i
A s a egy o he in eg a ion o hype -
au oma ion echnologies in o he Po uguese
companies
Da id José Fe nandes Cus ódio
Disse a ion p esen ed as a pa ial equi emen o ob ain he
Mas e ’s deg ee in In o ma ion Managemen
ii
NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
A STRATEGY FOR THE INTEGRATION OF HYPER-AUTOMATION
TECHNOLOGIES INTO THE PORTUGUESE COMPANIES
by
Da id José Fe nandes Cus ódio
Disse a ion p esen ed as a pa ial equi emen o ob ain he Mas e ’s deg ee in In o ma ion
Managemen wi h a specializa ion in Ma ke ing In elligence
Ad iso : PhD Vi o Dua e dos San os
No embe 2021
iii
ACKNOWLEDGEMENTS
My jou ney h ough his disse a ion and his deg ee has equi ed mo e e o han I was able o
deli e on my own. Pu suing a new deg ee while wo king has been he mos challenging ask I´ e
aced. Fo his eason, I would like o acknowledge he many people who suppo ed me h oughou
he p ocess.
Fi s , I would like o hank my hesis ad iso , P o esso Doc o Vi o Dua e dos San os o his
guidance, a ailabili y, hones y, and ime. His suppo no only has con ibu ed o he de elopmen o
his mas e ´s hesis, as also challenge my wo king me hods. I would no ha e comple ed his p ocess
wi hou him. Thank you o you guidance and knowledge.
I would also like o hank o all he expe s ha pa icipa e in he e alua ion o his wo k. Thei inpu
and knowledge we e undamen al and a key pa in his in es iga ion. They a e Alexand a Tene a
(AT), assis an p o esso a UNL/FCT, Sand o Cos a (SC), so wa e consul an and co- ounde o , and
Sand a Pe ei a (SP) head di ec o o logis ic a ATLA Logis ica SA. No ma e he dis ance ha his
“new imes” equi es, you a ailabili y and willingness o help we e much app ecia ed.
Finally, I would like o hank o my pa en s and b o he because wi hou hem any o his would be
possible. The oppo uni ies and sac i ices hey ha e made has been an inspi a ion. Also o my
iends, o hei encou agemen o no lose ack and making su e ha his inal chap e was
w i en. And o Ana, you us in me was he mos impo an mo i a ion ha I could ecei e.
i
ABSTRACT
Today´s compe i i e wo ld demand companies o explo e and disco e new business au oma ion
echnologies in o de o e ol e hei p ocesses and ob ain emendous bene i s, om inc eased
e iciency o educed cos s. The business p ocesses ha cu en ly in ol e a lo o manual wo k o a e
conside ed as non- alue added o he company a e in he lead o au oma e so employees can ocus
hei knowledge in o mo e ele an asks.
The pu pose o his s udy is o p opose a s a egy o he in eg a ion o hype -au oma ion
echnologies in o cu en Po uguese companies’ p ocesses and by his way inc ease he
compe i i eness o he Po uguese companies. An analysis o he subjec and unde s anding he
ele ance o hype -au oma ion p ocesses is conduc ed, as well as a collec ion o in o ma ion om
Po uguese companies o hei au oma ed p ocesses, as he basis o iden i y business needs ha
may be included in a s a egy o apply hype -au oma ion echnologies.
I will be ga he ed ele an li e a u e on he domain being analyzed o building a comp ehensi e
body. The esul s will be analyzed o unde s and in wha ex en Po uguese companies would adjus
om hype -au oma ion echnologies, epo ing he bene i s inhe en o echnological e olu ion and
measu e in which a eas/depa men s he manage s belie e hype -au oma ion will ha e a majo
in luence in he sho - e m.
KEYWORDS
Hype -au oma ion; Robo ic P ocess Au oma ion; A i icial In elligence; Wo k low
Au oma ion
INDEX
1. In oduc ion ............................................................................................................. 9
1.1. Backg ound and p oblem iden i ica ion .......................................................... 10
1.2. S udy obje i es................................................................................................ 11
1.3. S udy ele ance and impo ance ..................................................................... 11
2. Me hodology ......................................................................................................... 13
2.1. Li e a u e Re iew App oach ............................................................................ 14
2.2. Model De elopmen ....................................................................................... 14
2.3. Model E alua ion ............................................................................................ 15
3. Li e a u e Re iew ................................................................................................... 16
3.1. A i icial In elligence ....................................................................................... 16
3.1.1. Concep and A eas o applica ion ............................................................. 16
3.1.2. Machine Lea ning ..................................................................................... 17
3.1.3. Bo s .......................................................................................................... 18
3.2. Au oma ion Technology .................................................................................. 18
3.2.1. Wo k low Au oma ion .............................................................................. 19
3.2.2. Robo ic P ocess Au oma ion .................................................................... 19
3.2.3. In elligen P ocess Au oma ion ................................................................ 19
3.2.4. Indus y 4.0 .............................................................................................. 20
3.3. P ocess Au oma ion ........................................................................................ 20
3.3.1. Business P ocess Managemen ................................................................. 21
3.3.2. Business P ocess In eg a ion .................................................................... 21
3.3.3. Business P ocess Au oma ion ................................................................... 22
3.3.4. P ocess Analy ics ...................................................................................... 22
3.3.5. Hype -au oma ion .................................................................................... 23
3.4. Po uguese companies´ con ex ...................................................................... 24
3.4.1. The challenge o echnological ans o ma ion ......................................... 24
3.4.1. Dis ibu ion o Po uguese companies by sec o o ac i i y ...................... 25
3.4.2. Dimension o Po uguese companies ....................................................... 25
4. S a egy o he in eg a ion o hype -au oma ion echnologies in o he cu en
Po uguese companies’ p ocesses .............................................................................. 27
4.1. Da a collec ion ................................................................................................ 27
4.2. Da a discussion ............................................................................................... 28
4.3. P oposed s a egy ........................................................................................... 31

i
5. E alua ion .............................................................................................................. 42
6. Conclusion ............................................................................................................. 45
6.1. Syn hesis o he de eloped wo k ..................................................................... 45
6.2. Wo k Limi a ions ............................................................................................. 45
6.3. Fu u e Wo k .................................................................................................... 45
Re e ences ................................................................................................................. 47
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LIST OF FIGURES
Figu e 1 – Pe cen age o o al Po uguese companies by sec o o economic ac i i y ...... E o !
Bookma k no de ined.
Figu e 2 – Me hodology h oughou he s udy .......................... E o ! Bookma k no de ined.
iii
LIST OF ABBREVIATIONS AND ACRONYMS
AI A i icial In elligence
BPA Business P ocess Au oma ion
BPI Business P ocess In eg a ion
BPM Business P ocess Managemen
CSF C i ical Success Fac o s
CIP Con ede ação Emp esa ial de Po ugal
ICT In o ma ion and Communica ion Technologies
IoT In e ne o Things
IPA In elligen P ocess Au oma ion
ML Machine Lea ning
NLP Na u al Language P ocessing
RPA Robo ic P ocess Au oma ion
SME Small and Medium En e p ises
WEF Wo ld Economic Fo um
9
1. INTRODUCTION
Acco ding o Wo ld Economic Fo um, mo e han 70 million jobs will su e changes o be comple ely
eplaced by machines and algo i hms o e he nex h ee yea s. Ne e heless, o e e y job
dis up ed, wo news job oles will be c ea ed du ing he same pe iod o ime (Ricoh, 2019). This
migh ge seen as challenging bu also should be aced as a g ea oppo uni y o companies simpli y,
au oma e and be mo e e icien .
Simpli ica ion. Au oma ion. E iciency. Since he beginning o he human his o y, man always had
c i ical hinking on how o make a ask simple , execu e au onomously, and achie e as e wi h
be e esul s. The e olu ion o human ci iliza ion had i s peaks du ing he e olu ion o echnologies
o each e a, om he au oma ion o wa e anspo h ough aqueduc s by he Mayans du ing he
ag icul u al e a, o Fo d´s ins alla ion o he i s mo ing assembly line o he mass p oduc ion o an
en i e au omobile in he indus ial e a, and inally in he digi al e a when companies s a o compile
in o ma ion and de elop hei communica ion echnologies o ake ac ions based on a mo e eliable
base o achie e mo e e iciency h oughou he p ocesses (Cascio & Mon ealeg e, 2016).
As de ined by Ga ne , hype -au oma ion “deals wi h he applica ion o ad anced echnologies,
including a i icial in elligence (AI) and machine lea ning (ML), o inc easingly au oma e p ocesses
and augmen humans. Hype -au oma ion compile a b oad ange o ools ha can be au oma ed,
which equi e agili y o lapida e indi idual au oma ion echnology” (Au oma ion Solu ions, 2019).
The idea is o c ea e new and inc easingly au onomous knowledge, accessible o e e yone and
dissemina e in he o ganiza ion so hey can be pa o he ans o ma ion wi h he in en ion o
esponding o he as -paced echnology g ow h (Cha i, Klamma, Ja ke, & Nae e, 2007).
To simpli y, hype -au oma ion e e s o he mix u e o au oma ion echnologies ha exis o
augmen and expand human capabili ies – “ akes he obo ou o he human” (Be u i & Taglioni,
2017). The mos known e m ega ding he obo iza ion o human asks is obo ic p ocess
au oma ion (RPA), bu hype -au oma ion akes an ecosys em o nex -gene a ion ools and is able o
o ganize hem and c ea e a new way o wo k aking ad an age o each skill ha each au oma ion
echnology has. As a esul , i ´s possible o ake u he s eps and a he han simply use echnologies
like RPA o comple e epe i i e, eplicable, and ou ine asks ha can be w i en as ule-based,
hype -au oma ion can help ha monize wo k be ween people, obo s and sys ems sp eading
compu e iza ion o e e y non- ou ine ask whe e big da a becomes a ailable (Agui e & Rod iguez,
2017).
This will push au oma ion p ocesses om isola ed solu ion o en e p ise-wide solu ions. The
possibili y o join AI o he equa ion hen con ibu es wi h g ea e in elligence decisions o he mix.
This leads o ano he le el o unde s anding o he au oma ion as AI can analyze a lo mo e da a han
a human can, ecognize pa e ns in da a and lea n om pas decisions o make inc easingly
in elligen choices.
Employees and u u e employees need o ake in o conside a ion ha hype -au oma ion can c ea e
a wo kplace hype -connec ed ha is always in o med, agile and able o use da a and insigh s o
quick and accu a e decision-making (Ag awal, Gans, & Gold a b, 2019). In oday's digi al age, whe e
he ecosys ems o machines and humans a e inex icably linked, employees need o cons an ly keep
up o da e wi h hei knowledge and de elop new skills ela ed o using new echnology so hey can
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3. LITERATURE REVIEW
In his chap e , he s udy will ocus on collec ing ele an in o ma ion ega ding di e en au oma ion
echnologies. The goal is o collec and comp ehend a ious echnologies, wha hey b ing, and all o
he possibili ies ha come wi h hem. Au oma ion echnologies a e classi ied in o h ee ypes:
p ocess au oma ion, au oma ion echnology, and p ocess analy ics.
3.1. ARTIFICIAL INTELLIGENCE
The e is no unique and commonly ecognized de ini ion o AI. I is no mally e e ed o as he abili y
o a machine o lea n om expe ience, adap o new inpu s and pe o m human-like asks (Duan,
Edwa ds, & Dwi edi, 2019a). In he ield o compu e science esea ch de ines AI as he s udy o
“in elligen agen s” which a e de ices ha “iden i y hei en i onmen and ake ac ions o maximize
hei chance o success a some goal” (Duan e al., 2019a).
Du ing a con e ence a he Japan AI Expe ience in 2017, Da aRobo CEO Je emy Achin began
add essing he c owd by o e ing he ollowing de ini ion o how AI is used oday: "AI is a compu e
sys em ha can do asks ha would no mally need human in elligence... Many o hese AI sys ems
a e based on machine lea ning, while o he s a e based on deep lea ning and o he s a e based on
ai ly mundane hings like ules."
AI can be summa ized as a science o aining compu ed sys ems o mimic human asks h ough
lea ning and au oma ion. Au oma ion has always been used o inc ease p oduc i i y, inc ease sa e y
and make be e use o esou ces. Today, i is no only au oma ing by augmen ing human powe wi h
machine powe – i has been au oma ing by pe o ming asks and making decisions h ough
algo i hms de i ed om p ocessing immense quan i ies o da a (SAS, 2020).
3.1.1. Concep and A eas o applica ion
The ise o AI in ecen yea s has been made i an inc easingly popula and g owing ield in compu e
science, as i has allowed o imp o e he quali y o human li e in many a eas and acili a ing hei
wo k. Wi h he as paced g ow h o Big Da a echnologies and AI in he pas wo decades, he
wo king en i onmen has seen signi ican adjus men s in asks and imp o emen s in he
pe o mance o se e al ields, such as human esou ces, manu ac u ing and se ice sys ems (Duan,
Edwa ds, & Dwi edi, 2019b). The applica ion a eas o AI a e also ha ing a majo impac on a ious
o he opics in li e, as he s udy and cons an g ow h o AI echnology is widely used oday o sol e
mo e complex p oblems in a ious a eas such as science, inance, enginee ing, medicine and o he s
ields (Mi al & Sha ma, 2021) whe e wo decades ago people would se iously ques ioning he
capabili y o echnology o ac in such complex and delica e p ocesses.
Today, AI echnology is being employed in se e al a eas wi h many applica ions. The main a eas a e:
A) Language unde s anding – he abili y o a machine o ha e a eading comp ehension and
espond o he na u al language.
a. Speech unde s anding;
b. Ques ioning and Answe ing;

17
c. Language ansla ion.
B) Lea ning and adap o di e en sys ems – he abili y o a machine o lea n and adap o
di e en sys ems based on pas expe iences.
C) P oblem sol ing – he abili y o a machine o iden i y a si ua ion, de ine he p oblem and
p opose se e al solu ions.
a. Au oma ion o w i ing;
b. In e ac i e P oblem Sol ing;
c. Heu is ic p oblems.
D) Pe cep ion – he capabili y o a machine o in e p e da a in a manne simila o he way
human uses hei sense o ansla e he knowledge om he wo ld.
a. Pa e n ecogni ion.
E) Robo s – a combina ion o he di e en abili ies abo e desc ibed wi h he capabili y o mo e
and shape objec s.
a. Mili a y;
b. Deli e y;
c. Secu i y;
d. Na iga ion;
e. Explo a ion
. O he (mining, a ms, e c.)
F) Games – he simples way o AI applica ion whe e a se o ules is explained o he machine
and wi h hese ins uc ions i ha e he abili y o p oblem sol e he di icul ies p esen by he
opponen s.
a. Chess, Checke s, e c.
3.1.2. Machine Lea ning
ML has ep esen ed an ins umen al ole o he ad ance o bo h da a analysis and AI (Chen & Liu,
2018). ML algo i hms ha e been applied in almos all a eas o compu e science, na u al science,
enginee ing, social sciences, and beyond. The applica ion o ML p ac ices is e en mo e dissemina e
and ha e been ocused o sol e p oblems whe e hey can lea n om da a and enhance hei esul s
and accu acy o e ime wi hou he need o be p og ammed (Law & Von Ahn, 2011). ML algo i hms
ha e enabled he c ea ion and g ow h o many indus ies, and many o hem would no ha e been
c ea ed o flou ished, e.g., In e ne comme ce and Web sea ch.
ML algo i hms can disco e unexpec ed simila i ies be ween old and new da a h ough “ aining”,
aiding he compu e iza ion o asks o which big da a has newly become a ailable (B ynjol sson &
18
McA ee, 2011). A se o da a a e ained o ind pa e ns o be eliable o ea u e in massi e amoun s
o new da a and o be able o p edic and make decision upon ha da a. The be e algo i hm, he
mo e accu a e will be he p edic ions imp o ing he esul s.
ML is e e ywhe e and hei p ac ical applica ion can be no iced e e y day. He e a e a ew examples
o ML:
A) F om digi al assis an s (Amazon Alexa, Apple Si i, Nio Nomi) wi h he capabili y o lea n and
sol e na u al language p ocessing (NLP) p oblems such as p ocessing ex and oice da a
enabling he unde s anding o human speech;
B) Recommenda ions o use s based on wha hey ha e sea ch and iewed h ough deep
lea ning echnology;
C) Cha bo s also use NLP wi h pa e n ecogni ion o deciphe inpu ex and hando e he mos
con enien esponse;
D) Sel -d i ing ca s use machine lea ning and deep lea ning o cons an ly iden i y images a ound
he ca and p edic he beha io s o all “ oad agen s”.
3.1.3. Bo s
The e m bo is an abb e ia ion o obo and e e s o so wa e applica ion ha is p og ammed o
pe o m ce ain asks au onomously wi hou a human ope a o . They execu e asks acco ding o
ins uc ions gi en by humans. Bo s a e in ended o eplica e human beha io bu wi h g ea e
ou comes in pe o mance, less e o s and as e han a human employee could do (Gian ecchio, Xie,
Wu, & Wang, 2008).
The e a e se e al ypes o bo s, and each one wi h di e en applicabili y, goals, and asks. Some
examples include: 1) cha bo , which is a p og am ha alk wi h use s simula ing a human
con e sa ion. 2) social bo s, ope a es in social media pla o ms. 3) shop bo s, which is a p og am ha
ga he in o ma ion on in e ne and gi e he bes choices wi h he bes p ices o he sea ched
p oduc . 4) knowbo , collec knowledge o a use by au oma ically isi a si e and e ie e
in o ma ion ha mee s a ce ain equi emen p ede ined.
3.2. AUTOMATION TECHNOLOGY
Au oma ion echnology is he main indus y on he way o Indus y 4.0, which can be ansla ed in o
he cu en au oma ion o adi ional manu ac u ing and indus ial me hods, using mode n,
in elligen , and inno a i e echnology combined wi h he abili y o connec di e en echnologies in
he comple ion o a p ocess (Ha monic D i e SE, 2021). The e m means in elligen and digi ally
in e connec ed sys ems ha allow independen indus ial p oduc ion. In a sma ac o y, humans,
p oduc s, and logis ics a e all in e connec ed h ough communica ion channels, an app oach ha is
some imes seen as he ou h indus ial e olu ion (Jona hon, Da id, Chad, & Da id, 2014).
19
Au oma ion echnologies compile all wo k p ocesses and equipmen ha allow plan s and sys ems o
unc ion au oma ically and independen ly. Human in e en ion is minimal. The highe he deg ee o
au oma ion, he less in e en ion needs o be pe o med by humans o con ol and secu e he
p ocess. AI, ML and RPA a e some o he new au oma ion echnologies.
3.2.1. Wo k low Au oma ion
Wo k low can be de ined as s uc u ed ac i i ies o asks ha uns in ypical business in o ma ion
sys ems. These sequen ial ac i i ies ha p ocesses a se o da a, usually in ol e se e al da abase
sys ems, use in e aces, and applica ion p og ams and any ime da a is mo ed be ween humans
and/o sys ems, a wo k low is c ea ed(Singh & Huhns, 1994). I can be summa ized as a pa h
desc ibing how some hing goes om being incomple e o comple e.
When au oma ion echnology i s applied on hose s uc u ed p ocesses, machines suppo he
g oups o humans o pe o m he asks in ol ed in he wo k low. In some cases, he p ocesses can be
ully au oma ed and he e o e machines will eplace human ac i i ies, i.e., in oice ex ac ion h ough
RPA.
Nowadays he e a e al eady p ocess-managemen so wa e ools ha allows use s o ini ia e and
ack he s a us o wo k low p ocess in eal ime. I will manage he human use s and obo ’s
ela ionship and p o ide s a is ical da a ega ding he end- o-end p ocess and he bo lenecks
(Be u i, Nixon, Taglioni, & Whi eman, 2017). Wi h wo k low au oma ion, companies will be able o
educe he amoun o manual ac i i ies done by employees – educing he isk o mis akes –,
conclude mo e wo k in a sho e pe iod o ime and help companies achie e mo e consis en esul s.
3.2.2. Robo ic P ocess Au oma ion
A ounda ional ques ion o many companies and business p ocess owne s is “Wha should be
au oma ed?”. This ques ion is no new and de elopmen s s udies in ML, AI, da a science o ce o
e isi his ques ion con inuously (W. M. P. an de Aals , Bichle , & Heinzl, 2018). RPA can be
in e p e ed as he echnological eplica o a human employee wi h he goal o au oma ing s uc u ed
ac i i ies in a cos e icien and as app oach (Agui e & Rod iguez, 2017).
Al hough he e m “ obo ” can b ing o he imagina ion o a humanoid c ea u e wi h an appea ance
simila o human beings, i is impo an o unde s and ha RPA is no a physical obo , i is a
so wa e based solu ion ha is buil o ca y ou epe i i e and ules-based ope a ional jobs used o
be done by humans (M. C. Laci y & Willcocks, 2016).
3.2.3. In elligen P ocess Au oma ion
The e m In elligen P ocess Au oma ion (IPA) is a combina ion o au oma ion echnologies wi h
ocus on a limi ed se o asks, building on op o i e di e en co e echnologies: RPA, Wo k low
Au oma ion, ML, Na u al-Language Gene a ion and Cogni i e Agen s.
In simple e ms, IPA “b ings he obo ou o human” by pe o ming wo k p e iously made by
humans bu in a mo e e ec i e and as es o m (Be u i & Taglioni, 2017). I is a collec ion o
business p ocess imp o emen s and new echnologies used o au oma e and op imized exis ing
20
asks. Ha ing ML as one o i s co e echnologies allows IPA o lea n he ac i i ies pe o med by
humans and o e ime (and quan i y o da a p ocessed) will execu e hem be e due o i s abili y o
lea n om pas beha io , and adap o change o become mo e e icien (Lau en , Cholle , &
He zbe g, 2015).
3.2.4. Indus y 4.0
Indus y 4.0 s ands o he ou h indus ial e olu ion which is he ongoing au oma ion o his o ic
manu ac u ing and indus ial p ocesses, using mode n echnologies such as IoT, Cloud based
Manu ac u ing, Indus ial In e ne , and Sma In e ne in eg a e he i ual wo ld wi h he physical
wo ld.
In oday´s indus y, he manu ac o y indus ies a e shaping owa ds a cus omiza ion o p oduc s
a he han mass p oduc ion (Vaidya, Ambad, & Bhosle, 2018). To ul ill his demand o an
inc easingly indi idual cus ome need, companies a e ocusing on hei p ocess digi aliza ion. The
ga he ed da a can be used o op imize companies’ pe o mance, o p o ide guidance ega d he
company en i onmen and wha companies can do o be e app oach hei cus ome s. Indus y 4.0
in ends o educe human con ac in he manu ac u ing p ocess bu alues i s knowledge inpu , in
o de o ha e con inuous imp o emen and be able o ocus on ac i i ies ha can add alue o he
company and a oid losses (Lasi, Fe ke, Kempe , Feld, & Ho mann, 2014).
3.3. PROCESS AUTOMATION
The ad ancemen o echnology helps o ind solu ions o he basic needs o socie y and indus ies
ha un il hen we e conside ed impossible o sol e (M. Laci y, Willcocks, & C aig, 2015). This
echnological de elopmen allows companies o main ain hei cu en compe i i e ad an ages and,
in many ways, c ea e new p ocedu es o s ay ahead compa ing o di ec compe i o s, while also
imp o ing p ospe i y, well-being and he abili y o ocus on mo e c ucial decisions o he
o ganiza ion's success (M. Laci y e al., 2015).
Wi h echnological g ow h, he ele ance and impo ance o p ocess au oma ion in companies has
inc eased d ama ically. Among in o ma ion, communica ion and au oma ion echnologies,
adi ional in e connec ion ba ie s a e, in he ope a ional con ex , g adually disappea ing. Cu en ly,
companies a e mo e a ailable o change hei in e nal p ocesses, gi ing mo e alue o inno a i e
sys ems ha can now supe ise and con ol inc easingly complex wo k, gua an eeing accu acy and
sa e y, and p o ide suppo o he implemen a ion o inspec ion s a egies. ad anced. O ganiza ions
need o ake a di e en and mo e comp ehensi e app oach o issues o quali y, cos and ime, and
au oma ion enginee ing will play a cen al ole in his change (Jämsä-Jounela, 2007).
In he case o he manu ac u ing indus y, he implemen a ion o an au oma ic p ocess con ol
ensu es ha wo ke s can con inue o ope a e egula ly and always wi hin he mos p o i able ange,
a oiding bo lenecks - he poin o conges ion in a p oduc ion sys em -, leading o mo e consis en
p oduc ion in g ea e quan i y, eliabili y, yield, and quali y using less ene gy and in a sho e pe iod
21
o ime. This echnology will inc ease e iciency, help o inc ease p oduc i i y, imp o e he quali y o
wo ke s as hey will ha e g ea e elie o he achie emen o hei s and a educ ion in cos s (Lo enz
& Schmid , 1989). These consequences will allow companies o boos hei p o i s (M. Laci y e al.,
2015).
3.3.1. Business P ocess Managemen
The cons an changes on economic en i onmen ha e led o an inc easing in e es in imp o ing
o ganiza ional business p ocesses o enhance pe o mance and de elop hei ine iciency. One o he
s udy ields dealing wi h hese challenges is business p ocess managemen (BPM).
The BPM discipline explo e me hods and echniques o o m business p ocesses in an e icien and
e ec i e manne (Mendling, Hull, Webe , & Hull, 2018). Any combina ion o me hods used o
conduc a company's business p ocesses is BPM and a key idea is ha i engages he imp o emen o
business p ocesses by edesigning in o ma ion sys ems o bes suppo he people who a e wo king
in he p ocess.
Despi e conside able in es men in he a ea, mos e iews epo ha he g ea majo i y o BPM
ini ia i es ha ing been unsuccess ul (Abdol and, Albad i, & Fe dowsi, 2008). I is he e o e no
su p ising ha he indus y is eluc an ha a business p ocess plan could b ing posi i e changes and
beha io h oughou he companies o ha e signi ican impac angible, and measu able bene i s.
Because hese changes we e uled as isky and p one o ail, he e was a gap need o be illed and o
cla i y, i was in es iga ed wha could be poin as c i ical success ac o s (CSF). The mos commonly
CSFs o BPM included in li e a u e a e he ollowing: op managemen suppo , p ojec
managemen , p ojec champions, communica ion and in e -depa men al coope a ion, and end-use
aining (A iyachand a & F olick, 2008). Top managemen is o en conside ed o be he mos
in luen ial because i mus ini ia e and suppo BPM e o s o be implemen ed and assu e i is
con inued. Any isola ed conside a ion o he p e ious ci ed aspec s will yield subop imal conclusions
(T kman, 2010).
The la es ad ancemen s a ound AI, ML, c yp og aphy, and dis ibu ed sys ems ha e p o ided he
ounda ions o new echnologies, such as RPA, cha bo s, sel -d i ing ca s, sma objec s,
blockchains, and he IoT. I is di icul o p edic in which speci ic way hey will shape BPM, bu he
ising upg ade o hese echnologies will likely in luence how o ganiza ions design and execu e
business p ocesses in he u u e.
3.3.2. Business P ocess In eg a ion
Acco ding o he In e na ional Con e ence on e-Business, we can desc ibe BPI as “a key p ocedu e o
suppo business in e ope abili y. Focused and p ocess-o ien ed o ganiza ions a e con inually
in eg a ing hei p ocesses, and he in eg a ion o in e nal and ex e nal p ocesses is also he
essen ial poin o a ious ypes o business p ocess eenginee ing o op imiza ion asks, as well as
logis ics planning e o s (Be en e, Vandenbosch, & Aube , 2009). BPI has he bene i o allowing he
au oma ion o business p ocesses and he in eg a ion o di e en sys ems in a ious o ganiza ions
(Aouach ia, Gonzalez-Hue a, Ja ie Réda Ghoma i, Abdessamed Hadaya, & Leshob, 2017). I is
impo an o dis inguish be ween in eg a ion in he con ex o a BPM pla o m - a unc ion ha
allows so wa e o combine da a be ween o he sys ems - and in eg a ion in he con ex o a BPI -

22
which occu s when se e al business p ocesses in e connec ed in a e ical hie a chy wo k oge he
o mee de ined business objec i es.
Businesses ha wan o connec sys ems and in o ma ion e icien ly mus use BPI. As p e iously
s a ed, BPI enables he au oma ion o business p ocesses, he in eg a ion o a ious sys ems and
se ices, and he secu e sha ing o da a ac oss mul iple pla o ms ac oss he en i e o ganiza ion.
O e coming he challenges o in eg a ion allows o ganiza ions o connec sys ems in e nally and
ex e nally. Addi ionally, BPI also allows he au oma ion o logis ics, managemen , ope a ional and
suppo p ocesses(MuleSo , 2020). Today, so wa e ha enables he in eg a ion o business
p ocesses is no only a ailable o la ge companies and his makes i possible o all companies o gain
an ad an age o e compe i o s, because all he headaches caused by he challenges o in eg a ion,
including ime and ene gy o boos new business, hey will be minimized by he op ion o acqui e he
necessa y so wa e o le e age he business.
3.3.3. Business P ocess Au oma ion
A business p ocess is he collabo a i e execu ion o business asks in acco dance wi h a se o
business ules de ined o achie e p e iously s a ed o ganiza ional goals. The p ocess mus speci y
only one inpu and one ou pu . All ac o s ha con ibu e o adding alue o a gi en p oduc o
se ice (di ec ly o indi ec ly) a e called inpu s. These condi ions can be ca aloged in he
managemen , ope a ional and suppo business p ocesses. Se e al sec o s, om consume se ices
o indus ial companies, ha e applied e o s o es uc u e and eenginee hei business p ocesses
o achie e cos educ ion and imp o e e iciency. Wi h he apid g ow h o echnology leading o an
upg ade o oday's compu e s and in o ma ion echnology, di icul business p ocesses can now be
au oma ed, asks ha we e adi ionally pe o med manually and conside ed o be o a high deg ee
o complexi y a e now po en ially au oma ed h ough o in elligen machines o sys ems. This b ings
many new challenges o he equa ion: 1) Business p ocesses need o be easily c ea ed, mapped,
implemen ed, and upda ed. 2) Business p ocesses need o be de eloped in an in e ope able way, so
ha di e en o ganiza ions and depa men s can in eg a e hei business p ocesses. 3) The
co ec ness and secu i y o a business p ocess needs o be gua an eed. 4) Me hodological ools a e
needed o acili a e he managemen and euse o business p ocesses (Bose e al., 2012).
3.3.4. P ocess Analy ics
P ocess analysis is a new ype o analysis ha no only shows how p ocesses a e wo king and how
well hey a e pe o ming, bu also wha needs o be changed o help hem pe o m be e . Wi h he
help o RPA and o he au oma ion echnologies, i will enhance he p ocess e o s, i s e ec i eness
and ocus on impo an s eps (Celonis, 2020).
3.3.4.1. P ocess Mining
To be e comp ehend wha p ocess mining is, an analogy wi h a X- ay can help o esol e o
unde s and wha i is abou , whe e X- ays e eal how he p ocess is ca ied ou and can also be used
o ecognize p oblems ( ailu es in p ocess) and pu pose ea men (changes o he p ocess o
imp o e). A e consul ing, i is possible o e i y ha p ocess mining is conside ed an analy ical
me hod o ecognize, moni o and de elop eal p ocesses as hey a e, collec ing knowledge o
ac i i ies pe o med by people, machines and so wa e (W. Van De Aals , 2012b). P ocess mining is
23
based on ac s ob ained h ough collec ed da a, allowing o p o ide i s use s a g ea e unde s anding
o i s da a, g ea e suppo in decision making and be e analysis o business p ocesses.
E en hough he amoun o da a p ocessed and s o ed by in o ma ion sys ems in e en logs is
cu en ly much g ea e han hey e e we e, and wi h exponen ial g ow h, manage s con inue o
make hei decisions based on g aphs and ables, p ecluding analysis. mo e igo ous and ca e ul
in o ma ion a ailable (W. Van De Aals , 2012a). P ocess mining so wa e allows o ganiza ions o
cap u e in o ma ion om business ansac ion sys ems and, hus, p epa e mo e elabo a e and
de ailed epo s based on he da a sa ed on how he main p ocesses a e being execu ed. E en logs
a e c ea ed when he ask is inished - an o de is ecei ed, a pu chase is made, a paymen is made -
and he logs allow hese s eps o be sa ed (Da enpo & Spanyi, 2019). Because he logs allow you o
s o e hese e en s, a pos -p ocess analysis allows you o s udy and unde s and i o c ea e key
p ocess pe o mance indica o s. In his way, o ganiza ions will ha e he possibili y o analyze he
p ocess, e alua e i s pe o mance and ocus on he mos impo an s eps o con inuously imp o e
he execu ion o he p ocess.
As p ocess mining has been e y impo an in assessing he p ocesses o o ganiza ions, iden i ying
a eas o imp o emen , allowing hem o imp o e and make hem mo e e icien , and makes i an
e ec i e pa ne o ools such as AI, which can ind he easons o pe o mance luc ua ions o RPA,
as i can i s iden i y he bes places o implemen “bo s” and, as a esul , p o ide calcula ion
me hods on he impac o implemen ing RPA on p ocesses.
I can be concluded ha p ocess mining ep esen s an a ac i e iew o manage s, based on da a
ob ained om he pe o mance o he p ocess. This will a ac he in e es o senio execu i es, who
can easily isualize and iden i y whe e he p oblems and possible oppo uni ies a e. This will make i
possible o ein o ce an o ganiza ion's dedica ion and objec i es in decision making based on eal,
cohe en , and eliable da a, con ibu ing o he imp o emen o de icien asks, and inc easing he
impo ance o au oma ion o hese asks.
3.3.5. Hype -au oma ion
The e m "au oma ion" oday e e s o he use o echnology ha allows you o pe o m asks ha
we e p e iously epe i i e and highly manual o ha equi ed a signi ican amoun o human e o
wi hou making a signi ican con ibu ion o he o ganiza ion. These ypes o asks we e pe cei ed
ea ly on as a pe ec oppo uni y o au oma ion in gene al - and RPA in pa icula - since au oma ing
hem helps o minimize epe i i e and demo i a ing wo k o employees, sa e aluable ime o
employees and allow hem o ocus on highe alue wo k ini ia i es, con ibu ing o he
o ganiza ion's p ospe i y (Pa ick, 2019).
Hype -au oma ion compiles di e en componen s o p ocess au oma ion, managing o in eg a e
e e y hing om ools o new echnologies ha enhance he abili y o au oma e wo k. Hype -
au oma ion has i s o igin in RPA echnology and expands he au oma ion capaci y wi h new
echnologies in g ea g ow h such as AI, p ocess mining, analysis, decision managemen and NLP, and
o he applica ions mo e ad anced de ices can la e be used oge he in an end- o-end au oma ion
solu ion (Pa ick, 2019).
24
When i comes o hype -au oma ion, one should a oid alling in o he e o o jus conside ing a
mo e ad anced and sounde name o pe o ming ask au oma ion. I is no jus abou implemen ing
ools o manage asks and make hem simple , as e , and mo e e ec i e. I equi es collabo a ion
be ween humans as well. This is because i is humans who mus make he undamen al decisions o
he g ow h o o ganiza ions and he esponsibili y o use echnology o in e p e da a (Pa ick, 2019).
The idea is o au oma e mo e, and inc easingly complex asks, in ol e e e yone in he o ganiza ion
o sha e hei knowledge/inpu so ha e e yone is pa o he ans o ma ion and dissemina es i
h oughou he o ganiza ion.
I is impo an o men ion ha despi e bo h hype -au oma ion and IPA le e age AI and ML o
enhance p ocesses he e is a signi ican di e ence be ween he wo e ms. While IPA in en ion is o
au oma e and inc ease e iciency on exis ing asks, hype -au oma ion i is a consolida e business
s a egy o c ea e and op imize end- o-end p ocesses aiming o inno a e business p emises
(McHugh, 2021).
3.4. PORTUGUESE COMPANIES´ CONTEXT
On his sec ion, i is p esen ed ac s ega ding he ac ual economy su ounding Po uguese
companies and se e al opics will be app oach, such as he challenge o echnological
ans o ma ion, he dis ibu ion o Po uguese companies by sec o o ac i i y, he dimension o
Po uguese companies, he geog aphical dispe sion o Po uguese companies, and he
cha ac e iza ion o sec o s o ac i i y wi h he po en ial o adop hype -au oma ed p ocesses.
Acco ding o he Con ede ação Emp esa ial de Po ugal (CIP), Po ugal should see he cu en
echnological e olu ion as a g ea oppo uni y. Digi iza ion mus be seen as he engine ha will lead
he na ional economy on a pa h o compe i i eness and g ow h, due o he p oduc i i y gains ha i
can boos (Con ede ação Emp esa ial de Po ugal, 2019). Companies mus be p epa ed o ca y ou
ac ions aligned wi h a s a egic ision o he echnological e olu ion o capi alize and be ahead o
he upcoming changes h oughou he wo k en i onmen .
3.4.1. The challenge o echnological ans o ma ion
Digi al echnology, au oma ion and inno a ion a e impac ing all indus y sec o s by imp o ing
p oduc i i y and he consume expe ience.
The s udy on “Au oma ion and he Fu u e o Wo k in Po ugal”, p omo ed by CIP and p epa ed in
pa ne ship wi h he McKinsey Global Ins i u e and NOVA School o Business and Economics,
concludes p ecisely ha au oma ion can p o ide he necessa y injec ion o p oduc i i y, being one o
he main solu ions o coun e he end o slowdown in GDP due o he decline demog aphic.
Fo his po en ial o be ealized, i needs mo e in es men , indispensable o inco po a ing
echnological inno a ion in p oduc s, se ices, and p ocesses. Ano he c i ical ac o in his
echnological ans o ma ion ha he companies in Po ugal mus ace and manage, on a la ge scale,
is in he quali ica ion and equali ica ion o p o essionals. Requali y i s asse s and ec ui new
25
employees who ha e an app op ia e skills p o ile will be essen ial o he companies who wan o
ha e success in his change because i will equi e quali y p o essionals o know how o wo k and
adap o new p ocesses and au oma ed asks.
3.4.1. Dis ibu ion o Po uguese companies by sec o o ac i i y
By he end o 2019, he Po uguese economy was ep esen ed by a o al o 1.335.006 companies.
Rega ding he dis ibu ion o his companies by sec o o ac i i y, he mos ep esen ed speci ic
sec o o ac i i y is wholesale and e ail ade wi h 16,4% o o al en e p ises, ollowing by
ag icul u e, a ming o animals, hun ing, and ishing wi h 9,8%, accommoda ion and ood se ice
ac i i ies wi h 8,8%, human heal h and social wo k ac i i ies wi h 7,6%, cons uc ion companies wi h
6,8%, manu ac u ing, mining, and qua ying wi h 5,2%, and educa ion wi h 4,4%. Rep esen ing less
han 8% ( o al o 103.642 companies) i is eal s a e en e p ise wi h 3,7%, anspo a ion and s o age
companies wi h 2,3%, inancial and insu ance ac i i ies wi h 1,2% and he mino i y sec o is
espec ing o elec ici y, gas, and wa e wi h 0,4%. Ne e heless, abou a hi d o o al companies
wo ks in a di e en sec o o ac i i y han he ones p e iously men ioned (PORDATA, 2019). On
Figu e 3 ep esen ed on he below ba cha i is possible o isualize he ep esen a ion o
pe cen age o o al Po uguese companies by sec o o economic ac i i y.
Figu e 3 – Pe cen age o o al Po uguese companies by sec o o economic ac i i y
3.4.2. Dimension o Po uguese companies
The Na ional Ins i u e o S a is ics ollows a ecommenda ion o he Eu opean Commission o 6 May
2003 o dis inguish he cha ac e is ics o a mic o, small, medium, and big company (PORDATA, 2019).
• Mic o company: employs less han 10 people and whose annual u no e o annual balance
shee does no exceed 2 million eu os.
• Small company: employs less han 50 people and has an annual u no e o annual balance
shee ha does no exceed 10 million eu os.
32
P ocess Au oma ion se backs:
1) Business P ocess Managemen :
a. Al hough a conside able amoun ha e been in es ed in he a ea, mos e iews
epo ha he g ea majo i y o BPM ini ia i es ha ing been unsuccess ul
(Abdol and, Albad i, & Fe dowsi, 2008), and o his ac changes o be made a e
uled as isky and p one o ail.
b. I is undamen al op managemen o he company a e suppo i e and ini ia e
BPM e o s o be implemen ed and ollowed.
c. To implemen BPM i mus ha e a design and execu e o he p ocesses o be
in oduced and managed.
2) Business P ocess In eg a ion:
a. The e is a need o in eg a e hei p ocess in e nal and ex e nally.
b. I mus be able o in eg a e di e en sys ems.
c. Business p ocesses in e connec ed in a e ical hie a chy.
d. O e come he challenges o he in eg a ion.
3) Business P ocess Au oma ion:
a. Business p ocesses need o be easily c ea ed, mapped, implemen ed, and
upda ed.
b. Business p ocesses need o be de eloped in an in e ope able way.
4) P ocess Analy ics:
a. The cos associa ed o he implemen a ion o he p ocess analy ics so wa e such
as P ocess Mining.
5) Hype -Au oma ion:
a. Compila ion o di e en componen s o p ocess au oma ion, managing o
in eg a e e e y hing om ools o new echnologies.
b. Ca ies a high cos o cus ome s o implemen his kind o echnology.

33
Fu he mo e, conce ning he su ey ou come is possible o s a e he ollowing assump ions:
• Roughly h ee qua e s o he answe s we e made by big companies, and i ´s expec ed ha
due o i s bigge economy s eng h hey a e mo e p one o adop he p oposed s a egies
and likely o in es mo e capi al in o i .
The p oposed s a egy is di ided in h ee di e en analyses: implemen a ion on SME,
implemen a ion on Big Companies, and i s cos /complexi y. Fo each analysis, i will be gi en a sco e
(inadequa e, ai ly adequa e, adequa e) o each company sec o and espec i e e alua ion o he
in oduc ion o au oma ion echnologies and p ocess au oma ion. The s a egy can be easily adap ed
and implemen ed by any company.
Fi s i will be p esen ed a able ega ding he implemen a ion o such echnologies and au oma ion
p ocesses on SME. A e wa ds i will be e e ing o Big Companies, and las ly i will be an analysis
abou he cos /complexi y o hose implemen a ions. The sco e gi en will be e alua e ega ding
di icul y/complexi y and he cos o he implemen a ion o each echnology, and he cons ains a e
he same o SME o Big Companies, howe e i can be mo e ele an o less ele an depending o
each company size.
As i can see below on Table 2, i is compiled in o ma ion ega ding SMEs whe e we can see he
companies’ sec o s on columns and he au oma ion echnologies and p ocess au oma ion on ows.
The analysis and sco e o he echnologies will be he p oposed as he ollowing:
Au oma ion Technology:
• Wo k low Au oma ion - mainly due cos o acquisi ion o a p ocess-managemen so wa e
and he necessi y o ha e an o ganized da abase wi h a ull knowledge o he company we
gi e a sco e 2 (because o hei use ulness) o inancial and human esou ce sec o , bu a
sco e 1 o indus ial and comme cial because i would equi e mo e e o and complexi y.
• Robo ic P ocess Au oma ion - conside ing a mo e s anda d au oma ion echnology p oduc in
he ma ke , he acquisi ion and implemen a ion o such so wa e would be a o dable o
SME. Howe e , we ecognize ha in oduce his echnology in he inancial and human
esou ce sec o is easie o adap han o indus ial and comme cial a ea. Conside ing he
p e ious s a emen s, we ga e a sco e 3 o inancial and human esou ces, a sco e 2 o
indus ial sec o and a sco e 1 o comme cial sec o (mo e ules needed equals o mo e
complexi y)
• In elligen P ocess Au oma ion - because he main di icul y o implemen i is he ac ha
his echnology is mo e expensi e han RPA, we conside as sco e 2 o inancial and human
esou ces sec o s, and a sco e 1 o indus ial and comme cial sec o s. The easons o he
indus ial and comme cial sec o s ha e a lowe sco e han he o he s a e simila o he ones
o he implemen a ion o RPA in hese indus ies (mo e complex o adap ).
• Indus y 4.0 - Fo he easons men ioned abo e ega ding he conside a ions om he
li e a u e e iew, bo h inancial and human esou ces depa men s a en´ conside ed o his
au oma ion echnology, and we a ibu e a sco e 1 o indus ial and comme cial because o
he cos o implemen hose mode n echnologies in SME.
34
P ocess Au oma ion:
• Business P ocess Managemen - The implemen a ion pe se doesn´ equi e a signi ican
amoun o money o be execu able. The mos cons ains a e ime, e o , and p edisposi ion.
Fo his eason, we see a ibu e a sco e 3 o his implemen a ion o all sec o s (excep o
indus ial due o i s complexi y – sco e 2). Fo hese BPM changes o be succeeded i is
c ucial ha leade s acknowledge he impo ance o hei ole in hese asks o design,
implemen , and ollow-up he p ocess.
• Business P ocess In eg a ion - The p e equisi e o an in eg a ion o di e en sys ems in he
company can be di icul and/o expensi e o SME o accomplish. Fo his eason, bu ha ing
in conside a ion he added alue o such p ocess we e alua e as a sco e 2 o all sec o s.
• Business P ocess Au oma ion - Fo his s a egy, he e is a need o iden i y and c ea e a low
o ac i i ies and make i in e connec ed h ough he company. We conside his app oach
simple enough o SMEs o implemen bu wi h a somewha high p ice o hem. Due o his
eason, we conside ed a sco e 2 o implemen his s a egy on all sec o s.
• P ocess Analy ics - The only ba ie we ha e iden i ied o in oduce his s a egy in SME is he
cos associa ed o so wa e´s acquisi ion. Ne e heless, he bene i s o his echnology whe e
i can e eal how he p ocess is ca ied ou and can also be used o ecognize p oblems
( ailu es in p ocess) and pu pose ea men (changes o he p ocess o imp o e) and because
o his we classi y i as a sco e 3 o all sec o s.
• Hype -Au oma ion - Fo his echnology, once again he bigges impedimen o all SME adop
hype -au oma ion is i s high cos s o implemen a ion ega ding in eg a ion o di e en ools,
p ocesses, and echnologies. Bu he ad an ages ha SME can ake om he implemen a ion
a e eno mous. In his sense, we ha e a ibu ed o all sec o s sco e 2 ( ai ly adequa e).
35
Table 2 – S a egy implemen a ion on SME
SME
Companies Sec o s
Financial
Human Resou ces
Indus ial
Comme cial
Au oma ion Technology
Wo k low Au oma ion
2
2
1
1
Robo ic P ocess Au oma ion
3
3
2
1
In elligen P ocess Au oma ion
2
2
1
1
Indus y 4.0
-
-
1
1
P ocess Au oma ion
Business P ocess Managemen
3
3
2
3
Business P ocess In eg a ion
2
2
2
2
Business P ocess Au oma ion
2
2
2
2
P ocess Analy ics
3
3
3
3
Hype -au oma ion
2
2
2
2
Sco e
Meaning
1
Inadequa e
2
Fai ly Adequa e
3
Adequa e
Following on Table 3, i is compiled in o ma ion ega ding Big Companies (o e 250 employees)
whe e we can see he companies’ sec o s on columns and he au oma ion echnologies and p ocess
au oma ion on ows. The analysis o he echnologies will be iden ical o he ones ega ding SME, bu
wi h a di e en impac on sco e o hose echnologies.
36
Au oma ion Technology:
• Wo k low Au oma ion – he main se back o his echnology o SME we e conce ning o i s
cos o acquisi ion o a p ocess-managemen so wa e and he necessi y o ha e an
o ganized da abase. Fo big companies, we do no conside he cos s as an obs acle o he
implemen a ion. Ne e heless, ha ing an o ganized da abase elies on good managemen ,
bu we also ha e conside ed ha o a company become esponsible o o e 250
collabo a o s i equi es hese hu dles esol ed. In his sense, we ga e a sco e 3 o all sec o s
(p oblem o money is sol ed).
• Robo ic P ocess Au oma ion – RPA is a echnology p esen ed in mos o big companies,
whe e i is used on inancial a ea (i.e., in oices ex ac ion), human esou ces (au oma e
p ocesses wi hin exis ing digi al HR sys ems, such as SAP, Wo kday), o in indus ial sec o
(in en o y epo s), and o his eason we conside ed a sco e 3. Rega ding comme cial
sec o ial, due o i s complexi y we ha e ga e a sco e 1.
• In elligen P ocess Au oma ion – o his echnology on SME we ha e conside as sco e 2 o
bo h inancial and human esou ces because i equi es mo e in es men han RPA.
Conside ing ha o big companies he budge and openness o in es men should be
highe han o SME, we ha e conside ed o all sec o s a sco e 3, excep comme cial due o
i s complexi y and we a ibu ed a sco e 2.
• Indus y 4.0 – as p e iously s a ed, om li e a u e e iew i can be no ed ha o his
pa icula echnology bo h inancial and human esou ces a en´ conside ed o he
in oduc ion o his echnology, he e o e we didn´ a ibu e any sco e o hem. Al hough
he cos o implemen ing hese mode n echnologies in SME we e he bigges se back, we
also conside ha because hey a e new echnologies i can be mo e di icul o implemen
hese many changes a one ime, we ga e a sco e 2 o indus ial and comme cial.
P ocess Au oma ion:
• Business P ocess Managemen – as p e iously acknowledged, o BPM changes be succeeded
i is necessa y ha companies´ leade s ecognized hei impo ance in implemen and ollow-
up he new p ocess changes. The design, implemen a ion, and execu ion o he changes
doesn´ equi e a signi ican amoun o money. The mos “ aluable” esou ce o be
dispended is ime. And we ealize ha big companies´ leade s don´ ha e he same
a ailabili y, and he ollow-up s age would demand ha a ailabili y. In his sense, we ga e a
sco e 2 o all sec o s.
• Business P ocess In eg a ion – o his p ocess, he main di icul y would be he cos o
in eg a ing di e en sys ems. We unde s and hese cos s wouldn´ be a meaning ully se back
ha big companies wouldn´ in oduce in hei p ocesses, and we had a ibu ed a sco e 3 o
all sec o s.
• Business P ocess Au oma ion –BPA equi es an iden i ica ion and c ea ion o a low o
ac i i ies ha in e connec s p ocesses h ough he company. We iden i y he inancial and
37
comme cial sec o s as he ones wi h mo e complex wo k low ( ega ding indus ial sec o , i
is e y udimen al a wo k low p ocess ha ec ea es he indus ial p ocess). Because o his
eason, we a ibu e a sco e 2 o inancial and comme cial sec o , and sco e 3 o indus ial
and human esou ces.
• P ocess Analy ics – he ba ie iden i ied o implemen his s a egy in SME we e he cos s
associa ed o so wa e´s acquisi ion. Ha ing in conside a ion ha he weigh o so wa e´s
acquisi ion is signi ican ly highe in SME han in Big Companies, we also ga e a sco e 3 o all
sec o s.
• Hype -Au oma ion – we can say ha he main di e ences be ween SME and Big Companies
a e he numbe o employees unde hei pay oll, e enue/cos s/p o i , managemen and
business s uc u e, di e ence in ma ke niche, and know-how. And again, he main se back
o no conside adequa e o implemen hype -au oma ion echnology in SME was he high
cos s associa ed, and as men ioned abo e, big companies ha e ano he capabili y o in es
money in hei business, and o his eason we ga e a sco e 3 o all sec o s.

38
Table 3 - S a egy implemen a ion on Big Companies
Big Companies
Companies Sec o s
Financial
Human Resou ces
Indus ial
Comme cial
Au oma ion Technology
Wo k low Au oma ion
3
3
3
3
Robo ic P ocess Au oma ion
3
3
3
1
In elligen P ocess Au oma ion
3
3
3
2
Indus y 4.0
-
-
2
2
P ocess Au oma ion
Business P ocess Managemen
2
2
2
2
Business P ocess In eg a ion
3
3
3
3
Business P ocess Au oma ion
2
3
3
2
P ocess Analy ics
3
3
3
3
Hype -au oma ion
3
3
3
3
Sco e
Meaning
1
Inadequa e
2
Fai ly Adequa e
3
Adequa e
On Table 4, i is summa ized he in o ma ion ega ding he cos /complexi y o implemen each
au oma ion echnology and p ocess au oma ion, whe e we can see he companies’ sec o s on
columns and he au oma ion echnologies and p ocess au oma ion on ows. The analysis o he
echnologies will be based on he li e a u e e iew.
39
Au oma ion Technology:
• Wo k low Au oma ion
o Cos s: eliable and o ganized da abases.
o Complexi y: mo e complex o design and implemen he wo k low ac i i ies o
indus ial sec o han o inancial, human esou ces and comme cial sec o s.
o Sco e 3 o indus ial sec o , sco e 2 o inancial and human esou ces, sco e 1 o
comme cial.
• Robo ic P ocess Au oma ion
o Cos s: so wa e´s acquisi ion.
o Complexi y: mo e complex o in oduce business ules in inancial and comme cial
sec o s.
o Sco e 2 o inancial and comme cial sec o , and sco e 1 o human esou ces and
indus ial.
• In elligen P ocess Au oma ion
o Cos s: mo e expensi e han RPA echnology.
o Complexi y: mo e complex o in oduce business ules in inancial and comme cial
sec o s.
o Sco e 3 o all sec o s.
• Indus y 4.0
o Cos s: implemen a ion cos s.
o Complexi y: necessi y o ha e implemen ed se e al new mode n echnologies.
o Sco e 3 o indus ial and comme cial sec o s.
P ocess Au oma ion:
• Business P ocess Managemen
o Cos s: insigni ican ly.
o Complexi y: he design, implemen a ion, and execu ion o he changes doesn´
equi e a signi ican amoun o money bu i equi es ime.
o Sco e 1 o all sec o s.
40
• Business P ocess In eg a ion
o Cos s: a conside able amoun o money o in eg a e di e en sys ems.
o Complexi y: in e connec i i y o di e en sys ems.
o Sco e 3 o all sec o s.
• Business P ocess Au oma ion
o Cos s: no a signi ican amoun o money o be dispended.
o Complexi y: iden i ica ion and c ea ion o a low o ac i i ies ha in e connec s
p ocesses h ough he company.
o Sco e 2 o inancial and comme cial sec o s, and sco e 1 o human esou ces and
indus ial.
• P ocess Analy ics
o Cos s: cos associa ed wi h so wa e´s acquisi ion.
o Complexi y: low complexi y o implemen he so wa e in he o ganiza ion.
o Sco e 1 o all sec o s.
• Hype -Au oma ion
o Cos s: high cos s associa ed o his new echnology.
o Complexi y: no signi ican .
o Sco e 3 o all sec o s.
41
Table 4 – The cos /complexi y o he p oposed s a egy implemen a ion
Cos /Complexi y
Companies Sec o s
Financial
Human Resou ces
Indus ial
Comme cial
Au oma ion Technology
Wo k low Au oma ion
2
2
3
1
Robo ic P ocess Au oma ion
2
1
1
2
In elligen P ocess Au oma ion
3
3
3
3
Indus y 4.0
-
-
3
3
P ocess Au oma ion
Business P ocess Managemen
1
1
1
1
Business P ocess In eg a ion
3
3
3
3
Business P ocess Au oma ion
2
1
1
2
P ocess Analy ics
1
1
1
1
Hype -au oma ion
3
3
3
3
Sco e
Meaning
1
Low Cos
2
A e age Cos
3
High Cos
48
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51
APPENDIX 1 - QUESTIONNAIRE
The goal o his s udy is o gain a be e knowledge o how Po uguese o ganiza ions iew he
oppo uni y o in eg a e new au oma ion echnologies, as well as o unde s and he p io i y o each
business depa men . The p ima y esea ch ques ion is: wha can be add essed in he p ocesses o
Po uguese companies, and how can i be inco po a ed?
Po uguese companies´ con ex
1) Wha dis ic is you company in? I mo e han one, which headqua e s is loca ed.
2) How many employees does you company ha e?
3) Which sec o o ac i i y does you company belong o?
S a egy
4) Fo each a ea lis ed below, indica e whe he he implemen a ion o p ocess au oma ion is
expec ed and i s p io i y. (Figu e 8 illus a es he example gi en in he ques ionnai e)
Figu e 8 – Example om he ques ionnai e ega ding ques ion 4)
5) I you indica ed "O he A ea", please indica e which.
52
APPENDIX 2 – SPECIALISTS INTERVIEWS
In e iews’ ansc ip ion ega ding he p oposed amewo ks’ e alua ion s age o he p esen
disse a ion. To s ic ly espec he o iginal s a emen s o he in e iewees, he ansc ip ex
p ese ed he o iginal language.
Specialis s’ in e iews
1- Alexand a Tene a
Q1 – Conside a a amewo k p opos a ú il? Po quê? Caso não a conside e, qual a azão?
Em p imei o luga eu di ia que os alo es a ibuídos a cada uma das ecnologias e p ocessos de
au oma ização dependem do ipo de emp esa, da sua á ea de especialização (que in luencia de
o ma di e en e as p io idades de cada se o ), e dependem do p óp io ipo de abalho. No en an o,
conside ando as amewo ks como uma p opos a de es a égia gene alizada pelo amanho da
emp esa e cus o/complexidade c eio que em a sua u ilidade e bene ício de explo ação. Tendo em
con a a análise an e io men e ei a, eu conside o as amewo ks ap esen adas como ú eis,
nomeadamen e como indicado es/o ien ado es de e en es e a en a i a de e i ica dispa idades e
di e sidade en e se o es e na minha opinião ejo isso como algo in e essan e.
Também c eio que se ia mui o ele an e, e não sei se aplicas e isso nas es an es en e is as, se ia a
c iação de um p o ocolo de en e is a de o ma a ga an i que odos os en e is ados pos os as
ques ões da mesma manei a, embo a os con eúdos sejam di e en es pois cada um em a sua isão.
Q2- Que ecomendações ou suges ões em pa a melho a as amewo ks p opos as?
Acho que pa a se e uma maio p ecisão na alo ização das á ias componen es se ia bené ica uma
cla a dis inção e de inição dos concei os cus o e complexidade, ou pelo menos en a da uma
o ien ação do que se en ende po cus o e complexidade, qual a sua e en e que se encon a a se
a aliada, de modo a uma pessoa que á analisa a amewo k (e uma ez que é uma análise
subje i a) que consiga oma uma decisão com oda a a aliação ei a sob e o mesmo concei o
ap esen ado a odos os inqui idos.
Ou o aspe o é o ní el de p ecisão da escala u ilizada (ní el 1-3, sis ema ímpa ). Eu conside a ia uma
mais- alia a implemen ação de um sis ema pa (ní el 1-4), de modo a “ob iga ” a os en e is ados
oma em a decisão de adequado s não adequado, po que caso con á io a endência na u al é a
pessoa em dú ida op a pelo ní el médio, e sendo es a escala de ní el 1-3, pode p o oca algum
en iesamen o de omada de decisão cen al que na ealidade não exis e se eu ado asse um ou o
sis ema. No en an o, ambém é impo an e ealça que no caso de uma escala pa eu ambém
condiciono a a aliação pois “ob igo” o en e is ado a oma uma decisão mais, ou menos, a o á el.
53
Também acha ia in e essan e ap esen a -se aos en e is ados uma sín ese à p io i dos concei os
abo dados na amewo k (como oi o meu caso no início quando ques ionei o signi icado de hipe -
au oma ização), po que uma pessoa não endo comple a noção de odos os concei os e ia assim
uma espos a condicionada pela expe iência e ao que en ende pelo concei o. Em jei o de sumá io,
em e mos de aplicabilidade u u a se ia de ini o que se en ende pelos concei os, onde eles se
posicionam com a amewo k.
O sco e podia se consensualizado, is o é, o sco e se ia de inido pelos p óp ios inqui idos ou pelas
p óp ias o ganizações e depois consensualiza a-se a a és de um alo médio e a pa i dessa análise
u iliza -se esse alo como e e ência.
2- Sand o Cos a
Q1 – Conside a a amewo k p opos a ú il? Po quê? Caso não a conside e, qual a azão?
Acho que a amewo k ap esen ada é ú il, apesa de não e acesso às jus i icações ap esen adas
pa a a a ibuição dos sco es.
C eio que a amewo k pode se pe ei amen e ap esen ada a pequenas, médias e g andes emp esas
pa a em p imei o luga aze em uma a aliação de como eles es ão, ou seja, pa a pe cebe em em que
pon o da si uação é que se encon am e o que é que eles podem aze p imei o (is o é, como é que
as ações podem e impac o) de modo a pe cebe o que ex ai em e mos de cus o/bene ício. Ou
seja, um consul o pode ia u iliza is o pa a pe cebe o que é que ele pode aze num p oje o a 6
meses, ou 1 ano, de o que é que e á mais impac o e o maio cus o/bene ício pa a uma emp esa,
depois de a alia com os pa âme os da amewo k p opos a, desde que o sco e ap esen ado pa a
cada uma das ecnologias/p ocessos de au oma ização açam sen ido ambém no mundo eal. Is o é,
a amewo k ap esen ada pa ece bem desenhada, mas no mundo eal p ocu a ia ob e mais
eedback de p oduc o owne s, especialis as da á ea e e i ica se a a aliação ap esen ada nes as
amewo ks e as suas jus i icações são plausí eis no mundo do abalho. Pa a isso a c iação de um
guião que con enha as p io idades pa a di e en es es a égias, se ia uma mais- alia no momen o da
ap esen ação das es a égias p opos as nas amewo ks.
Q2- Que ecomendações ou suges ões em pa a melho a as amewo ks p opos as?
Uma ez mais, iso que as classi icações ap esen adas nas amewo ks são jus as, mas en a a c ia
ou as amewo ks idên icas às ap esen adas consoan e o eedback des as en e is as, en e is as
mais pessoas pa a ob e um maio núme o de eedback sob e as classi icações e os po quês das
classi icações dadas, e a pa i daí consegui ia ex ai mais in o mação daquilo que ealmen e
p e endes com o eedback dado nas en e is as pa a a elabo ação da amewo k, além que
consegues “p o á-la” no me cado com esses es emunhos de pessoas que acabam po implemen a
essas ecnologias e p ocessos de au oma ização nas emp esas.
Di o is o, ac edi o que endo a opo unidade de en e is a e ala com pessoas com la ga
expe iência na indús ia a ia mui a in o mação ú il pega , analisa , e aze uma a aliação ge al dos
depa amen os a ibuindo uma classi icação e a pa i daí, pe cebe o que é que é p eciso melho a
ou não, e u iliza a amewo k que es á a se desen ol ida pa a comp eende qual se ia a á ea de

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de e minada emp esa com maio impac o e maio cus o/bene ício. O ideal se ia consegui de ini
uma amewo k de a aliação pa a as adminis ações das emp esas e em a possibilidade de analisa
a amewo k de a aliação, a ua consoan e a amewo k com a es a égia de implemen ação e
no amen e, a alia consoan e a amewo k de a aliação pa a e os esul ados das ecnologias e
p ocessos de au oma ização. Assim, a adição des a no a amewo k c eio que a ia um g ande
bene ício pa a a implemen ação das suges ões ap esen adas.
3- Sand a Pe ei a
Q1 – Conside a a amewo k p opos a ú il? Po quê? Caso não a conside e, qual a azão?
Acho que a amewo k encon a-se bem o mulada. No en an o as emp esas p ocu am o imiza e
au oma iza udo o que seja da pa e indus ial, ou seja udo o que seja de p ocessos indus ias, chão
de áb ica, udo o que seja p ocessos de e eno em de se o mais au omá ico possí el com meno
g au de ope acionalização das pessoas, pelo que conside a ia udo o que seja indus ial como
p io i á io. Em elação à pa e inancei a igual, odas as pessoas da á ea do pelou o inancei o
que em e a in o mação em empo eal de modo a consegui comp eende o es ado do p ocesso,
pelo que a au oma ização dos p ocessos ligados à á ea inancei a acaba po se mui o impo an e
pa a os adminis ado es, CEO´s, CFO´s e em acesso a in o mação eal e idedigna pa a a omada de
decisão se a mais undamen ada.
Resumindo, consigo comp eende a impo ância e u ilidade da amewo k, pois conside ando que a
ideia é ap esen a a amewo k às emp esas pa a de ini quais as p io idades de cada emp esa com o
obje i o de mos a às pessoas que acabam po oma as decisões po onde é necessá io começas a
implemen a . Eu olhando pa a a amewo k ap esen ada, se osse uma g ande emp esa, eu
começa ia po udo o que osse inancei o po que os cus os ap esen ados pa a a á ea são
conside ados acessí eis, de modo a inicia as al e ações e c ia mais alia pa a a emp esa na
au oma ização dos p ocessos. Uma ez mais, na minha opinião eu c eio es as implemen ações no
se o da indús ia cons i uem uma mais alia po que udo é p a icamen e au oma izá el, e acaba po
se mui o di ícil pa a uma emp esa cob i as encomendas dos clien es se não i e um chão de
áb ica bas an e au oma izado, pelo que são mui as as emp esas que se encon am a ado a medidas
com essa isão de au oma iza os seus p ocessos indus iais. Um dos p imei os passos na
implemen ação de p ocessos au omá icos na emp esa na á ea indus ial é a in e ligação com a pa e
inancei a, e udo is o acaba po eduzi o núme o de pessoas nos ecu sos humanos necessá ios
pa a uma emp esa, menos bu oc acia, e c…
Po úl imo, e i ica se o sco e a ibuído ainda se man ém dadas as e lexões ei as du an es odas as
en e is as com base nas isões dos en e is ados.
Q2- Que ecomendações ou suges ões em pa a melho a as amewo ks p opos as?
F isando um pouco do que oi di o na p imei a pe gun a, melho / e e sco es e adiciona ia mais
depa amen os à amewo k. Puxando um pouco da minha expe iência, c eio que a inco po ação do
se o da Logís ica az odo o sen ido, po que odas as emp esas êm es e depa amen o, que seja
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esidual ou não, endo um peso no p odu o inal de ce ca de 10%, sendo uma das á eas que em que
es a o mais o imizado possí el e com um ní el de in e e ência de ecu sos humanos baixo, po que
êm uma p obabilidade de e a supe io ao dos sis emas.
Po isso, no u u o, não sei se i ás inco po a nes a e são o depa amen o de logís ica, uma adição
des e se o à amewo k con ibui ia pa a a mesma osse mais inclusi a e p o unda na análise.
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APPENDIX 3 – FRAMEWORK PRESENTATION
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