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Seven Paradoxes of Business Process Management in a Hyper-Connected World

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Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.
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RESEARCH NOTES
Seven Paradoxes of Business Process Management in a Hyper-
Connected World
Daniel Beverungen Joos C. A. M. Buijs Jo
¨rg Becker Claudio Di Ciccio Wil M. P. van der Aalst
Christian Bartelheimer Jan vom Brocke Marco Comuzzi Karsten Kraume Henrik Leopold Martin Matzner
Jan Mendling Nadine Ogonek Till Post Manuel Resinas Kate Revoredo Adela del-Rı
´o-Ortega
Marcello La Rosa Fla
´via Maria Santoro Andreas Solti Minseok Song Armin Stein Matthias Stierle
Verena Wolf
Received: 19 December 2018 / Accepted: 19 February 2020 / Published online: 22 April 2020
ÓThe Author(s) 2020
Abstract Business Process Management is a boundary-
spanning discipline that aligns operational capabilities and
technology to design and manage business processes. The
Digital Transformation has enabled human actors, infor-
mation systems, and smart products to interact with each
other via multiple digital channels. The emergence of this
hyper-connected world greatly leverages the prospects of
business processes – but also boosts their complexity to a
new level. We need to discuss how the BPM discipline can
find new ways for identifying, analyzing, designing,
implementing, executing, and monitoring business pro-
cesses. In this research note, selected transformative trends
are explored and their impact on current theories and IT
artifacts in the BPM discipline is discussed to stimulate
transformative thinking and prospective research in this
field.
Accepted after two revisions by Martin Bichler.
D. Beverungen (&)C. Bartelheimer V. Wolf
Paderborn University, Paderborn, Germany
e-mail: daniel.beverungen@upb.de
J. C. A. M. Buijs
APG, Amsterdam, The Netherlands
J. Becker K. Kraume N. Ogonek A. Stein
European Research Center for Information Systems (ERCIS),
University of Mu
¨nster, Mu
¨nster, Germany
C. Di Ciccio
Sapienza Universita
`di Roma, Rome, Italy
W. M. P. van der Aalst
RWTH Aachen, Aachen, Germany
J. vom Brocke
University of Liechtenstein, Vaduz, Liechtenstein
M. Comuzzi
Ulsan National Institute of Science and Technology, Ulsan,
Republic of Korea
H. Leopold J. Mendling K. Revoredo A. Solti
Vienna University of Economics and Business, Vienna, Austria
M. Matzner M. Stierle
University of Erlangen-Nuremberg, Nuremberg, Germany
T. Post
AFSMI German Chapter e.V., Munich, Germany
M. Resinas A. del-Rı
´o-Ortega
University of Seville, Seville, Spain
M. La Rosa
University of Melbourne, Melbourne, Australia
F. M. Santoro
State University of Rio de Janeiro, Rio de Janeiro, Brazil
M. Song
Pohang University of Science and Technology, Pohang,
Republic of Korea
123
Bus Inf Syst Eng 63(2):145–156 (2021)
https://doi.org/10.1007/s12599-020-00646-z
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Keywords Business process management (BPM) Social
computing Smart devices Big data analytics Real-time
computing BPM life-cycle
1 Introduction
Business Process Management (BPM), as it is seen today,
is a boundary-spanning research field that builds on and
consolidates research on ‘‘[...] how to best manage the (re-
)design of individual business processes and how to
develop a foundational BPM capability in organizations
catering for a variety of purposes and contexts’’ (vom
Brocke and Rosemann 2010).
BPM can, therefore, be understood as an organization’s
core competency for managing all its business processes,
from operational to managerial. BPM spans all functional
areas in organizations, and networks an organization with
its environment, including consumers and other organiza-
tions, such as suppliers and customers (and beyond that,
with their suppliers’ suppliers and their customers’ cus-
tomers). Based on conceptualizing organizations as socio-
technical systems, BPM views business processes as
organizational structures that are enabled by Information
Technology (IT). Rosemann and de Bruin (2005) introduce
a framework that illustrates the BPM field’s diversity,
comprising six capabilities: Governance, Strategy, Meth-
ods, Technology, People, and Culture. BPM is highly rel-
evant for business success and has become a crucial
organizational core competency for all kinds of organiza-
tions in their daily practice (Mullich 2011). Speaking even
more generally, business processes are a primary compo-
nent of an organization’s DNA, since the performance of
day-to-day work – such as business processes – even
constitutes an organization as a social (or, more precisely, a
socio-technical) structure (Giddens 1984; Beverungen
2014).
Breaking free from the three traditions of work simpli-
fication/quality control (engineering tradition), perfor-
mance of the firm (management tradition), and
digitalization (IT tradition) (Harmon 2006), the academic
community of BPM researchers has contributed theories
and IT artifacts that approach the management of business
processes in its own right since the 1990s. For almost three
decades, international conferences like (Association for
Information Systems (AIS) 2017; BPM Community 2019;
Institute of Innovative Process Management e.V. 2017),
journals like (Emerald Group Publishing Limited 2017), or
books like (Dumas et al. 2018; vom Brocke and Mendling
2018; vom Brocke and Rosemann 2014) have been
reflecting the field’s increasing significance, diversity, and
maturity.
Increasingly, organizations face the phenomenon of
Digital Transformation, an umbrella term pointing at a
broad and fundamental economic (and related societal)
change that is heavily influenced by disruptive IT. IT
trends include, among others, ubiquitous internet access of
myriads of physical devices, access to a vast amount of
data, which can be reproduced and shared at almost zero
costs (Brynjolfsson and McAffee 2014), algorithms that are
able to process big data in real-time, as well as a global
workforce that is capable of creating new business models
from these new opportunities (Brynjolfsson and McAffee
2014). The Digital Transformation can be considered as a
fundamental change that could prove to be equally dis-
ruptive as the industrialization of Europe in the 19
th
cen-
tury (Brynjolfsson and McAffee 2014).
At closer inspection, the Digital Transformation of our
society brings about a Hyper-Connected World (see also:
World Economic Forum 2016), in which human actors and
artificial actors are networked with each other via multiple
communication channels. Hyper-connectedness allows to
perform business processes in an entirely new way, but also
increases the complexity of managing them in line with
corporate or societal objectives. This trend appears to
become so powerful and disruptive that it might funda-
mentally change the resources and capabilities that orga-
nizations and people require to manage business processes.
In particular, organizations have to re-evaluate the rules of
the game in order to build up the assets and core compe-
tencies required to remain successful in their industries. We
take up this trend and investigate how some new tech-
nologies leave their mark on BPM in our society.
In this research note, we focus on four technological
enablers for the fact that we consider their interaction with
BPM as least understood: Social Computing as a paradigm
for connecting individuals digitally, Smart Devices as
digitized physical resources that join processes as artificial
actors in their own right (e.g., Internet of Things, Cyber-
Physical Systems), Big Data Analytics as a tool to auto-
matically analyze extensive data volumes from business
processes and their environments, and Real-Time Com-
puting that enables organizations to analyze data in (near)
real-time to adapt their business processes on-the-fly.
Various other recent technologies are not discussed in
detail, because their potential contribution to BPM is dis-
cussed elsewhere, including Blockchain (Mendling et al.
2018b), Internet of Things (Janiesch et al. 2017a), Se-
mantic Technologies (Mendling et al. 2017a), Artificial
Intelligence (Cangemi and Taylor 2018), and Cognitive
Computing (Roeglinger et al. 2018). We identify how the
selected technologies challenge the main tasks to be ful-
filled by BPM stakeholders – including process owners
(strategy), process analysts (modeling and analysis), and
system developers (implementation) (cf. Fig. 1) – and
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146 D. Beverungen et al.: Seven Paradoxes of BPM, Bus Inf Syst Eng 63(2):145–156 (2021)
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discuss to what extent these challenges align or contradict
each other, pointing to paradoxes that we need to resolve as
a discipline. We defined these tasks in line with the BPM
Life-Cycle Model proposed by Dumas et al. (2018)–a
model that highlights core activities performed by business
process managers, while it does not specify a process
execution phase explicitly and puts less emphasis on how
and why process participants execute/enact business pro-
cesses in their day-to-day work.
This research note implements two objectives. First, we
discuss recent developments in research and practice at the
intersection of BPM and Digital Transformation. Second,
we propose avenues for future research to advance our
understanding of BPM in a hyper-connected world. We
expect these trends to profoundly transform theories and IT
artifacts that currently constitute the BPM discipline, such
that theories have to be tested and refined, whereas IT
artifacts need to be (re-)designed and (re-)evaluated.
Beyond that, we anticipate entirely new challenges to
emerge that require novel theories, as well as new classes
of IT artifacts, which – in the past – were impossible to
develop without the hardware capacity available now.
While we do not claim to cover all aspects of BPM, we
intentionally focus on operational processes, which were
identified (Westerman et al. 2011) as one of three crucial
areas affected by the Digital Transformation.
The paper is structured as follows: in Sect. 2the four
selected IT enablers are introduced in more detail. Subse-
quently, the enablers’ implications on the BPM discipline
are being reflected (Sect. 3), followed by discussing
avenues for future research in BPM (Sect. 4) and a con-
cluding call for action (Sect. 5).
2 Four Information Technology Enablers
In our joint research project RISE_BPM, we explored four
information technology enablers, comprising Social Com-
puting, Smart Devices, Big Data Analytics, and Real-Time
Computing. Subsequently, we briefly present each enabler
and some of its impacts on the BPM field.
2.1 Social Computing
For white-collar workers and customers alike, Social
Media present an opportunity to network with each other
and establish digital communities that foster communica-
tion, cooperation, and collaboration on a group level.
Social Media are means to make information, such as
personal opinions, facts, recent experiences, and stories
available at different levels of public accessibility. They
enable users to communicate with a theoretically unboun-
ded crowd of other people about products and the com-
panies providing them. Based on these interactions, Social
Media contain a partially unfiltered source of information
that typically transcends the boundaries of a single orga-
nization, club, association, or company. Social Media can
be as diverse as online forums, including blogs, company-
sponsored discussion boards and chat rooms, consumer -
to -consumer e-mail, consumer product or service ratings
websites and forums, Internet discussion boards, and social
networking websites, to name a few (Kaplan and Haenlein
2010).
User-Generated Content (UGC) has a significant impact
on tools and strategies adopted by companies to commu-
nicate with their customers (Mangold and Faulds 2009). In
Social Media, data are published with a direct attribution of
the author and the exact time and date of publication. The
main content of the message is conveyed through natural
language, thus making published data semi-structured.
Limiting their automated interpretation, user-generated
content often contains abbreviations, idiomatic expres-
sions, and emoticons. Tags and links enrich the semantics
of a message, which is critical to conduct machine-driven
information linkage.
Still, the extraction and analysis of this UGC can rep-
resent a valuable source of knowledge to companies.
Examples of such sources of information include com-
plaints via Instagram posts about the delivery of a defected
product, or suggestions for improvements via the product
user forum of an e-mail service provider, as well as tweets
about a recent patent, publication, or released product from
the creator. For instance, DELL has analyzed social media
Fig. 1 BPM framework structuring this research note
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posts to identify more than 550 new ideas for their products
based on analyzing UGC on their online community Idea
Storm (Gardner 2014). The opportunities related to ana-
lyzing UGC have lead to a florescence of data mining
techniques applied on customer information to ameliorate
customer relationship management (Ngai et al. 2009).
Within their own boundaries, many organizations offer
their workforce collaboration tools – including Groupware
applications and Corporate Social Media – to enable them
to perform knowledge-intensive processes and knowledge
work. White-collar workers take advantage of the tools to
communicate, cooperate, and coordinate their activities.
Tools include, among others, instant messaging, e-mail
(Geyer et al. 2006), and tools for designing and executing
ad-hoc workflows. Taken together, Social Media represent
a good deal of the communication and information sharing
means used by employees to manage their day-to-day work
and provide a valuable means to connect process actors,
stakeholders, and clients on a shared public platform. The
business processes conducted with these tools often rep-
resent rather informal, non-routine processes that do not fit
well with the top-down design of mass transaction pro-
cesses that are often implemented in a Business Process
Management System (BPMS).
As communication tools, Social Media can also be used
to perform follow-up work on standard processes that are
conducted in enterprise systems. For instance, employees
might be quickly asking for support during a process via,
e.g., their private Skype accounts. Having so much
important activity occur outside and beyond the awareness
of an enterprise application degrades the application’s
effectiveness and management value. For this reason,
companies nowadays tend to offer their employees tailored
Social Media Platforms to exchange process-focused
information (Bernstein 2000) within their organization.
Preserving the ‘‘soft knowledge’’ of the overall process is
of critical importance, in particular in the area of knowl-
edge-intensive processes (Di Ciccio et al. 2015) and artful
processes (Di Ciccio and Mecella 2013; Hill et al. 2006),
that is, processes whose conduct and execution are heavily
dependent on white-collar workers performing various
interconnected knowledge-intensive decision making tasks.
On a meta level, Social Media are repositories of recent
relevant facts that the authors want to make available to
their colleagues, friends, or acquaintances. Those facts
could enrich, specify, or glue together events that are
recorded by BPMSs or other intra-organizational IT sys-
tems by embedding a process into contextual information,
e.g., to explain things that could otherwise be less expli-
cable, very often articulated in the words of the people
involved directly.
2.2 Smart Devices
The introduction and proliferation of Smart Devices is an
earth-shattering event that will profoundly change infor-
mation processing and business models in our world. In
2017, Gartner Inc. (2017a) stated ‘‘[...] that 8.4 billion
connected things will be used worldwide in 2017 [...],
rising up to 20.4 billion by 2020. Total spending on end-
points and services [related to the Internet of Things, IoT]
will reach almost $2 trillion in 2017.’’ That said, in the
Gartner HypeCycle, the IoT is still viewed as being at the
(first) peak and/or sliding into the trough of disillusionment
(Gartner Inc. 2017b).
Smart Devices are equipped with sensors that can detect
their own status as well as physical and digital events in
their proximity. They have build-in hardware to store and
process data to reason autonomously about the data they
collect. They feature actuators that can perform physical
actions inside a device and/or in a device’s proximity,
while they have connectivity to transmit and receive digital
data to/from their environment (Beverungen et al. 2019),
i.e., from other devices and information systems, including
Workflow Management Systems (WfMS) and Enterprise
Systems.
Smart Devices are expected to profoundly transform
various industries, including transport and logistics,
healthcare, and manufacturing as well as the individual
domains of living and social interactions (Atzori et al.
2010). As artificial actors in their own right, myriads of
Smart Devices – including smart meters, smart vehicles,
smart machines, smart phones, and others – will be starting,
conducting, influencing, and ending business processes.
Their build-in features will make Smart Devices partially
autonomous, such that their actions cannot be controlled by
one central authority, such as a business process engine.
This shift of control means that business processes will be
conducted a lot more decentralized, which will render top-
down process engineering unfeasible, shifting control from
build-time to run-time.
Moreover, the emergence of Smart Devices adds a
physical perspective to business processes; while faulty
processes in digital execution environments might be rol-
led-back, it might be impossible to undo physical actions
that have been performed. Therefore, business processes
that lead to physical actions performed by Smart Devices
must be fail-safe to prevent adverse consequences of
business processes.
First industrial business processes have been trans-
forming to incorporate the benefits of Smart Devices, many
of them stemming from the machine tools industries, in
which production technology has been equipped with
automation technology for a long time. Continuing this
tradition, connecting a machines’ internal data processing
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capabilities with the ‘‘world outside’’ seemed like the next
logical step, such that many current cases and prospects
(Atzori et al. 2010; Perera et al. 2010) focus on sensing
events in the field and taking these events up in business
processes. For instance, Oracle reports a case in which a
smart equipment senses outages proactively – based on
acquiring data on themselves and on their environment –
and reports the outages as events to remote information
systems (Acharya 2015). These information systems listen
for events and start the execution of pre-defined business
processes (for instance, maintenance processes aimed at
fixing the equipment) as soon as these events have been
thrown.
Another case that utilizes Smart Devices to perform
physical actions is situated in Hamburg, where ‘‘300
roadway sensors were installed by the Port Authority in
order to monitor, control and manage roadways traf-
fic’’ (Ferretti and Schiavone 2016, p. 278). For instance,
since movable bridges are being opened on arrival of a
ship, the road traffic in the port can be diverted to alter-
native routes now. In addition, the ‘‘system also calculates
the weight of vehicles in order to establish the volume of
traffic on the 140 bridges available in the port for trucks
and trains and provide useful information for the design,
maintenance and restructuring of these infrastruc-
tures’’ (Ferretti and Schiavone 2016, p. 279), to improve
the port’s ‘‘integration with customers, reduce direct con-
tacts and formal information exchanges with them and,
finally, made easier and shorter their decision-making
process’’ (Ferretti and Schiavone 2016, p. 279).
2.3 Big Data Analytics
Increasing amounts of data have been recorded for decades
now (Hilbert and Lopez 2011), many of them generated by
the trends for Social Computing and Smart Devices.This
development is often referred to as Big Data, which in
general means that each of the ‘‘four V’s’’ is at play:
Volume, Velocity (data grow quickly), Variety (data are
heterogeneous), and Veracity (data quality varies). Big data
as such does not always refer to large datasets, but could
also indicate small but complex datasets.
In general, data are increasingly collected for general
purposes and do not refer to a single goal or type of
analysis. The main challenge is to make sense of the
available data, using the right data and analysis techniques.
In recent years, the field of Data Science emerged, which is
an amalgamation of different sub-disciplines (van der Aalst
and Damiani 2015): statistics, data mining, machine
learning, process mining, stochastics, databases, algo-
rithms, large scale distributed computing, visualization and
visual analytics, behavioral and social sciences, industrial
engineering, privacy and security, and ethics. Of these
areas, process mining bridges the gap between big data and
data science to BPM.
Process mining answers crucial BPM questions, based
on analyzing data from event logs. An event log contains a
collection of events, where each event corresponds to: a
case or process instance (e.g., an order number), an activity
(e.g., evaluate request), a timestamp to indicate when the
activity was executed, and additional (optional) attributes,
such as the resource executing the corresponding event, or
the type of event (van der Aalst and Damiani 2015).
Based on the data provided in the event log, process
mining covers three main aspects: discovery of a process
model (e.g., BPMN model or Petri net) based on event
data; conformance checking of event data with respect to a
provided (or discovered) process model; and enhancement
of a process model by using event data to project, for
instance, time information on the process model in order to
analyze the performance of the business process.
Extending the conventional approach to mine processes
based on event logs, the analysis of Big Data allows putting
data on business processes into a context of other events
that are related to a process. These additional data might,
e.g., be provided on Social Media or by Smart Devices,as
sources of data that might extend, complement, or even
contradict data stored in BPMSs. A crucial prerequisite for
making these data usable is to assure data quality and an
adequate degree of granularity (e.g., consistent process
IDs), such that the data can be mapped to process data
supplied in event logs.
Within our project, we investigated how contextual
information about process instances and activities is cau-
sally related to process performance over time. For
example, the resource executing a particular activity in the
process can influence the overall case duration and/or
quality, since more or less rework is required. Another
question is how different schedules for different resources
can have an influence on the waiting time for activities
performed by those resources. This, in turn, can affect the
total duration of a process.
Another example is the analysis of health care event
data in order to identify how patients are treated in a health
care organization. Questions like ‘‘what is the most com-
mon treatment process’, ‘‘among which persons are han-
dovers performed in an organization’’, or ‘‘how efficient
are processes in a hospital’’ can be answered using health-
care event data, as has been done for a Dutch hospital
(Mans et al. 2009). However, the issue is that disease
treatment is not structured, despite clinical guidelines and
pathways, due to the combinations of diseases, patient
characteristics and variability in medical staff. Providing
insights into these processes, using the recorded event data,
can result in re-designing and improving the business
processes.
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2.4 Real-Time Computing
Recent advances in data processing, allowing for higher
data volumes due to distribution, have enabled the devel-
opment of technologies that are capable of processing a
huge amount of information in real-time. This means that
organizations can leverage this information instantly and
take immediate action to adapt operational processes and
corporate strategies to the ever-accelerating pace of busi-
ness. Note that when we talk about real-time, we do not
refer to the classical meaning of real-time systems in which
tasks have hard deadlines and timing faults may cause
catastrophic consequences (e.g. car automated safety sys-
tems) (Stankovic 1988). Instead, in this context Real-Time
Computing refers to the so-called near real-time, in which
the goal is to minimize latency between the event and its
processing so that the user gets up-to-date information and
can access the information whenever required.
Amongst the technologies that have fostered the use of
Real-Time Computing, we highlight four of them with a
strong impact in a business context. Complex event pro-
cessing (CEP) enables filtering, composition, aggregation
and pattern-detection of events that come from multiple
sources, such as customer orders or social media posts
(Cugola and Margara 2012). In-memory analytics involves
the use of Random Access Memory (RAM) to store and
analyze data, in contrast to traditional analytics in which
data are stored on disks. This results in significant perfor-
mance gains that allow business users to experiment with
customer data in real-time and hence, to make timely
decisions (Acker et al. 2011). Big data stream analytics
enable the real-time processing of streams of data that have
high volume and velocity by relying on parallelization
platforms like Apache Spark Streaming (Zaharia et al.
2013). Finally, data stream mining performs traditional
data mining techniques with continuous rapid data records.
This includes techniques that can produce acceptable ap-
proximate mining results to cope with the high data rate of
data streams as well as capturing the changes of data
mining results coming from the evolving nature of data
streams (Maimon and Rokach 2005).
These Real-Time Computing technologies provide BPM
with the necessary tools to leverage intelligence instantly
and make evidence-based timely decisions. This means that
the traditional division of on-line transaction processing
(OLTP) and on-line analytical processing (OLAP) can be
overcome, making real-time process execution viable.
Doing so is critical in a digitized and globalized environ-
ment in which organizations must adjust their processes at
maximum speed and, at the same time, they have to make
sure that their decisions are based on proper data and
analytics. Connecting with Social Media and Smart Devi-
ces, this implies that business processes can be started,
conducted, influenced, and stopped from outside classic
BPMSs.
There are many different situations in which real-time
computing brings clear advantages to BPM. For instance,
real-time business activity monitoring can support deci-
sion-making to react faster to different situations. For
example, a movie streaming service company tracks
instantly which films are most popular among its customer
segments so that their content team knows which films they
should promote (Oxford Economics 2011), or an airline
company that uses real-time information to manage seat
availability for its 2000 daily flights with the goal to put as
many travellers on board as possible (Oxford Economics
2011). Another case in which Real-Time Computing brings
significant advantages is the immediate detection of non-
compliance situations or fraud. For instance, a payment
platform leverages big data stream analytics to detect
fraudulent credit card payments (Li 2017).
3 Implications for BPM
Given the four enablers presented in the previous section,
and considering four typical phases of BPM (cf. Fig. 1), the
authors conducted a workshop session,
1
supplemented by
follow-up discussions. In the workshop session, groups of
3–4 researchers discussed how – from their point of view –
one of the identified technological enablers impacts the
BPM discipline. All researchers involved in this session
had a long standing record of projects and publications in
the BPM field. As a result, a total of 60 consequences for
the BPM field were identified. These consequences were
presented, discussed, and consolidated in the entire group
of 16 researchers. From the consolidation step, 23 ideas
emerged, pointing to eleven challenges. Thus, while the
statements developed by individual researchers might ini-
tially have reflected their subjective points-of-view on the
BPM discipline, we followed a consensus-oriented inter-
pretivist research approach that was promoted by the
diversity of our viewpoints on the BPM discipline. This
approach is an established epistemic theory of truth for
conducting research on conceptual modeling (Becker and
Niehaves 2007). Subsequently, we present these challenges
in terms of the four main categories we selected.
3.1 Strategy
The emergence of the IT enablers requires closer integra-
tion of the four phases contained in our framework, and
speeds up a process’s life-cycle itself. Also, business
1
At Schloss Dagstuhl, see http://www.dagstuhl.de/17364.
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processes might have consequences in the physical world,
which greatly impacts their governance and management.
Challenge 1 The main strategic challenge for organiza-
tions is the need to adapt their processes at an ever
increasing speed, to follow up on the technological
advancements that influence BPM. This means that orga-
nizations need to speed up a process’s life-cycle, changing
the process more often, maybe even continuously. One way
to achieve this is to integrate the activities in a process’s
life-cycle more tightly, for instance by linking the model-
ing, implementation, and analysis phases through the data
created and used in process execution. First concepts on
integrating AB-Testing and BPM have been proposed in
this direction (Satyal et al. 2019). The trend for continuous
adaptation will likely divert management attention and
resources away from transformational re-engineering ini-
tiatives to incremental on-the-fly improvements of business
processes, at least if the underlying IT infrastructure of a
business process remains largely unchanged – termed the
third wave of BPM (Smith and Fingar 2003). In regard to
the BPM workforce, we expect that the traditional gaps
between process analysts, process owners, process
designers, and process participants will disappear, in favor
of establishing interdisciplinary teams; a similar trend can
be observed in applications management, where (Biz)De-
vOps establish teams that include software developers,
operators, and users (Bass et al. 2015).
Challenge 2 A hyper-connected world leverages the
emergence of omni-channel interactions between compa-
nies and customers (Verhoef et al. 2015). With the rise of
Social Computing, companies adjust their strategies to use
appropriate communication channels to interact with their
clients (Tiago and Verı
´ssimo 2014). Implementing omni-
channel strategies means that business processes will span
across more tools than today (Mangold and Faulds 2009).
This fragmentation necessitates linking data from diverse
systems and establishing identifiable process IDs – both are
crucial prerequisites for making process mining and other
data science approaches work. On the clients’ side, the
openness of Social Media enables customers to network
with other customers they might not know personally.
While social media enables networks of customers to
become participants in a business process, the communi-
cation on Social Media is (at least partially) public. While
benefits of using social media for BPM include integrating
BPM stakeholders into the design, modeling, implemen-
tation, execution, and process improvement (Erol et al.
2010), they add complexity to managing and performing
business processes, too.
Challenge 3 Caused by the emergence of Smart Devices,
business process execution can have physical consequences
that – other than purely digital processes – cannot be rolled
back. For instance, business processes could set physical
devices – such as bridges or vehicles – in motion. As long
as business processes were confined to the digital world of
software systems (e.g., BPMSs, Process Engines, and
Enterprise Systems), errors in business process instances
could be resolved by database roll-backs or other corrective
digital operations. In a world in which business processes
have physical consequences issued by Smart Devices, such
corrective actions might no longer be viable. In this world,
business processes might become safety-critical and
demand much higher degrees of reliability and process
quality that are beyond the capabilities of current IT arti-
facts used in BPM (Meroni et al. 2017). Moreover, this
issue contradicts the decisions and actions that process
designers might conduct based on probabilistic methods in
Big Data Analytics, since these methods are subject to
uncertainty when predicting unobserved data (Ghahramani
2015). Thus, the applicability of probabilistic data science
approaches might remain limited to digital-only business
processes and to processes for which enough data are
available to train the model adequately. If unresolved, this
restriction to digital processes is a profound one, since it
would severely limit the ability of process participants and
process managers to apply data science to processes that
influence the behavior of smart devices.
Challenge 4 The introduction of Smart Devices into
business processes as actors in their own right increases the
complexity and unpredictability of business processes,
since decisions will no longer be made by one central
business process engine alone. Soon, chat bots might play a
bigger role in processes such that their interactions with a
BPMS need to be specified (Mendling et al. 2018a). As a
consequence, data associated with one business process
will be scattered across various software systems and Smart
Devices. Scattered process data and distributed process
control will create entirely new challenges in regard to the
complexity and accountability faced by process partici-
pants conducting a business process. In addition, process
managers will need more effective and efficient methods to
re-integrate data on a business process, before meaningful
analyses of process data can be performed.
3.2 Modeling
In a hyper-connected world, process modeling must feature
additional modeling constructs, while conceptual models
must be integrated more closely with field data and the
workflows implemented.
Challenge 5 Business process modeling languages must
support additional constructs to include new data and
effects related to the four IT enablers. For instance, process
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modeling languages must have the right level of abstraction
to deal with the diverse data involved, from a top-down
refinement of business processes to a bottom-up (re-)or-
ganization through data retrieved from event logs and
sensors (Janiesch et al. 2017b). A holistic approach would
allow stakeholders to seamlessly navigate through different
levels of abstraction, to use process models as efficient
means to communicate about a process from different
angles. Future modeling languages also need to integrate
activities/control flows more tightly with analytics/decision
making, as a foundation for real-time process execution.
From a human-centered perspective, the beliefs, intentions,
desires, feelings, decisions, collaboration, and contingency
events of human agents designing or performing processes
could be modeled to account for the unpredictable nature of
knowledge-intensive processes (dos Santos Franc¸a et al.
2015). Finally, current process modeling languages do
neither address Social Computing, nor Smart Devices (both
of them can be sensors or actuators in a process) with their
native constructs.
Challenge 6 Process models need to be more tightly
integrated with both the implemented workflow models
and with the process data generated while performing
processes. In addition, process models need to be designed
more efficiently to save resources and to put them into
operation more quickly. This can be an advantage for
addressing Challenge 1too, since it would speed up a
process’s life-cycle based on using process models to
bridge field data with implemented workflows. One way to
speed up the modeling process is to build on best-practice
knowledge obtained from process handbooks, reference
model collections, or from process participants’ expertise
(Mendling et al. 2017b). Automatic text analyses might
prove useful to identify reference processes from collec-
tions of unstructured texts (Friedrich et al. 2011). Process
mining might serve to detect variations and workarounds
(Alter 2014) in business processes. Also, advancing mod-
eling languages includes the provision of a tighter inte-
gration of modeling choices in the process with decisions
made during run-time, based on the available process data
and other input.
3.3 Implementation
From the perspective of process engines, the advent of
Social Computing and Smart Devices highlights the need to
roll out processes across distributed systems that might
include various information systems and physical objects.
Also, workflows must be implemented into organizations
and software systems more quickly, be consistent with
conceptual models, and be based on hard field evidence
and data analytic capabilities, to direct their control flow
on-the-fly.
Challenge 7 While many of the challenges discussed
before increase processes’ complexity, we see a strong
challenge to simplify the implementation of all the extra
features (La Rosa et al. 2011). For instance, the different
data sources, devices, and social media channels that affect
a business process must be efficiently connected to process
information systems. This includes the ability to leverage
available data at near real-time while executing a process,
i.e., to enable process analysts to analyze activities at run-
time, and to offer process participants evidence-based
recommendations concerning a process’s control flow.
Challenge 8 In line with the distributed socio-technical
environment in which processes will take effect, business
processes must be implemented and deployed across
diverse applications, Smart Devices, and social systems.
For instance, Smart Devices will act autonomously
depending on their own sensor data, which limits a process
engine’s ability to control a business process fully. This
lack of control requires to introduce new strategies to
govern and direct the execution of process instances in
distributed settings, making sure that the process’s execu-
tion complies with predefined standards. At the same time,
implementing a business process also becomes more
complex if the process includes more (and more diverse)
process participants and organizations. This increasing
complexity motivates reflecting and updating strategies
(Kettinger et al. 1997) and best practices (Mansar and
Reijers 2005) for re-designing business processes. Beyond
adjusting process re-design, research evidences that pro-
cess participants often work around or deviate from pre-
defined business processes (Alter 2014). In a distributed
environment, workarounds and variability might effect
other participants, information systems, and devices (Wolf
and Beverungen 2019). In a hyper-connected world, busi-
ness processes will, therefore, exhibit more variability,
become more unpredictable, and are more difficult to
control with current methods.
3.4 Analysis
Process analysis must built on much broader and deeper
data, comprising event logs and myriads of other data
points generated by diverse information systems, users, and
Smart Devices. Based on these data, analytics can, there-
fore, have a much greater impact on processes in the future,
but we must solve the obstacles associated with making
these data usable, which range from data quality issues to
matters of data privacy, data security, and responsible data
science.
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Challenge 9 The main analysis challenge is the correct
and simple application of data analysis techniques and a
correct interpretation of their results. For instance, predic-
tive analytics currently is actively researched, but it is not
yet practically applicable (Teinemaa et al. 2019). Due to
the amount of data that is available for analysis, the dis-
cipline still struggles to translate data analysis into process
improvements that have strategic importance, closing a
process’s life-cycle. Analysis techniques should be
expanded beyond a ‘single focus’ perspective, and be able
to automatically include domain knowledge that enable
analysts to interpret the results in their context (de Me-
deiros et al. 2007). Furthermore, more efficient or even
simpler visualization of the results is needed to ease the
access of the analysis outcome not only for specialized
consultants but also for process participants (Buijs et al.
2014; Lieben et al. 2018).
Challenge 10 With the use of Social Media and Smart
Devices, the additional data generated need to be included
in the analysis phase to add context to a business process.
This can go so far as to identify a complete state of an
organization, by integrally analyzing all activities and
resources. Since many of the data required for this purpose
will be unstructured and were never meant to be used for
analyzing business process, the data must be processed to
make them available on a sufficient level of quality. The
analysis techniques must involve the adoption of Natural
Language Processing (NLP) techniques to allow for the
correct labelling and interpretation of human-written
information outside the scope of the automated IT systems
logging (Leopold et al. 2014). Also, analysis techniques
must be able to interpret, enrich, integrate, and filter data
from multiple sources, where data are stored not only in a
structured manner, as they can be semi-structured or
unstructured (Di Ciccio and Mecella 2013). Content-wise,
we need new techniques that can cope with specific data
characteristics, such as beliefs, desires, and intentions of
process participants, but also machine states and physical
actions, as well as unstructured data that might be noisy,
leading to more extensive data preparation activities before
meaningful analyses can be performed. Many of these
challenges are due to the properties of knowledge-intensive
processes that are particularly subject to decisions made by
participants performing a business process (Di Ciccio et al.
2015).
Challenge 11 Like data science in general, business
process analysis techniques need to follow the principles of
responsible data science, including fairness, accuracy,
confidentiality, and transparency (van der Aalst et al.
2017). The importance of those principles becomes
prominent even more because of the rapidly increasing
amount and reach of data stored in a process context,
including Social Media and Smart Devices. In particular,
identifying beliefs, desires, and intentions of human pro-
cess participants in processes brings about ethical concerns
regarding false interpretations made from analyzing the
data, in particular so if these insights are made public.
Ethical guidelines for data science do not only apply to
personal data used in a process, but they also need to be
respected when analyzing process participants’ perfor-
mances in a process. For instance, methods for identifying
social networks with process mining (van der Aalst et al.
2005) must be designed and used to comply with ethical
guidelines (Fahrenkrog-Petersen et al. 2019).
4 Discussion
While the challenges identified in the preceding section
seem valid in their own right, a closer look revealed that
some of them influence – or even contradict – each other.
On a higher level of abstraction, then, we consolidated the
challenges to identify seven paradoxes that the BPM dis-
cipline must solve when developing new theories and IT
artifacts. The paradoxes highlight the need to perform
integrated research cycles, which consider the dialectic
properties of these aspects.
Paradox 1: Propelled by the introduction of Social
Computing and Smart Devices, strategies, models, imple-
mentations, and analyses of business processes become
more complex, whereas a process’s life-cycle speeds up
and requires tighter integration. We need to develop new
technologies and organizational ideas to achieve both of
these conflicting objectives at the same time. An important
aspect can be to re-define traditional roles of process
managers and process participants.
Paradox 2: Modeling languages must feature additional
modeling constructs to grasp additional information on a
process, which will increase process models’ complexity.
Still, conceptual models must be designed more efficiently
and at lower cost. We need to design modeling languages
that satisfy both requirements at the same time, based on
reducing complexity – where possible – and guiding
modelers through the design process in an efficient way.
Also, the models must be made actionable as artifacts that
seamlessly link the conceptualization, implementation, and
data analysis of a process.
Paradox 3: Process execution and data analysis must
converge to enable process participants to make real-time
decisions when performing a process. However, process
execution environments and process data become scattered
across different organizations, information systems, and
Smart Devices, leading to noisy, incomplete, or contra-
dictory data. These deficiencies call for performing more
complex data preparation activities that stand against real-
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D. Beverungen et al.: Seven Paradoxes of BPM, Bus Inf Syst Eng 63(2):145–156 (2021) 153
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time decision making. Process managers have to decide
what process performance dimension(s) to prioritize and to
what extent performing data preparation activities is nec-
essary and justified from a business perspective.
Paradox 4: Big data on processes must be analyzed in
near real-time to fine-tune process execution. Many data
analysis approaches used for this purpose are probabilistic,
and the recommendations made with these methods are not
always traceable to the data. On the other hand, in a world
that is permeated by Smart Devices, processes might have
physical manifestations that display safety-critical proper-
ties, which conflict with using (potentially inaccurate)
probabilistic algorithms. Both aspects need to be recon-
ciled, to enable process participants to adapt business
processes where needed, while complying with safety
requirements.
Paradox 5: Due to their increased complexity, IT arti-
facts for BPM are more difficult to conceptualize and
implement, which leads to increased resource consump-
tion. Furthermore, processes are subject to autonomous
actions performed by people and by Smart Devices, which
might render efforts to steer a process with a central
business process engine useless. Therefore, we need to
clearly identify in what scenarios it will pay off to apply
the resources needed to define standardized processes – and
what scenarios will have an intentionally incomplete defi-
nition, recognizing the ability of humans and artificial
actors to adapt a process where needed.
Paradox 6: Companies are faced with a need to stan-
dardize most of their business processes, to capitalize on
economies of scale and reduce process costs. In addition,
the autonomy built into Smart Devices will make products
adaptive to their use and context, leading to individualized
products. Individualization of products will then bring
about individualized service processes, which contradicts
efforts for their standardization. Companies are, therefore,
challenged to manage some parts of a process for effi-
ciency, while other parts of a process must be managed for
business value. The BPM discipline must develop theories
and artifacts that allow managers to reconcile both objec-
tives, based on applying methods on a higher level of
abstraction.
Paradox 7: IT artifacts for BPM become more complex,
while their evaluation requires hard field evidence that is
based on data. Since performances and data of a process
might differ across scenarios, the same process will likely
evolve quite differently in each context. This dependency
on field evidence interferes with the mission of design
science research to develop theories for design and action
(Gregor and Jones 2007) that hold true beyond individual
contexts (Gregor and Hevner 2013), thus making design
science projects more difficult to plan and to document.
5 Conclusion
In this research note, we identified some information
technology enablers that promote a hyper-connected world,
and inferred some implications for strategizing, modeling,
implementing, and analyzing business processes. As we
have discussed, these trends display disruptive potential
that question many of the taken-for-granted theories and IT
artifacts in the BPM discipline. In particular, the challenges
we presented strongly point at an increasing level of
complexity associated with BPM, while processes also
must be implemented more quickly and more frequently.
To foster a discussion and point at the next steps for
research in our discipline, we operationalized these con-
flicting developments with seven paradoxes that will leave
a strong mark on future research on business processes.
An overarching issue in the challenges and paradoxes
we identified is the need to integrate the design – per-
formed by process owners, process analysts, and system
developers – and the execution of business processes –
performed by process participants – further. Future BPM
research needs to identify to what extent shifting and
recombining traditional roles in BPM can work as a strat-
egy to solve the paradox of managing processes at
increasing speed and complexity. One idea towards that
end is building on theory on organizational routines
(Pentland and Feldman 2008) to investigate how perfor-
mances of business processes may contradict and refine IT
artifacts as well as organizational structure.
We would like to invite other researchers to help pro-
pelling the BPM discipline into this new age. As a guide-
line for performing this research, we state that it is
important to be mindful of the paradoxes identified in this
article, to establish a consistent body of knowledge on
BPM that does not suffer from local optima.
Finally, cooperating in our project proved that we can
draw great potential from an inter-disciplinary and inter-
national cooperation of researchers that integrates – and at
times reconciles – a business perspective and a more
technical perspective on business processes. We strongly
encourage other researchers to do the same; after all, BPM
rightfully claims its place as a boundary-spanning
discipline.
Acknowledgements Open Access funding provided by Projekt
DEAL. The research leading to these results received funding from
the European Unions Horizon 2020 research and innovation program
under the Marie Skłodowska-Curie Grant agreement no. 645751.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made. The images or other third party material in this
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article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http://creativecommons.
org/licenses/by/4.0/.
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... Business process management (BPM) continuously attracts academia and practice, as it is known to drive organizational performance [7]. Especially process (re-)design entails significant economic value by introducing innovation, reducing costs, as well as improving quality, productivity, and customer experience [22]. ...
... Today, organizations must overthink their business processes at an increasingly fast pace, consider continuously rising customer needs, create novel processbased value propositions, and engage in innovation to stay successful [7,13,15]. Technological developments are rapidly gaining momentum, processes are at drift, and ever more players enter the global market, resulting in the organizational environment becoming more volatile, uncertain, complex, and ambiguous (VUCA) [5]. Even though this poses pressure on organizations, it also offers a wide range of opportunities. ...
... acquiring real-time insights into future behavior and results of running process instances and provide recommendations for optimizing process control [21]. Driven by the recent "hyperautomation" trend [19] and the widespread adoption of process-aware information systems, organizations increasingly aspire to leverage automation potential in the context of process operations [7]. Whereas process mining and monitoring primarily focus on (partially) automated process control, robotic process automation (RPA) has become the new "technological star" for the lightweight automation of process execution [20]. ...
... This study's results demonstrated notable benefits of using a communication approach to process modelling, in alignment with a recent academic interest in alternative approaches to process modelling that are more agile and stakeholder-oriented (Hull et al., 2016;Beverungen et al., 2020). We see this as a sound basis to propose future research. ...
... In Section 5.2, we proposed the use of a communication approach to support process discovery by enhancing the involvement of domain experts in elicitation and modelling. This has the potential to address one of the challenges of modern BPM that has to deal with more complex processes in a hyper-connected world (Beverungen et al., 2020): by speeding up the design and implementation of process models. One of the questions to be answered by academic research is whether this has more fundamental implications for our way of thinking about process modelling. ...
... Could it be traded off against the benefits of simpler representations such as S-BPM? In the conflict between the need for expressiveness and the need for efficient modelling (Beverungen et al., 2020), recent literature on S-BPM argues in favour of the latter. For example, Kannengiesser (2017) proposes that for tighter integration of life-cycle phases the focus of process modelling needs to be on simplicity and executability rather than expressiveness. ...
Article
Purpose – Business process modelling integrates and visualizes relevant information essential for managing day-to-day business operations. It plays a critical role in the design and execution of business transformations. Recognizing the role of process modelling, a large number of modelling languages, methods and techniques have been developed, each offering diverse advantages and having inherent limitations. Traditional and popular process modelling approaches focus on the exact specification of the control flow of business processes, whereas more recent approaches like Subject-oriented Business Process Management (S-BPM) are focused on the communication between process participants. This study aims to provide comparative insights about these two approaches through their experimental application. This study does so by comparing Business Process Model and Notation (BPMN); a control flow approach, with S-BPM; a communication approach, with a specific focus on their suitability for novice modellers. Design/methodology/approach – This paper reports on an exploratory experiment that compares BPMN to S-BPM. Applying cognitive load theory, this study compares the experiences and outcomes of novice process modellers, assessing perceived ease of use, model quality (syntactic and semantic) and modelling efficiency (time to model) across the two approaches. Findings – Study results show that S-BPM (a communication approach) led to significantly better user performances for process modelling than BPMN (a control flow approach). This study points to how a different modelling approach such as S-BPM could be either considered as an alternative or to complement the more popular control flow approach BPMN. This observation was especially relevant for modelling contexts where domain experts are novice process modellers. Originality/value – This study provides the first empirical evidence that communication approaches like S-BPM could outperform modelling approaches which are control-flow based (i.e. BPMN), especially when being used by novice process modellers who hold the domain and process knowledge. This study uses this as a springboard to present important considerations for practice and guide future process modelling research.
... As processes become more fragmented, span across IS, or even go beyond organizational boundaries with the rise of ecosystems, the organizational embedding of novel digital innovations to support BPM becomes more complex. Simultaneously, innovation cycles speed up and require tighter integration, increasing pressure on all actors (Beverungen et al. 2021). Organizations find themselves in a paradox of rising opportunities and challenges to their functioning. ...
... The fourth tension is fueled by changes to the technical system, as system landscapes, process steps, and data points do not necessarily become leaner, but can present more fragmented (Beverungen et al. 2021;Vial 2019). "With a growing and individualized landscape, it becomes less standardized. ...
Conference Paper
Full-text available
Digital process innovation (DPI) is anchored to the continuous optimization and redefinition of business processes based on digital technologies. DPI is central for digital transformation as it enables organizations to maintain competitiveness and operational excellence in a world shaped by short innovation cycles and boundary-spanning forces of change. Knowledge on DPI stems from separate communities: digital innovation and business process management (BPM). Consequently, DPI currently lacks an integrated perspective at the junction of operational processes and digital innovation. To address this gap, we characterize DPI and develop a taxonomy, drawing on the theoretical lens of sociotechnical systems and empirical data from 26 expert interviews. We provide a comprehensive overview of DPI characteristics and derive four interrelated tensions across its sociotechnical components. Our contribution is twofold. First, we add to contemporary discussions on how digital innovation challenges traditional BPM. Second, we expand research on digital innovation with respect to organizational processes.
... Moreover, several scholars suggest that I4.0 adoption implies relevant managerial challenges that must be addressed to achieve an effective implementation and to reach the maximum potential of smart technologies (Agostini and Filippini, 2019;Bajic et al., 2021). Firms face many technical and organizational issues when implementing I4.0, this may lead to a growing complexity on different firm levels that generates uncertainty (Beverungen et al., 2021;Schneider, 2018). In this kind of complex and innovative environments, demands that are contradictory but at the same time interrelated intensify, generating tensions that become paradoxical if persisting over time (Smith and Lewis, 2011). ...
Article
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While implementing Industry 4.0, organizational environments become more global, dynamic, and competitive thereby intensifying contradictory demands. In light of the resource and knowledge intensiveness of this often multi-year process, this study draws on paradox theory to identify the main organizational tensions emerging and persisting during the Industry 4.0 transformation of companies in a two-step approach. First, a systematic review of 73 academic papers on organizational challenges in Industry 4.0 adoption is conducted that summarizes 35 key challenges. Second, from these challenges a conceptual framework is built that illustrates the main tensions in Industry 4.0 implementation. The identified organizational tensions are categorized according to the learning, organizing, belonging, and performing categories proposed by the paradox theory. Moreover, resolution strategies to address these tensions have been drawn from the reviewed literature. These strategies are presented and linked to the individual tensions. Finally, tensions and resolution strategies were preliminary validated by a group of Industry 4.0 professionals during a workshop. As a result, the findings provide 23 tensions and 18 related resolution strategies illustrating how organizations can address to competing demands simultaneously when implementing Industry 4.0 technologies and thus raise their competitiveness and performance. Based on these results, the article discusses implications for operations management practitioners that can use the proposed framework to inform their strategies and decisions in Industry 4.0 implementation. Moreover, policymakers can adopt the results to develop focussed support actions for driving the Industry 4.0 transition. Finally, four main avenues for future research and implications for operations management scholars are provided.
... Despite its severity, the COVID-19 pandemic is not the only exogenous shock that organizations have had to tackle in recent years; for example, they also faced the 2008 global financial crisis (Roy and Kemme 2020), Brexit (Todd 2017), the US-China trade war (Thomas et al. 2020), and the Fukushima nuclear disaster (Wakiyama et al. 2014). While not all exogenous shocks have been, or will be, as severe as the COVID-19 pandemic, organizations will probably experience such events more frequently since the economy is becoming increasingly volatile, uncertain, complex, ambiguous, and hyperconnected (World Economic Forum 2016; Beverungen et al. 2020). ...
Article
Full-text available
Business process management (BPM) drives corporate success through effective and efficient processes. In recent decades, knowledge has been accumulated regarding the identification, discovery, analysis, design, implementation, and monitoring of business processes. This includes methods and tools for tackling various kinds of process change such as continuous process improvement, process reengineering, process innovation, and process drift. However, exogenous shocks, which lead to unintentional and radical process change, have been neglected in BPM research although they severely affect an organization’s context, strategy, and business processes. This research note conceptualizes the interplay of exogenous shocks and BPM in terms of the effects that such shocks can have on organizations’ overall process performance over time. On this foundation, related challenges and opportunities for BPM via several rounds of idea generation and consolidation within a diverse team of BPM scholars are identified. The paper discusses findings in light of extant literature from BPM and related disciplines, as well as present avenues for future (BPM) research to invigorate the academic discourse on the topic.
... Essam and Limam Mansar [11] propose iterate over steps 1-5 fully automated but lack evaluation and instantiation of their proposal. The dBOP approach [45,46] is a business process optimization platform consisting of three architectural layers that help to integrate, analyze, and optimize processes continuously (steps [1][2][3][4][5]. The dBOP enforces a rigorous methodology that may limit flexibility and creativity. ...
Article
For many organizations, the continuous optimization of their business processes has become a critical success factor. Several related methods exist that enable the step-by-step redesign of business processes. However, these methods are mainly performed manually and require both creativity and business process expertise, which is often hard to combine in practice. To enhance the quality and effectiveness of business process redesign, this paper presents a conceptualization of assisted business processre design (aBPR). The aBPR concept guides users in improving business processes based on redesign patterns. Depending on the data at hand, the aBPR concept classifies four types of recommendations that differ in their level of automation. Further, this paper proposes a reference architecture that provides operational support for implementing aBPR tools. The ra has been instantiated as a prototype and evaluated regarding its applicability and usefulness in artificial and naturalistic settings by performing an extensive real-world case study at KUKA and interviewing experts from research and practice.
... Business Process Management (BPM) plays a critical role in improving organizations' efficiency and competitiveness, through the optimization of internal processes, overcoming of organizational silos and reduction of waste [1]. However, the discipline has focused to a limited extent on disruptive process improvements [2] and, as argued by some researchers, may not be fit for the challenges of today [3]. ...
Chapter
Public sector organizations need to innovate their processes incrementally and radically to face political challenges. BPM ambidexterity, which is defined as the simultaneous pursuit of incremental and radical process innovation, provides a relevant lens to analyze this. Since there are only a few studies on ambidexterity in the public sector, we have conducted an embedded case study at an EU institution. We confirm that process characteristics, and the resulting culture and structure are critical enablers for the success of BPM initiatives. Departments with more mature practices for process optimization also turn out to be more open to radical process change. Moreover, temporal ambidexterity is the most common strategy to resolve the tension between incremental and radical innovation, while departments with a higher process orientation also show preference for contextual ambidexterity. We end with public sector guidelines on how to develop organizational capabilities in incremental and radical process innovation.
Thesis
Business processes are at the core of every organisation’s effort to deliver services and products to customers and, thus, achieve the organisation’s goals. The discipline that deals with the design, analysis, execution, and improvement of such business processes is called business process management (BPM). Over the years, the BPM research discipline has created a large number of methods and tools to support practitioners in managing and improving their business processes. In recent years, the increasing abundance of process data available in organisational information systems and simultaneous progress in computational performance have paved the way for a new class of so-called data-driven BPM methods and tools, the most prominent of them being process mining. This cumulative doctoral thesis concentrates on two challenges related to data-driven BPM methods and tools that impede faster and more widespread adoption. First, while data-driven methods and tools have found quick adoption in BPM lifecycle phases such as process discovery and process monitoring, the lifecycle phase of process improvement has so far been neglected. However, process improvement is considered to be the most value-adding BPM lifecycle phase since it is the necessary step to address existing issues in as-is processes or to adapt these processes to constantly changing environments and customer needs and expectations. Process improvement is often expensive, time-consuming, and labour-intensive, which is why there is a particular need to support process stakeholders in redesigning their processes. Second, there is a need for high-quality process data in all phases of the BPM lifecycle. In practice, process data, e.g., in the form of event logs for process mining, is often far from the desired quality and process analysts spend the majority of their time on identifying, assessing, and remedying data quality issues. Thus, in the BPM community, the interest in exploring the roots of data quality problems and the related assurance of high-quality process data is rising. Hence, it is essential to have a means for detecting and quantifying process data quality. Against this backdrop, this cumulative doctoral thesis comprises five research articles that present advances in process data quality management on the one hand and data-driven process improvement on the other hand. Taking on a design-oriented research paradigm and applying different qualitative and quantitative research methods, this thesis proposes several IT-enabled artifacts that support stakeholders in managing process data quality and improving business processes. The insights contained in this thesis are relevant for academia and practice as they provide both scientific perspectives and practical guidance. Concerning process data quality management, research article #1 presents an approach for (semi-) automated and quality-informed event log extraction from process-agnostic relational databases. It applies metrics for data quality dimensions that are relevant to process mining in order to quantify the data quality of the source data in selected database tables and simultaneously allows users to extract event logs in XES format from the database tables. Research article #2 presents an approach for detecting and quantifying timestamp data quality issues in events logs already present in XES format. The approach applies metrics for identifying timestamp imperfection patterns and allows users to interactively filter, repair, and annotate the event log. Furthermore, this thesis provides several concrete approaches to data-driven business process improvement. First, it focuses on process improvement in itself and aims to create artifacts for supporting process improvement initiatives. Therefore, research article #3 provides a model based on generative adversarial networks to create new process designs. Specifically, it uses event logs and annotated information on process variants and process deviance to generate a new process model which provides suggestions for process improvement to the user. Second, this thesis targets data-driven decision support in business processes. In particular, research article #4 uses multi-criteria decision analysis to extend traditional vehicle routing problems in last-mile delivery with a customer-centric perspective. The customer-centric vehicle routing uses process and customer data and the concept of customer lifetime values to predict customer satisfaction and, thus, optimise delivery routes. Finally, research article #5 presents a modelling approach for IT availability risks in smart factory networks based on Petri nets. The modelling approach uses modular components of information systems and production machines to model, simulate, and analyse production processes. The thesis concludes by pointing to limitations of the presented research articles as well as directions for future research. Overall, this thesis contributes to several important research streams in BPM while applying a broad range of qualitative and quantitative research methods such as simulation, normative analytical modelling, multi-criteria decision analysis, and interview studies within an overarching design science research paradigm. It builds upon and extends existing research on process data quality management and business process improvement.
Preprint
Full-text available
Business process management (BPM) drives corporate success through effective and efficient processes. In recent decades, knowledge has been accumulated regarding the identification, discovery, analysis , design, implementation, and monitoring of business processes. This includes methods and tools for tackling various kinds of process change such as continuous process improvement, process reengineering, process innovation, and process drift. However, exogenous shocks, which lead to unintentional and radical process change, have been neglected in BPM research although they severely affect an organization's context , strategy, and business processes. In this research note, we conceptualize the interplay of exogenous shocks and BPM in terms of the effects that such shocks can have on organizations' overall process performance over time. On this foundation, we identify related challenges and opportunities for BPM via several rounds of idea generation and consolidation within a diverse team of BPM scholars. We discuss our findings in light of extant literature from BPM and related disciplines, as well as present avenues for future (BPM) research to invigorate the academic discourse on the topic.
Conference Paper
Full-text available
Employees’ acceptance and resistance of new technology and social structure are frequently examined in Information Systems research. Resistance is expressed in various forms, including a lack of cooper-ation, workarounds, and physical sabotage. Workarounds, in particular, have a dual nature and can refer to both, undesirable behavior that contradicts organizational structure and to desired organiza-tional innovation. While antecedents and different forms of workarounds have been explored, literature has remained silent on how and why workarounds of an individual employee can affect activities per-formed by other employees and thereby, change work routines on an organizational level. Since em-ployees’ day-to-day performances constitute the ostensive patterns of a routine, we argue that worka-rounds will not only impact performances of adjacent routines, but also transform the organization as a social structure. With a preliminary set of qualitative data from 24 interviews, we used a multiple case study design to conceptualize six patterns that illustrate how and why workarounds can spread through an organization. The patterns are systematized by a framework that considers three types of collabora-tion and two types of handoffs across routines. This first evidence points at the nature of complex desired and undesired consequences that can emerge through workarounds performed in an organization.
Conference Paper
Full-text available
Event logs that originate from information systems enable comprehensive analysis of business processes, e.g., by process model discovery. However, logs potentially contain sensitive information about individual employees involved in process execution that are only partially hidden by an obfuscation of the event data. In this paper, we therefore address the risk of privacy-disclosure attacks on event logs with pseudonymized employee information. To this end, we introduce PRETSA, a novel algorithm for event log sanitization that provides privacy guarantees in terms of k-anonymity and t-closeness. It thereby avoids disclosure of employee identities, their membership in the event log, and their characterization based on sensitive attributes, such as performance information. Through step-wise transformations of a prefix-tree representation of an event log, we maintain its high utility for discovery of a performance-annotated process model. Experiments with real-world data demonstrate that sanitization with PRETSA yields event logs of higher utility compared to methods that exploit frequency-based filtering, while providing the same privacy guarantees.
Article
Full-text available
This paper summarizes a panel discussion at the 15th International Conference on Business Process Management. The panel discussed to what extent the emergence of recent technologies including machine learning, robotic process automation, and blockchain will reduce the human factor in business process management. The panel discussion took place on 14 September, 2017, at the Universitat Politècnica de Catalunya in Barcelona, Spain. Jan Mendling served as a chair; Gero Decker, Richard Hull, Hajo Reijers, and Ingo Weber participated as panelists. The discussions emphasized the impact of emerging technologies at the task level and the coordination level. The major challenges that the panel identified relate to employment, technology acceptance, ethics, customer experience, job design, social integration, and regulation.
Article
Full-text available
A fundamental assumption of Business Process Management (BPM) is that redesign delivers refined and improved versions of business processes. This assumption, however, does not necessarily hold, and any required compensatory action may be delayed until a new round in the BPM life-cycle completes. Current approaches to process redesign face this problem in one way or another, which makes rapid process improvement a central research problem of BPM today. In this paper, we address this problem by integrating concepts from process execution with ideas from DevOps. More specifically, we develop a methodology called AB-BPM that offers process improvement validation in two phases: simulation and AB tests. Our simulation technique extracts decision probabilities and metrics from the event log of an existing process version and generates traces for the new process version based on this knowledge. The results of simulation guide us towards AB testing where two versions (A and B) are operational in parallel and any new process instance is routed to one of them. The routing decision is made at runtime on the basis of the achieved results for the registered performance metrics of each version. Our routing algorithm provides for ultimate convergence towards the best performing version, no matter if it is the old or the new version. We demonstrate the efficacy of our methodology and techniques by conducting an extensive evaluation based on both synthetic and real-life data.
Article
Full-text available
As demand for data scientists in audit/Governance, risk management and compliance (GRC), and industry in general, outpaces supply, data science in a box—packaged analytics powered by artificial intelligence (AI) and guided machine learning—can bridge the gap to bring analytics to every major enterprise. Packaged analytics harness the power of AI and machine learning technologies to help operations, finance executives, and GRC professionals do their jobs better; optimize business processes; and deliver actionable insights for better decision making. This article will explore real-world case studies of how companies have used packaged analytics to achieve process improvements, better oversight over financial spend, and significant return on investment. It is a guide to internal auditors and their GRC counterparts on what is available and suggests they can partner or use the products independently and significantly contribute to their companies.
Chapter
In the domain of process discovery, there are four quality dimensions for evaluating process models of which simplicity is one. Simplicity is often measured using the size of a process model, the structuredness and the entropy. It is closely related to the process model understandability. Researchers from the domain of business process management (BPM) proposed several metrics for measuring the process model understandability. A part of these understandability metrics focus on the control-flow perspective, which is important for evaluating models from process discovery algorithms. It is remarkable that there are more of these metrics defined in the BPM literature compared to the number of proposed simplicity metrics. To research whether the understandability metrics capture more understandability dimensions than the simplicity metrics, an exploratory factor analysis was conducted on 18 understandability metrics. A sample of 4450 BPMN models, both manually modelled and artificially generated, is used. Four dimensions are discovered: token behaviour complexity, node IO complexity, path complexity and degree of connectedness. The conclusion of this analysis is that process analysts should be aware that the measurement of simplicity does not capture all dimensions of the understandability of process models.
Book
This textbook covers the entire Business Process Management (BPM) lifecycle, from process identification to process monitoring, covering along the way process modelling, analysis, redesign and automation. Concepts, methods and tools from business management, computer science and industrial engineering are blended into one comprehensive and inter-disciplinary approach. The presentation is illustrated using the BPMN industry standard defined by the Object Management Group and widely endorsed by practitioners and vendors worldwide. In addition to explaining the relevant conceptual background, the book provides dozens of examples, more than 230 exercises – many with solutions – and numerous suggestions for further reading. This second edition includes extended and completely revised chapters on process identification, process discovery, qualitative process analysis, process redesign, process automation and process monitoring. A new chapter on BPM as an enterprise capability has been added, which expands the scope of the book to encompass topics such as the strategic alignment and governance of BPM initiatives. The textbook is the result of many years of combined teaching experience of the authors, both at the undergraduate and graduate levels as well as in the context of professional training. Students and professionals from both business management and computer science will benefit from the step-by-step style of the textbook and its focus on fundamental concepts and proven methods. Lecturers will appreciate the class-tested format and the additional teaching material available on the accompanying website.
Book
Business Process Management (BPM) has become one of the most widely used approaches for the design of modern organizational and information systems. The conscious treatment of business processes as significant corporate assets has facilitated substantial improvements in organizational performance but is also used to ensure the conformance of corporate activities. This Handbook presents in two volumes the contemporary body of knowledge as articulated by the world' s leading BPM thought leaders. This first volume focuses on arriving at a sound definition of Business Process Management approaches and examines BPM methods and process-aware information systems. As such, it provides guidance for the integration of BPM into corporate methodologies and information systems. Each chapter has been contributed by leading international experts. Selected case studies complement these views and lead to a summary of BPM expertise that is unique in its coverage of the most critical success factors of BPM. "The practice of Business Process Management has progressed significantly since Michael Hammer and I wrote the Reengineering book. This Handbook presents the most complete description of the competencies required for BPM and exhaustively describes what we have learned about process management in the last 20 years." Jim Champy
Article
Business Process Management (BPM) is the art and science of how work should be performed in an organization in order to ensure consistent outputs and to take advantage of improvement opportunities, e.g. reducing costs, execution times or error rates. Importantly, BPM is not about improving the way individual activities are performed, but rather about managing entire chains of events, activities and decisions that ultimately produce added value for an organization and its customers. This textbook encompasses the entire BPM lifecycle, from process identification to process monitoring, covering along the way process modelling, analysis, redesign and automation. Concepts, methods and tools from business management, computer science and industrial engineering are blended into one comprehensive and inter-disciplinary approach. The presentation is illustrated using the BPMN industry standard defined by the Object Management Group and widely endorsed by practitioners and vendors worldwide. In addition to explaining the relevant conceptual background, the book provides dozens of examples, more than 100 hands-on exercises – many with solutions – as well as numerous suggestions for further reading. The textbook is the result of many years of combined teaching experience of the authors, both at the undergraduate and graduate levels as well as in the context of professional training. Students and professionals from both business management and computer science will benefit from the step-by-step style of the textbook and its focus on fundamental concepts and proven methods. Lecturers will appreciate the class-tested format and the additional teaching material available on the accompanying website fundamentals-of-bpm.org.