found in the
Educating and Advising archetypes as well. P redictive techniques can be found in all
arche types, including the Refining archetype, since techniques were usually combined in
more sophisticated Analytics Initiatives. This holds true for descriptive Initiatives as well.
While the type of Analytics has b een our greatest concern, we additionally conducted an
analysis for dominating character istics by eval uating all characteristics across all
arche types in search for chara cteristics present in all Initiatives forming an a rchetype but
not pre sent in any other arc hetype. However, we c ould not find a ny characteristic
fulfilling this condition of domi nance. To extend our ana lysis of critical characteristics,
we considered whether t he supermajority (two-th irds) of Initiatives possessing a certain
character istic are given in any archetype. This c ondition was de fined as wea k dominanc e
for this rese arch. This condition was fulfilled by the features S CO8, TEC1 and TEC5.
However, these features have two, one, and three observed I nitiatives possessing the
character istic, r espectively. Therefore, we did n ot consider these features as critical.
Concluding, we are confident in our results not being dominated by one single
character istic.
When presenting these re sults to scholars, a major point of controve rsy has be en, whether
the arc hetypes present levels of Analytics maturity of an organiza tion which we reject
after careful consideration due to the following a spects: First, considering the given data,
one I nitiative does not reflect the whole organization but a business unit exe cuting the
Initiative. This is consis tent with research indicati ng Analytics should follow process
maturity (Oliveira et al., 2012) or rather additiona l Analytics Maturity should fit process
maturity (Trkman et al., 2010) which is therefore not necessarily leveled across the
organization. Second, or ganizations could ex ecut e Initiatives of lower maturity since it
may still provide benefits and Initiatives of higher maturity using e xternal support. Thus,
an Initiative is not a distinct confirmation o f an organizations capabilities. Third, in the
Initiatives considere d, tw o organizations have been observed twice and on e organization
three times. The Initiatives thus spre ad across archetypes with the seemingly more mature
Initiatives either in the s ame year or earlier. Fou rth, research has pointed out, that the
objective of an Analytics Initiative is oft en set with out the consideration of t he complexity
of the Analytics required (Viaene and Bunder, 2011) . While the objective guides the
Initiative, the necessary Analytics maturity may be determined during the execution and

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not before and thus not influence th e Initiative. H owever, we acknowledg e that the level
of internal expertise involved may indicate the business criticality.
Concerning an In itiati ve perspective, this actually opposes maturity models setting
standards for organization-wide implementation for highest maturity (Wu et al., 2016) or
considering strategic Initiati ves for highest maturity (Wang et al., 2016). T hese levels o f
maturity address the analytics culture of the organization (Davenport and Harris, 2007)
which may influence the spread of Initiatives but not dictate the choice of Initiatives.
Finally, we learned that, in order to achieve value with SCA, the solution does not have
to be an organization-wide expensiv e third-party tool. Small models build with R or SPSS
and visualized with Tableau can provide significant value already.
4.4.8 Discussion on overcoming barriers with arc hetypes
This researc h aspires to provide means to ove rcome barriers of applying Analytics to
LSCM related to a missing understanding. Considering Sanders (2014), we chos e and
summarized severa l barriers relevant for this research in section 4.2.2.
Lack of leadership is indicated to be caused by la ck of vision, lack of understanding of
the capability, and the lack of understanding how to lead change. Th e latter, also described
as creating a data -oriented culture (Kiron et al., 2012) or data-driven cultur e (McAfee and
Brynjolfsson, 2012), is considered a key competency for managers to tra nsfor m
organizations to sophisti cated Analytics capabilities and beyond the scope of this
research. The lack of vision is addre ssed by the core concepts of each archetype since
they are supposed to guide vision by providing points of referenc e to individualize, a dap t
and combine. Th e lack to understand the capabilities required to apply Analytics is
addressed by the characteristics of Analytics Initiatives emphasizing structure of and
resources needed for executing an Initiative. However, it has been sugg ested that t h e
existence of thes e capabi lities in an organization doesn’t guarantee the ability to bring it
to full use (2 014).
The barriers of lacking o bjectives are addressed by the generic objectives presented by
the two objective characteristics categories, a nd b y the specific objectives provided in the
arche type descriptions. This highlights to mana gers the necessity of de fining an objective
as compared to the “poking” for co rrelation as described by the notio n of analysis
paralysis. Defining an o bjective which An alytics should answer is a valuable starting
point (Lavalle et al., 2011). The archetypes further emphasize that Analyti cs Initiatives

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should not bedriven by the latest and most innovative technology but by technology fitting
the purpose of the identified objective s.
Further, as evident from the discussion above, with an orientation on objective -driv e n
SCA with subsequent choice of data, mana gers should not drown in da ta. The a rchetypes
further p resent th e opportunity of relying on exter nal guidance for choosing the necessary
data. I n addition, it is indica ted that using non-Big Da ta can still ac hieve benefits. This is
further und erlined by process models for Analytics Initiatives recommending data
collection to be a later step in the project (e.g., Dutta and Bose, 2015; Pr ovost and Fawcett,
2013). Even while d rowning in data, having the right data to successfully execute the
Analytics Initiative is not guaranteed.
4.5 Conclusion
In our research, we investi gated how SCA Initiatives can be distinguished. Literature
suggests reluctance of L SCM to invest in Analytics Initiatives caused a mongst other
reasons by man agers missi ng ideas in how to approach SCA. With our research, we
address thi s shortage by providing a distinction of Initiatives providing knowledge to
managers about typical approaches to use SCA to gain business value. Based on the
patterns emerging form a cluster analysis of 46 SCA I nitiatives w e p ropose six archetypes
that show c onsiderable differences in how organizations deploy SCA. I n the ana lysis, the
problem to be solved, execution, techniques, and resulting Ana lytics Solution of the
Initiative h ave been considered. I n detail, we examined ch aracteristics necessary to
execute an SCA I nitiative and the refore display a reas that h ave to b e taken into account
by managers designing new Initiatives. The characteristics a re aggr egated into the
following groups:
• Supply chain objective that shall be addresse d which represents the
problem or defic iency in the LSCM process;
• Analytics objective, which is a ddressing how data and Analytics are
supposed to support, effect or c hange the LSCM process;
• Human exp ertise in areas relevant to the Initiative as Analytics, IT a nd the
LSCM process (and how it is sourced);
• Applied software and hardware for analytical tasks and d eployment of
developed solutions and tools;
• Data sources and characteristics;

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• Applicated types of A nal ytics (and subsequently analytical methods)
Regarding the groups of c haracteristics above, our findings support considerable
differe nces in Initiative ar chetypes. The patte rns identified allow us to answer the research
question: SCA Initiatives can be distinguished in the six clusters whi ch are described in
regard to the characteristics in subsec tion 4.4 as well as LSCM process centric as follows:
(1) Educating: The LSCM process remains as ex isting but will be enhanced with new
data (sour ces) information as pro cess input to improve decisions to be m ade during the
process resulting in enh ance d LSC M process ou tput quality and custome r orientation.
This typically e merges a s improve d tool used in the process like a new foreca sting model
in a product alloca tion process or new forecast model for a risk evaluation process.
(2) Observing: The LSCM proce ss is extensively investiga ted for conditions that indicate
process d eficiencies o r issues in the sho rt-term o r medium-term future with a resulting
tool to monitor the process based on the newly gained insight. The knowledge a bout the
conditions improves process quality and costs du e to earlier reaction. Exa mples include
detection of engine vibration patterns enabling maintenance planning of vehicles such
that a repa ir shop is the fina l stop of a route on a s uitable point in time inste ad of r andom
breakdown far away from access to maintenance, or detection of weather patterns
resulting in traffic and road conditions demanding changing of routes. However,
identified conditions are indications and lea ve room for human decision-making.
(3) Alerting: LSCM process owne rs are provid ed with alerts on critical conditions and
events that immediately demand reactions. The conditions are usually kno wn by process
owners without the nee d of analytical identificatio n and certain in their negative impact
on the process demanding actions. Alerting Initiati ves’ central task is making the
necessary dat a available to automate the alert as opposed to repeated human check -up
actions. Examples inclu de alerts on closed roades for v ehicle routing or aut omated
recommenda tions of price changes and acceptance of shipments for cargo airlines in close
to departure time-windows. Here a gain, the LSCM process is typically supporte d but not
altered.
(4) Advancing: The LSCM processes and busines s models will be advanced by enabling
changes due to insight made ava ilable with intense data collec tion and analysis. Large
scale data colle ction is central to the I nitiative, using sensors and mobi le de vices to crea te
data-availability-based transparency and evaluation of LSCM process steps . The insi ght

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is used to improve process quality by changing pr ocess steps under incorporation of the
insight and cre ating analytics driven innovations replacing pro cess steps as well as
making insi ght available to interested thi rd p artie s as business mod el innovation.
Examples are machine profiles allowing determina tion of accurate predictive
maintenance processes which can be sold by the machine manufacturer to the machine
user, or driver p rofiles to create new moni toring steps to reduce idle time. These
Initiatives differ from o bserving and alerting b y extensiveness of d ata collection and
analysis typically demanding big data technologie s, and range of the re sulting tool, which
changes the process to become tool and thus data focused as opposed to a minor process
support.
(5) Refining: The LSCM processes ar e changed to incorporating faster, broader, and more
freque nt guidance on actions and decision support. Instead of optimized p lans that are
executed, the objective of these Initiatives is to optimize plans during execution
dynamically based on data about current events and conditions. Example s are dynamic
changes of routes of vehicles already on the road, or dyna mic c hanges of picker route s in
distribution centers already picking. The LS CM process is changed due to extensive focus
on guidance tools guidance during process execution.
(6) Inve stigating: The LSCM process (and asset) deficienc ies and issues are investigated
for their causes to enable the solution search for design changes to the process. These
changes are supposed to create new processes with improved costs and qu ality over the
process under investigation. As opposed to iss ues described in advan cing or refining ,
process changes like automation or dat a-driven too ls for guidance will not create control
over the process issues addressed in these Initiatives. Thus, crea tivity and engineering
design is required. Examples include the investiga tion of occurr ence of empty shelf space
in retail stores to redesign replenishment processes of products or the investigation of
process environment factors in production lines le ading to quality issues that have to b e
avoided.
4.5.1 Theoretical Contr ibution
Our research contributes to LS CM researc h with a focus on S CA and the practical
application of SCA , an area that has been demanded to be inv estigated by several
researchers in LS CM (Sanders, 2014; Schoenherr and Speier-Pero, 2015; Waller and
Fawcett, 2013). The iden tified archetypes provide an empirically developed taxonomy.
Further, they give insight into the underlying s tructure of An alytics Initiatives used in

98
LSCM – why and how they are applied. The research decomposes SCA Initiatives in
important distinct parts, which ca n structure future rese arch. Thereby, this re searc h
specifica lly addresses chara cteristics influencing Ana lytics I nitiative and is not limited to
distinction by software (Hahn and Pac kowski, 2015) or LSC M process (Souza, 2014).
The proposed archetypes seek to guide discussions, resea rch and training of students
becoming managers enabled to use S CA. The di scussion aspired by the authors should
address how to ena ble organizations to crea te Initiatives beyond th e presented
contemporary a rchetypes with more sophisticated supply chain and Analytics objectives,
rather than conducting single case studies or literature re views on the c ompetitive impact
without empirical evid ence. Our research pr ovides a f ramework s upporting the
investigation of the effect s of diff erent types of An alytics Initiatives and helps researchers
working with data models and quantitative case studi es to orientate themselves in the
bigger picture of their research. This framework further allows to investigate the
implications of various kinds of S CA Initiatives o n performance, barriers as well as the
efficie ncy of the Initia ti ves based on the arc hetype of the Initiative. Finally, this researc h
gives a two -dimensional picture to introduce stude nts to thi s field and ease the process of
understanding important factors and possibilities by the proposed first dimension of
arche types to understa nd wha t c ompanies do and the second dimension of c harac teristics
of SCA I nitiatives to understa nd what aspe cts to consider when constructing an I nitiative.
Thus, it enables resea rchers and students to introduc e their LSCM knowledge into
Analytics I nitiatives an d provide conside rable value that is required for successful
Initiatives (Schoenherr and Speier-Pero, 2015; Waller a nd Fawcett, 2013).
This research further addresses the gap between theory and o rganizational activities
highlighted by several scholars, especially in manage ment scien ce (Banks et al., 2016;
Suddaby, 2010). Our archetypes map the activities of organizations and prov ide templates
for org anizations and scholars in the field to und erstand what drives org anizations to their
activities.
4.5.2 Managerial Contribution
Th is research copes with the managerial barri ers related to missi ng insight into the
application of SCA. By de scribing arc hetypes of thi s application, we gi ve mana gers
directions for future SCA I nitiatives based on th eir initial busi ness situation, available
means and objectives. Presenting the results to experts in Analytics, the ar chetypes were
well received with the r emark that managers m ay lack creativity of how to address

99
business problems with Analytics which could be supported with the r esults of this
research. In thi s regard, manage rs may combine archetypical app roaches to create new
Initiatives or explor e Initiatives with supply chain or analytical objectives rarely
observed.
Considering the barriers discussed in section 4.4.8, our researc h presents how the
application of SCA creates value for an organizati on and how decisions are made based
on S CA. Managers should be enabled to decide which of the overwhelming opportunities
provided by SCA to take and which to postpone or reject with the primary objective of
providing value to the o rganization . Naturally, this requires th e c reativity t o design new
Initiatives.
The research further p rovides a f ramework for managers to understand the key
components to build an S CA I nitiative. First and for emost, an Initiative has to address
existing problems – meaning any disparity between objective state and act ual state – for
the LSCM Part as well as they require an analytical objective to address the LSCM
problem. Further characteristics display fields that have to be developed and improved
over time to design more c omplex I nitiatives, e ven in the same archetype. For the Human
category, that includes building skills supporting the execution of Initiative as hard skills
in Analytics as well as communication skil ls to transfer thoughts, ideas and experience
between the dif ferent experts. In the technology category, this inclu de in vestments in
easier data ex change , faster analysis and calcula tion as well as mor e -powerful analytical
tools. In the data cat egory, this includes broader data collection and higher standards for
data quality.
Our research further develops a vocabulary to communicate managers’ o bjectives and
vision while highlighting small but cruc ial difference s. The archetypes are imagined as a
menu of options a mana ger may use to choose spec ific or c ombined items. We intend the
arche types to guide his Initiative design process, as opposed to having an infinity of
options that quickly bec omes overw helming. Therefor e, besides providing dire ctions, the
arche types also serve as validation of the fit of characteristics of the Initiative and thus,
it’s practicality. This en ables managers to pinpoin t what they aspire and comm unicate it
directly and properly.
The other way around, t he archetypes can be stimulation for two additional types of
managers keen to use Ana lytics in LSCM. First, Mana gers that achieved some routine in
Initiatives of a specific archetype may get stuck i n that arche type and rep eat it for ever

100
new use cases which ev entually leads to de creasing marginal value from that kind of
Initiati ves. S econd, mana gers the are supposed to “make more from their data” – a type
that is not very rare from our personal experience. Both types of managers could, using
the archetypes, id entify promising problems to address and subsequentl y search for
interested users o r r ather “problem owners”. The first type obviously benefits over the
second from knowing eventual problem o wners from h er previous proj ects she could
address again with another beneficial Initiative.
4.6 Final remarks
4.6.1 Limitations
Due to the various sources of the I nitiatives c onsidered, their des criptions are provide d in
various levels o f detail regarding the charac teristics used to eva luate them. With 46 cases,
the amount of considered cases is low. Additionally, the observed c ases only represent
successes since th ese are more likely to b e published in any form. Unsucc e ssful cases to
use Analytics in LSCM c ould not be identified. Further, the cases have bee n collected in
a proc edure which is ha rd to recreate. This is due to the lac k of a public database fo r such
case studi es, especially considering the amount of studi es needed to conduct a meaningful
cluster analysis. Since databases for research ( e.g., S copus, Web of Scienc e or EBSCO)
did not yield relevant results, we were reliant on an open se arch platform. The search was
suspended when a reasonable amount of time (1 6 h / two workdays) for s earching cases
did not yield any new results. However, the po ssibilities to collect the data for this
research were rather limited.
Furthermore, consid ering the data ana lysis, the dec ision about the number of c lusters and
thus the number and structure of the identified arche types depends on several vagu e
factors and cannot be made objectively. Th e cluste rs are created based on the researchers’
interpretations and judgement. We presented the results to re searc hers in LS CM and
experts in Analytics, which assessed the clusters as rea sonable.
4.6.2 Future Rese arch
This study takes a step t owards understanding the inner stru cture of a growing field of
research, which should not be investi gated as a single entity to generalize use , eff ects and
benefits anymore. Thus, future research may investigate the effects of S CA I nitiatives
distinguished by arche type to c reate more sophisticated insight. The archetypes may also
be correlate d to Analytics maturity or the growth-share matrix to identify easy- to -start
arche typical Initiatives for organizations with l ow maturity and identif y factors of

101
successful Initiatives with the potential to create competitive advantage. Additionally,
since we consider this researc h to be contemporary, we encourage to repeat t his research
in five to ten years.

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Submitted version. Published as: Herd en, T. T. (2019 ). Explaining the competitive advantage g enerated
from Analytics with th e knowledge -based view: the example of Logistics and Supply Ch ain Management.
Business Research , 1 - 52 . https://doi.or g/10.1007/s4068 5 - 019 -00104 -x (published by Spr inger, CC BY
4.0)
103
5 Explaining the Competitive Advantage Generate d from Analytics with the
Knowledge-based View – The Examp le of L ogistics and Supply Chain
Management
The purpose of this paper is to provide a theory-based explanation for the genera tion of
competitive advantage from Analytics and to examine this explanation with evidence
from confirmatory c ase studi es. A theoretical argumentation for achieving sustainable
competitive advantage from knowledge unfoldin g in the knowledge-based view forms
the foundation for this explanation. Lit erature about the process of Analytics initiatives,
surrounding factors and conditions, and benefits from Analytics are map ped onto the
knowledge-ba sed view to derive propositions. Eight confirmatory case studies of
organizations mature in Analytics are collected, focused on Logistics and Supply C hain
Management. A theoretical fram ework explaining the creation of competitive advantage
from Ana lytics is de rived and presented with a n e xtensive description and rationale. This
highlights various aspects outside of analytical methods, including cross-functional
teams, iterative problem solvi ng with user feedback, solut ion consumabi lity , and
innovative culture. Further, this study prese nts a practical manifestation of the
knowledge-ba sed view.
5.1 Introduction
The use of Analytics is incre asing across industries. I t is fueled by tre nding concepts like
big data and data science, innovative technologies such as distributed computing and in -
memory databases, as well as the rapid incr ease o f data available fo r process ing. A recent
survey showed a constant increase of organizations perc eiving the role of Analytics as
critical, a closing maturity and capability gap betwe en digital natives and traditional
companies in applying analytics, and it s strategic role, as embodied in appointments of
C-level executives for Ana lytics (Alles and Burshe k, 2016). A lar ge proportion of
surveyed o rganizations believe that they can gain c ompetitive advantages from Analytics.
Another study goes as far as to suggest that data-empowered org anizations may threaten
the market survival of companies not using these approaches (FreshMinds, 2015) .
However, organizations also struggle with adopting Analytics successfully (Viaene and
Bunder, 2011), with one difficulty being the ongo ing discussion about what defines
Analytics. Holsapple et al. (2014) investigated a plethora of definitions of Analytics,

104
including the definition by Davenport and Ha rris (2007), who initi ated the broader
recognition of Analytics with their famous book. Holsapple et al. (2014 ) id entified at least
six definitional perspectives on Analytics just in the literature they re viewed, highlight ing
the diverse comprehension of the topi c. Based on the core ch aracteristics of Analytics, it
has been described as recognizing a nd solvi ng business problems based on evidence such
as data, facts, but also well-reasoned estimations. Further, Analytics ini tiatives are diverse
and have to fit with people, processes, and tasks t o enable their benefits (Ghasemaghaei
et al., 2017), demanding investigation of which practices and conditions lead to
genera tion of competitive advantage from Analytics.
Competitive advantage is frequently discussed in the strategic m anagement literature. Th e
resource-ba sed view argues for competitive advantage based on the resources of firms,
including assets, capabilities, processes, attributes, and knowledge – if these a re rare,
imperfec tly imitable, and non-substitutable (Barney, 1991). The ca pabilit y-based view
emphasizes the se resources as the capabilities of firm s that cannot be purchased on the
market and require strate gic vision to deve lop over time through the strategic decisions
of bounded rational ma nagers facing uncertainty, complexity , and conflict (Amit and
Schoemaker, 199 3). The relational view argues th at firms’ resources are of limited value
in providing competitive advantage, and instead c redit it to the combined re sources of a
network of firms (Dyer and S ingh, 1998). Finally, the knowledge-based view narrows
down the resource required to provide competitive adv antage to firms to j ust one item,
which satisfies all the necessar y characteristics – the knowledge held by the individuals
of the firm (Grant, 1996a). Managers are responsible for integrating and applying that
knowledge. As the integration and application process of knowledge fits the definition of
Analytics as proble m recognition and solving, the knowledge-b ased vie w provides a
reasona ble theoretical gr ounding to investigate the generation of competitive adva ntage
from Analytics.
One discipl ine increasingly adopting Analytics is Logistics and Supply Chain
Management (LSCM). Scholars expect Analytics to cha nge how supply chains operate
(Schoenherr and Speier-Pero, 2015). In p ractice, e xecutives assess Analytics a s playing a
pivotal role in driving profit and cre ating competitive advantage in LSCM (Thieullent et
al., 2016) . Due to the vast number of a pplications a reas and the assumed potential, a sub-
discipline of Analytics used in LSCM has formed, labeled S CM Data Sc ience (Waller
and Fawcett, 2013) or Supply C hain Analytics (Chae, Olson, et al., 2014; Souza, 2014) .

105
LSCM is considered an early adopter of analytical methods, using Operations Resea rch
to optimize inventories, locations, and transportation costs (Davenport 200 9). Holsapple
et al. (2014) even cite an article on p roduction control and automation whil e exhibiting
the origins of Analytics. In recent research, the use of Analytics has sho wn a positive
impact on LSCM performance (Chavez et al., 2017; Sanders, 2016; Trkman e t al., 2010)
and researchers have call ed for further research on Analytics in LSC M (Schoenhe rr and
Speier-Pero, 2015; Waller and Fawcett, 2013 ). However, research has also shown that a
major proportion of organizations remain reluctant to use Analytics or are not even
familiar with it , due to, amongst other f actors, lack of ideas about how to achieve
advantage from it (Sanders, 2016; S choenherr and Speier-Pero, 2015).
To investigate Analytics’ impact on organizations’ competitive a dvantage, narrowing the
focus is necessary. This article ’s investigation focuses on the example of LS CM for
severa l reasons in addition to the field be ing an early a dopter and a chieving considera ble
value from employing Analytics. From its cor e characteristics, LSCM is driven by
efficie ncy and cost- effectivenes s (Sim chi-Levi et al., 2003), which demands sophisticated
decision-making – as supported by Analytics. Th erefore, it is not surprisi ng that LSCM
has a long history of emphasizing data- driven decision-making (Souz a, 2014; Waller and
Fawcett, 2013). LSCM is usually a complex ta sk, managing information, products,
services, and financial a nd knowledge flows across internal unit s such as procure ment
and manuf acturing, as well as between globally dis persed organizations including
suppliers, retailers, or manufacturers (Bowe rsox et al., 2007). C onsequently,
collaborative approaches to Analytics are needed, presenting a unique challenge for
Analytics, since data comes from several differe nt orga nizations and r esults a re de ployed
across them (Davenport, 2009). In addition, LSCM is a human-center ed process with a
variety of decision makers acting on the basis of their personal experience, resulting in
unexpected events, human errors, and conseque ntial dynamic effects in the processes
(Wang et al., 2014). Wang e t al. (2014) further highlighted the diversity of proc esses and
the resulting heterogeneity of process knowledge. I n summary, LSCM is chosen as a
focus due to th e field’s e xperience with data -driv en solutions, the constant demand for
further improvement, and the challenges associ ated with adopting Analytics given the
complex, diverse, dispersed, and error-p rone p rocesses distributed across several business
units, organizations, and decision makers.

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