Robotic process automation in logistics: Implementation model and factors of success

Krakau, Jan; Feldmann, Carsten; Kaupe, Victor Conference Paper Robotic process automation in logistics: Implementation model and factors of success Provided in Cooperation with: Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management Suggested Citation: Krakau, Jan; Feldmann, Carsten; Kaupe, Victor (2021) : Robotic process automation in logistics: Implementation model and factors of success, In: Jahn, Carlos Kersten, Wolfgang Ringle, Christian M. (Ed.): Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 32, ISBN 978-3-7549-2771-7, epubli GmbH, Berlin, pp. 219-256, https://doi.org/10.15480/882.4005 This Version is available at: https://hdl.handle.net/10419/249652 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-sa/4.0/ Published in: Adapting to the Future: Carlos Jahn, Wolfgang Kersten and Christian M. Ringle (Eds.) ISBN 978-3-754927-71-7, September 2021, epubli Jan Krakau, Carsten Feldmann, and Victor Kaupe Robotic Process Automation in Logistics: Implementation Model and Factors of Success CC-BY-SA4.0 Proceedings of the Hamburg International Conference of Logistics (HICL) –32 Robotic Process Automation in Logistics: Implementation Model and Factors of Success Jan Krakau1, Carsten Feldmann1 and Victor Kaupe2 1 – University of Applied Sciences Münster 2 – BASF Coatings GmbH Purpose: Robotic process automation (RPA) refers to software robots (bots) that automate repetitive, rule-based tasks in a business process. In this study, the research questions regarding logistics applications are as follows: (1) What are suitable use cases for RPA in logistics? (2) Which criteria support the selection of appropriate processes? (3) How should a procedure model for implementation be designed to systematically support the introduction while considering critical success factors? Methodology: This study follows the design science research process by Peffers et al. (2006). The research gap was identified through an extensive literature analysis, reflecting the state of research. Insights gained were compared with empirical data from the use of RPA at a case company. Findings: A procedure model was designed to systematically consider success factors for an implementation, comprising (1) initiation; (2) piloting; (3) deployment; and (4) ongoing governance, maintenance, and continuous improvement. Originality: RPA can contribute to solving challenges such as increased service demands from customers, combined with cost pressures and a shortage of skilled labor. The procedure model closes a research gap, both from a scientific perspective and from the practitioners’ viewpoint, supporting an efficient and effective implementation. The consideration of knowledge from both theory and practice ensures practical relevance and significantly expands the state of research. First received: 12. Mar 2021 Revised: 18. Aug 2021 Accepted: 31. Aug 2021 Robotic Process Automation in Logistics 1 Introduction Robotic process automation (RPA) refers to software robots (bots) that emulate humans in executing repetitive, rule-based tasks in a business process (Cernat et al., 2020; Willcocks et al., 2015a). In comparison to other modes of automation, RPA bots act at the front-end level of applications (Lacity et al., 2016a). Logistics is one of the many domains of interest for RPA implementation. Logistics is characterized not only by physical processes but also by digital processes such as interactions between application systems that can potentially be automated with RPA. The benefits of RPA in logistics are manifold. First, the automation of routine tasks enables employees to conduct more value-adding work and coincides with cost reductions achieved by workforce salary savings (Mullakara and Asokan, 2020; Murdoch, 2018). Second, organizations profit from a fast and reliably predictable return on investment (ROI; Alberth and Mattern, 2017). Further benefits are the increasing process execution speed and 24/7 availability of bots as well as higher process execution accuracy and improved compliance due to log data transparency (Murdoch, 2018). In addition, the implementation effort is relatively low compared to invasive automation solutions. This is because neither complex adjustments to application systems nor extensive coding knowledge are required, as programmed modules can be reused (Czarnecki and Auth, 2018; Lacity et al., 2016a; Langmann and Turi, 2020). The main challenge in exploiting these benefits is the development of a holistic framework for RPA implementation. The ensuing research questions are as follows: RQ1: What are suitable use cases for RPA implementation in logistics? RQ2: What criteria support the selection of processes suitable for the implementation of RPA? RQ3: How should a procedure model for implementation be designed to systematically support the introduction while taking critical success factors into account? Chapter 2 provides an overview of the state of research to deduce the research agenda, and the research methodology is outlined in Chapter 3. Following the phases of the design science research process, the procedure model is developed and validated in Chapters 4 to 6. In the concluding chapter, the main findings are summarized, and implications for further research and practice are derived. Krakau et al. (2021) 221 2 State of the Field and Research Gap To obtain an overview of the state of research, a comprehensive literature review was conducted utilizing the approach by Vom Brocke et al. (2009). Relevance was gained by refraining from investigating what is known already (Baker, 2000). Rigor results from effectively applying the existing body of knowledge base (Hevner et al., 2004). As part of the keyword-based literature research, 1,120 publications were initially identified in eight databases. Based on an analysis of article titles and abstracts as well as forward and backward searches, this number was decreased to a sample of 57 publications by applying the criteria of relevance, timeliness, and validity (see Figure 1). To provide high quality sources, the focus was on articles in scientific journals and conference proceedings. For the literature review presented next, the authors used the concept matrix presented in Appendix A, which breaks down topic-related concepts into different units of analysis. Figure 1: Databases and statistics from the literature search process Many of the examined articles focus on general success factors for subsections of RPA implementation, often lacking a holistic and coherent view. However, in 17 papers, a structured phase model for implementation is presented (Alberth and Mattern, 2017; Hallikainen et al., 2018; Herm et al., 2020; Ilo, 2018; Jimenez-Ramirez et al., 2019; Kanakov and Prokhorov, 2020; Koch and Fedtke, 2020; Kyheröinen, 2018; Langmann and Turi, 2020; Masó, 2018; Myllymäki, 2019; Rutschi and Dibbern, 2020a; Sig-urðardóttir, 2018; Smeets et al., 2019; Willcocks et al., 2015a; Willcocks et al., 2019; Zaharia-Radulesu et al., 2017). Nonetheless, an in-depth analysis of these papers revealed substantial differences regarding the implementation approach and the focus of consideration. Furthermore, only four articles provide a profound practical validation of the theoretically derived procedure model (Ilo, 2018; Kyheröinen, 2018; Masó, 2018; Rutschi and Dibbern, 2020a). Databases Web of Science n = 39 SCOPUS n = 233 ScienceDirect n = 312 EBSCOhost n = 273 Emerald n = 35 WISO n = 65 ECONbiz n = 64 IEEE Xplore n = 99 Relevant Articles after Forward and Backward Search n = 57 Selected Articles after Title and Abstract Examination n = 27 n = 1120 Robotic Process Automation in Logistics Apart from these limitations, a major portion of the examined articles are confined to a general-level analysis. Of the few domain-specific papers that exist, a large proportion focuses on finance and accounting, followed by auditing, human resources, controlling, and manufacturing. However, none of those articles related to RPA implementation addresses logistics-specific aspects. Therefore, the literature research was extended to identify logistics use cases for RPA, resulting in an analysis of seven further articles (Agaton and Swedberg, 2018; Czarnecki and Auth, 2018; Feld et al., 2017; Kaya et al., 2019; Madakam et al., 2019; NTT DATA, 2018; Scheer, 2018), although these articles do not derive any logistics-specific characteristics and success factors for implementation. Hence, the state of the field can be summarized as follows: Procedure models for RPA implementation are rarely domain-specific, but are often limited to general-level analysis. No logistics-specific procedure model exists. Moreover, most procedure models lack practical validation. It is therefore difficult for practitioners to understand how the outlined benefits of RPA can be achieved in logistics. Krakau et al. (2021) 223 3 Research Methodology For the design and evaluation of the procedure model, design science was chosen, as it offers a proven methodological context for construction-oriented research projects. Specifically, the research logic is based on Peffers et al. (2006), incorporating the guidelines by Hevner et al. (2004). The design science research process outlined by Peffers et al. (2006) essentially consists of six steps: problem identification and motivation, objectives for a solution, design and development, demonstration, evaluation, and communication. Following this approach, Hevner et al.'s (2004) guidelines ensure the scientific relevance and rigor of research as well as sufficient validation and effective communication of the outcome to both researchers and practitioners (see Figure 2). Figure 2: Design science research process and guidelines, cf. Zellner (2015) Problem identification, objective formulation, and design and development were carried out based on a systematic and comprehensive literature review, following Vom Brocke et al. (2009); see Chapter 2. With regard to the design phase, the aforementioned 17 existing phase models for RPA implementation were mapped to identify commonalities and deviations. Building upon this mapping, insights from a case study at a leading German coating manufacturer were obtained, validating the theoretical findings and enhancing the quality of the artefact. RP 1: Problem Identification & Motivation RP 2: Objects of a Solution RP 3: Design & Development RP 4: Demonstration RP 5: Evaluation RP 6: Communication GL 2: Problem Relevance GL 1: Design as an Artefact GL 3: Design Evaluation GL 7: Communication of Research GL 4: Research Contributions GL 5: Research Rigor GL 6: Design as a Search Process RP: Research Process according to Peffers et al. GL: Guidelines according to Hevner et al. Robotic Process Automation in Logistics 4 Problem Identification and Objectives for a Solution Based on the literature review outcomes, the research gap identified is the lack of a holistic procedure model for systematically guiding practitioners in implementing RPA in logistics processes. To illustrate the complexity, the research problem can be further atomized. First, substantial knowledge about logistics use cases and suitable logistics processes is lacking. Moreover, practitioners risk making poor decisions when implementing RPA, leading to an unnecessarily long implementation duration. As a result, the costs incurred may increase, especially if external consultants are hired for implementation assistance. The problem specification indicates that practitioners require a structured approach for implementing RPA in logistics, comprising transparent information about the objectives, input, procedure, output, methods, and success factors at every stage. A procedure model is a mapping of the activities to be performed within the context of an overall task (Schütte, 1998; Feldmann et al, 2020): A standardized process structures the fulfillment of the implementation task so that progress can be tracked and documented during the RPA project. A procedure model encourages a common understanding of the process and cooperation between the parties involved. With regard to the research questions outlined in the introductory chapter and the problem specification delineated in the previous chapter, three main objectives for a solution are pursued. First, suitable logistics use cases for RPA implementation are to be identified. Second, criteria supporting the selection of suitable logistics processes are to be depicted in a structured way. Third, a procedure model for systematic implementation is to be designed, consisting of critical success factors derived from a literature analysis and practical implementation. 5 Design and Development 5.1 Overview The artifactual solution to be designed is a procedure model for the implementation of Krakau et al. (2021) 225 RPA in logistics processes. In Section 5.2, the fundamental design principles are outlined. Then, in the subsequent sections, an overview of the procedure model is provided, and its individual phases are described in detail. 5.2 Design Principles According to Vom Brocke (2007), modeling is a design process intended to create a model that meets users' requirements. In terms of RPA implementation, the procedure model should provide a useful reference guideline for practitioners such as project managers and team members of RPA implementation projects as well as logistics process owners. To ensure high model quality and practicability, various design principles are applied. Following Becker et al. (1995), these design principles are accuracy, relevance, costeffectiveness, clarity, comparability, and systematics. 5.3 Procedure Model 5.3.1 Overview Seventeen phase models for RPA implementation were identified during the literature research. These phase models were mapped to detect commonalities as well as deviations and to derive an appropriate procedure model framework (see Appendix B). The resulting model encompasses four main phases (see Figure 3): 3. Initiation, which entails (1.1) project setup, (1.2) logistics use cases and processes identification, (1.3) business case calculation, and (1.4) software provider selection; 4. Piloting, comprising (2.1) process documentation and optimization, (2.2) pilot bot development, and (2.3) pilot validation; 5. Deployment, which involves (3.1) operating model setup, (3.2) center of excellence creation, and (3.3) deployment at a large scale; 6. Ongoing governance, maintenance, and continuous improvement. Robotic Process Automation in Logistics was activated, and the process went “live.” Change management was then documented, the teams were onboarded, and management was informed. After implementation, Phase 4 was launched. The process was maintained regularly, performance was checked, statistics were compiled, and the process itself has been optimized continuously according to the LEAN principles of plan, do, check, act (PDCA). 6.2 Use Case Two: Digital Outbound Checklist The second use case is the application of an RPA bot instead of developing a conventional interface between two IT systems. Interfaces for automated data exchange with other IT systems are not available with every IT system. For process automation, they must often be programmed and implemented in a time-consuming and cost-intensive manner. For simple standard processes, the use of bots is an alternative to data transfer between IT systems as a so-called non-invasive solution without major programming effort or deeper intervention in the respective IT systems. Standardized workflows, the comparison of use cases, and the adaptation of existing bots for further implementations are part of the deployment phase (Phase 3). The second use case demonstrates the applicability of this approach. The physical logistics department handles the dispatch of outgoing trucks. Here, the picked and packed pallets are loaded onto the trailers. Loading security is ensured by warehouse workers using a paper-based checklist to document the correct condition of the truck and loaded cargo leaving the coating manufacturer's yard. Since this process was digitized using a digital checklist, a transfer method was needed to digitally store the checklist data, picklists, loading image, personal data of the trucker, and other shipping documents for further processing. Time-consuming and costly custom-programmed interfaces were traditionally used to transfer the data to the system, which would hinder rapid process implementation. Instead, the above-mentioned existing transportation management bot was modified to fill the gap of a missing data transfer interface between the plug-and-play checklist and the company's ERP system. The process was designed as visualized in Figure 4. Krakau et al. (2021) 233 Figure 4: Process for robotic process automation (RPA) implementation in case company The bot receives the aforementioned data as an e-mail attachment, reads the content, logs on to the transport management system with its personalized access rights, files the document under the corresponding shipment number, saves the status, and completes the process at the end. A detailed overview of success factors derived from the delineated RPA implementation approaches at the coating manufacturer can be found in Appendix C. 6.3 Evaluation From the company's point of view, the bots offer flexibility because they can work independently 24/7, thereby enabling improved productivity. The bots also work in a standardized and error-free manner, meaning that the high process performance remains constant and measurable. Both the productivity boost and the consistent high performance increase compliance and safety, especially in companies that are strongly quality driven, such as the automotive industry. Employees can focus on more valueadded work rather than dealing with strenuous or low-value work, thus increasing employee satisfaction. In addition, RPA can be implemented quickly and cost-effectively, and the benefits could be measured both qualitatively and quantitatively. In summary, RPA has the potential to increase quality and efficiency, and the lessons learned that have been incorporated into the procedure model are transferable to other use cases. RPA is suitable not only for taking over human activities on IT systems, but also for data transfer between two systems as an alternative to a complex programmed interface. Robotic Process Automation in Logistics 7 Communication The objective of this paper was to provide support to logistics practitioners in implementing RPA efficiently and sustainably. To achieve this objective, three research questions were answered. RQ1: What are suitable use cases for RPA implementation in logistics? Based on a comprehensive literature review, numerous use cases were identified. RQ2: What criteria support the selection of processes suitable for the implementation of RPA? Qualitative and quantitative criteria were provided for the selection of suitable processes. In particular, a high degree of rule-based tasks, standardization, repeatability and digitization, low complexity and cognitive requirements, high process maturity, high error-proneness in manual execution, and a high stability of the system environment indicate suitability for RPA. RQ3: How should a procedure model for implementation be designed to systematically support the introduction while taking critical success factors into account? Previously available process models were not specifically geared to the requirements of logistics or were not sufficiently validated. This gap was closed by the domain-specific process model. The logistics-specific procedure model presented in this paper significantly expands the state of research. On the one hand, a comprehensive literature review and a phase model synopsis were conducted to derive an appropriate framework based on commonalities and deviations. On the other hand, the results of a case study validation comprising two logistics use cases were considered. Nonetheless, limitations remain. First, apart from the necessity of further validation, a distinction between small and large companies would be useful regarding, for example, the number of employees in the CoE. Moreover, further research could focus on a more holistic approach to process automation, including a criteria-based selection between different tools and technologies such as artificial intelligence (AI) or intelligent business process management suites (iBPMS). RPA has various benefits for practitioners. Aside from cost reductions, 24/7 bot availability, and a higher process execution accuracy, the automation of routine tasks enables employees to conduct more value-adding work. Moreover, neither complex adjustment to application systems nor extensive programming knowledge are required Krakau et al. (2021) 235 for RPA implementation. The presented procedure model supports practitioners with the implementation process, providing step-by-step guidance including objectives, input, procedure, output, methods and tools, and success factors for each phase. It must be emphasized that continuous change management is essential to run RPA successfully. Robotic Process Automation in Logistics Appendix A: Concept Matrix for Literature Analysis Krakau et al. (2021) 237 Appendix B: Synopsis of Phase Models R o b otic Pr o c ess A ut o m ati o n i n L o gisti cs A p p e n dix C: Pr o c e d ur e M o d el f or R P A I m pl e m e nt ati o n 1 I niti ati o n 1. 1 Pr oj ect S et u p 1. 2 L o gistics Us e C as es & Pr oc ess es I d e ntifi c ati o n O bj ecti v es - D efi niti o n of b asic pr oj e ct g ui d eli n es ( K oc h a n d F e dt k e 2 0 2 0) - Pr oj ect pl a n ni n g - Pr oj ect t e a m ass e m bl y a n d divisi o n of r es p o nsi bilit y - Us e c as e i d e ntific ati o n a n d pr oc ess s el ecti o n f or R P A p ilo t im p le m e n ta tio n b a se d o n co m b in a tio n o f q u alit ati v e a n d q u a ntit ati v e crit eri a I n p ut - S u p p ort b y m a n a g e m e nt ( Willc oc ks et al. 2 0 1 9) - St at e m e nt of W or k ( S O W) - P ers o n n el r es o urc es - B u d g et a p pr ov al - Ass e m bl e d pr oj e ct t e a m - Pr oj e ct pl a n - P ur p os e of R P A i m pl e m e nt ati o n Pr oc e d ur e - D et er mi n e t h e p ur p os e/ o bj ecti v es of R P A i m pl e m e nt ati o n ( Al b ert h a n d M att er n 2 0 1 7, K y h er ö i n e n 2 0 1 8) - E ns ur e ali g n m e nt wit h b usi n ess str at e g y ( H er m et al. 2 0 2 0) - D efi n e pr oj e ct sc o p e ( C ar d e n et al. 2 0 1 9) - Pr e p ar e ti m eli n e ( K oc h a n d F e dt k e 2 0 2 0) - Ass ess risks, c alc ul at e pr oj ect eff ort, d efi n e q u alit y re q u ire m e n ts, d e v e lo p co m m u n ica tio n a n d ch a n g e m a n a g e m e nt pl a n - Ass e m bl e a cr oss-f u ncti o n al t e a m f or R P A i m pl e m e nt ati o n ( B al as u n d ar a m a n d V e n k at a giri 2 0 2 0, K oc h a n d F e dt k e 2 0 2 0, S m e ets et al. 2 0 1 9): R P A fa cilita to r (e xp e rie n ce d p ro je ct m a n a g e r), R P A ex p ert (I T-s a v v y e m pl o y e e wit h R P A d e v el o p m e nt ex p ertis e) a n d I T i nfr astr uct ur e e x p ert ( e m pl o y e e wit h a br o a d n et w or k wit hi n t h e I T d e p art m e nts) - D efi n e g ui d eli n es f or c o o p er ati o n i n t h e pr oj ect ( K oc h a n d F e dt k e 2 0 2 0) - D e v el o p b asic R P A c a p a biliti es ( Willc oc ks et al. 2 0 1 9) - Pr e p ar e pr oj e ct c h art er ( C ar d e n et al. 2 0 1 9) I d e ntif y g e n er al l o gistics us e c as es a n d g et o v er vi e w of pr oc ess l a n dsc a p e (cf. a p p e n di x f or d et ail e d o v er vi e w of pr o v e n l o gistics us e c as es f o u n d i n lit er at ur e) ( Al b ert h a n d M att er n 2 0 1 7) E v al u at e us e c as es b as e d o n q u alit ati v e pr oc ess c h ar act eristics i n sc ori n g m o d el ( H er m et al. 2 0 2 0, M ur d oc h 2 0 1 8) - D e gr e e of r ul eb as e d ( hi g h -- > e asy d ec o m p ositi o n i nt o cl e ar s u bpr oc ess es) - Pr oc ess c o m pl exit y (l o w) / c o g niti v e r e q uir e m e nts (l o w) - Pr oc ess m at urit y ( hi g h) - D e gr e e of di git aliz ati o n ( hi g h) - N u m b er of i n v ol v e d syst e ms ( hi g h) - Exc er pti o n r at e (l o w) / st a n d ar diz ati o n d e gr e e ( hi g h) - D e gr e e of r e p etiti v e n ess ( hi g h) - St a bilit y of e n vir o n m e nt ( hi g h -- > n o/f e w c h a n g es i n u n d erl yi n g syst e ms) - R ed e pl oy a bilit y of p ers o n n el ( hi g h) E v al u at e us e c as es b as e d o n q u a ntit ati v e pr oc ess c h ar act eristics ( S m e ets et al. 2 0 1 9) - C alc ul ati o n of m o nt hl y c ost s avi n gs b y m ulti plic ati o n of: - N u m b er of pr oc ess r u ns p er m o nt h - Us u al d ur ati o n of pr oc ess r u n i n h o urs - I n v ol v e d e m pl o y e e c osts p er h o ur C o m bi n e q u alit ati v e a n d q u a nt ati v e c h ar act eristics e v al u ati o n i n m atrix/ h e at m a p t o s el ect us e c as es / pr oc ess es f or R P A pil ot i m pl e m e nt ati o n Kr a k a u et al. (2 0 2 1 ) 2 3 9 1 I niti ati o n 1. 1 Pr oj ect S et u p 1. 2 L o gistics Us e C as es & Pr oc ess es I d e ntific ati o n O ut p ut - P ur p os e/ o bj ecti v es of R P A i m pl e m e nt ati o n ( Al b ert h a n d M att er n 2 0 1 7) - Pr oj ect pl a n, sc o p e, ti m eli n e, risks, eff ort, q u alit y r e q uir e m e nts, c o m m u nic ati o n, c h a n g e m a n a g e m e nt - Ass e m bl e d pr oj ect t e a m wit h cl e ar r ol es a n d c a p a biliti es - G ui d eli n es f or c o o p er ati o n ( K oc h a n d F e dt k e 2 0 2 0) - Pr oj ect c h art er ( C ar d e n et al. 2 0 1 9) - Us e c as es / pr oc ess es f or R P A pil ot i m pl e m e nt ati o n ( H alli k ai n e n et al. 2 0 1 8) M et h o ds - Pr o v e n m et h o ds f or pr oj e ct pl a n ni n g ( e. g. st a k e h ol d er a n al ysis m atrix, w or k br e a k d o w n str uct ur e a n al ysis, r es p o nsi bilit y assi g n m e nt m atrix, ris k pr o b a bility a n d i m p act m atri x) - W or ks h o ps, s ur v e ys, disc ussi o ns f or b asic pr oc ess i d e ntific ati o n ( H er m et al. 2 0 2 0) - Sc ori n g m o d el f or q u alit ati v e pr oc ess c h ar act eristics e v al u ati o n (L a n g m a n n a n d T uri 2 0 2 0) - M a trix/h e a tm a p fo r co m b in a tio n o f q u a lita tiv e a n d q u a ntit ati v e pr oc ess c h ar act eristics S ucc ess F act ors - D e v el o p st ak e h ol d er s u p p ort a n d or g a niz ati o n al c o m mit m e nt ( m a n a g e m e nt, e m pl o y e es) b y c o m m u nic ati n g visi o n a n d b e n efits, e ns uri n g cl arit y a b o ut w h at is g oi n g t o h a p p e n a n d e ns uri n g acti v e st a k e h ol d er p artici p ati o n ( Willc oc ks et al. 2 0 1 9) - C o nsi d er hiri n g a n ext er n al r es o urc e s p eci aliz e d i n R P A i m pl e m e nt ati o n t o ac q uir e R P A skill-s et ( T a ulli 2 0 2 0) - A p pr o ac h R P A pr oj ect wit h a l e a n t e a m ( M ur d oc h 2 0 1 8) - M a n a g e m e nt t ol er a nc e f or m a ki n g mist ak es a n d ex p eri m e nti n g wit h R P A ( K oc h a n d F e dt k e 2 0 2 0) - R P A h as t o b e r e g ar d e d as a str at e gic i n n o v ati o n ( n ot o nl y o p er ati o n al) b y m a n a g e m e nt ( Willc oc ks et al. 2 0 1 9) - E arl y I T i n v ol v e m e nt t o e ns ur e c o m pli a nc e wit h I T s ec urity a n d c o nfi g ur e i nfr astr uct ur e (L acit y a n d Willc oc ks 2 0 1 6) - Us e a c e ntr al ex p ert u nit f or i nt er n al d e v el o p m e nt ( C as e St u d y) - E ns ur e a n o p e n mi n ds et t o tr y a n d t est a n e w t ec h n ol o g y ( C as e St u d y) Hi g hl y r el e v a nt pr oc ess c h ar act eristics f or R P A pil ot i m pl e m e nt ati o n: - Si m plicit y of pr oc ess (r ul eb as e d) t o e ns ur e s ucc essf ul i m pl e m e nt ati o n ( H alli k ai n e n et al. 2 0 1 8) - Hi g h m oti v ati o n of pr oc ess ex p ert/ o w n er a n d willi n g n ess t o c o m m u nic at e e x p eri e nc es t o ot h er d e p art m e nts ( K oc h a n d F e dt k e 2 0 2 0) - Cl e ar visi bilit y of i m pr o v e d pr oc ess effici e n cy aft er R P A i m pl e m e nt ati o n ( hi g h v ol u m e) t o e ns ur e m a n a g e m e nt s u p p ort ( H alli k ai n e n et al. 2 0 1 8) - Hi g h d e gr e e of r e p etiti v e n ess a n d err orpr o n e n ess d uri n g m a n u al e x ec uti o n t o e ns ur e e m pl o y e e s u p p ort a n d str e n gt h e n i nt er est i n R P A t ec h n ol o g y ( Willc oc ks et al. 2 0 1 9) - Sc a n l o gistics pr oc ess l a n dsc a p e s yst e m atic all y ( Al b ert h a n d M att er n 2 0 1 7) - A n al yz e if t h er e ar e b ett er-s uit e d a ut o m ati o n t ec h n ol o gi es f or s p ecific pr oc ess es t h a n R P A (Il o 2 0 1 8) - Or g a niz e g e n er al R P A tr ai ni n g f or p ers o n n el at pil ot sit e ( pr oc ess s p eci alists) ( H alli k ai n e n et al. 2 0 1 8) - C o nsi d er us e c as es i n a d mi nistr ati v e l o gistics ( e. g. tr a ns p ort m a n a g e m e nt) or t h e bri d gi n g b et w e e n t w o a p plic ati o n syst e ms as st arti n g p oi nts f or R P A i m pl e m e nt ati o n ( C as e St u d y) R o b oti c Pr o c ess A ut o m ati o n i n L o gisti cs 1 I niti ati o n 1. 3 B usi n ess C as e C alc ul ati o n 1. 4 S oft w ar e Pr o vi d er S el ecti o n O bj ecti v es - C alc ul ati o n a n d ev al u ati o n of b usi n ess c as e f or s el ect e d us e c as es / pr oc ess es (L a n g m a n n a n d T uri 2 0 2 0) - D ecisi o n w h et h er R P A i m pl e m e nt ati o n pr oj ect s h o ul d b e pr oc e e d e d or st o p p e d - S el ecti o n of a s uit a bl e R P A s oft w ar e pr o vi d er ( Al b ert h a n d M att er n 2 0 1 7) I n p ut - S el ect e d pr oc ess es f or pil ot i m pl e m e nt ati o n - C urr e nt b usi n ess str at e g y - D ecisi o n t h at R P A i m pl e m e nt ati o n pr oj e ct s h o ul d b e pr oc e e d e d b as e d o n b usi n ess c as e c alc ul ati o n - K n o wl e d g e of b asic R P A s oft w ar e r e q uir e m e nts ( M ur d oc h 2 0 1 8) - S el ect e d pr oc ess es f or pil ot i m pl e m e nt ati o n Pr oc e d ur e C alc ul at e / esti m at e q u a ntit ati v e a n d q u alit ati v e b e n efits ( A g at o n a n d S w e d b er g 2 0 1 8, Al b ert h a n d M att er n 2 0 1 7, M ur d oc h 2 0 1 8) - R e d uc e d pr oc essi n g w or kf orc e (i n e ur os p er y e ar) - R e d uc e d offic e s p ac e (i n e ur os p er y e ar) - R e d uc e d c osts d u e t o d ef ecti v e pr oc essi n g (i n e ur os p er y e ar) - R e d uc e d F T E o v er h a n g c osts (i n e ur os p er y e ar) - N e w r e v e n u e s o urc es b ec a us e of n e w pr o d ucts ( n e ar r e al-ti m e; i n e ur os p er y e ar) -- > e. g. off er b ett er s er vic e l e v el a gr e e m e nts d u e t o 2 4/ 7 a v ail a bilit y - I m pr o v e d e m pl o y e e s atisf acti o n / e n g a g e m e nt - I m pr o v e d c o m pli a nc e d u e t o l o g d at a tr a ns p ar e nc y C alc ul at e c osts / e x p e ns es ( Al b ert h a n d M att er n 2 0 1 7, M ur d oc h 2 0 1 8) - I nv est m e nt c osts u pfr o nt f or fr a m e w or k (i n e ur os) - I nv est m e nt c osts u pfr o nt p er us e c as e (i n e ur os) - Lic e ns e c osts (i n e ur os p er y e ar) - N e w w or kf orc e c osts t o i m pl e m e nt, c o ntr ol, g o v er n, m ai nt ai n R P A (i n e ur os p er y e ar) - Offic e s p ac e c osts f or t h os e p e o pl e (i n e ur os p er y e ar) C o m p ar e b e n efits a n d c osts ( Al b ert h a n d M att er n 2 0 1 7) - D efi n e r e q uir e m e nts a n d s el ecti o n crit eri a ( M ur d oc h 2 0 1 8) - G et m ar k et o v er vi e w ( S m e ets et al. 2 0 1 9) - Pr e-s el ect g e n er all y s uit a bl e pr o vi d ers ( Cz ar n ecki a n d A ut h 2 0 1 8) - D eci d e o n o n e pr o vi d er b as e d o n s el ecti o n crit eri a ( M asó 2 0 1 8) S el e cti o n crit eri a: ( H er m et al. 2 0 2 0, M ur d oc h 2 0 1 8, T a ulli 2 0 2 0, Willc oc ks et al. 2 0 1 9) - S oft w ar e c osts - S kill r e q uir e m e nts - V e n d or s u p p ort - V e n d or r e p ut ati o n - S oft w ar e m at urit y a n d s e c urit y - Sc o p e of f u ncti o ns ( e. g. f u ncti o ns f or exc e pti o n h a n dli n g or t esti n g, pr oc ess disc o v er y t o ol) - E as e of us e / us er-fri e n dli n ess - N extg e n er ati o n c a p a biliti es ( e. g. artifici al i nt elli g e n c e) Kr a k a u et al. (2 0 2 1 ) 2 4 1 1 I niti ati o n 1. 3 B usi n ess C as e C alc ul ati o n 1. 4 S oft w ar e Pr o vi d er S el ecti o n O ut p ut - C alc ul at e d a n d e v al u at e d b usi n ess c as e f or s el ect e d pr oc ess es ( B al as u n d ar a m a n d V e n k at a giri 2 0 2 0) - D ecisi o n w h et h er R P A i m pl e m e nt ati o n pr oj ect s h o ul d b e pr oc e e d e d or st o p p e d - Off ers of v ari o us R P A s oft w ar e pr o vi d ers - S el ect e d R P A s oft w ar e pr o vi d er ( Al b ert h a n d M att er n 2 0 1 7) M et h o ds - R et ur n o n i n v est m e nt c alc ul ati o n ( H er m et al. 2 0 2 0) - Sc ori n g m o d el S ucc ess F act ors - Br o a d p ers p ecti v e o n q u a ntit ati v e a n d q u alit ati v e di m e nsi o ns of b e n efits - R o b ust d at a as i n p ut or v ali d ass u m pti o ns i n c as e of pr e dict e d d at a - B e a w ar e t h at R P A is n ot a " u niv ers al r e m e d y" ( o n e b ot c a n pr o vi d e ti m e s avi n gs, b ut t h er e ar e m a n y b ots n ec ess ar y t o pr o vi d e F T E s avi n gs) - C o nsi d er all ki n ds of pr oc ess st e ps ( e. g. w al ki n g dist a nc es) ( C as e St u d y) - T a k e "s oft" f act ors li k e c o m pli a nc e or us er s atisf acti o n i nt o acc o u nt ( C as e St u d y) - F ast s el ecti o n of a s uit a bl e pr o vi d er ( d o n ot i n v est t o o m uc h eff ort i n pr o vi d er e v al u ati o n f or pil ot i m pl e m e nt ati o n) b ec a us e all m aj or pr o vi d ers ar e o n a si mil ar l e v el a n d m or e c o m pr e h e nsi v e e v al u ati o n c a n b e c o n d uct e d aft er s ucc essf ul R P A pil ot i m pl e m e nt ati o n ( K oc h a n d F e dt k e 2 0 2 0) - Us e tri al v ersi o ns of pr o vi d ers t o g et a f e eli n g f or t h e c a p a biliti es of t h e s oft w ar e ( T a ulli 2 0 2 0) Robotic Process Automation in Logistics Appendix D: Logistics Use Cases for RPA Implementation Krakau et al. 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