Conference PaperPDF Available

Automated Process (Re-)Design

Authors:
Automated Process (Re-)Design
Maximilian R¨oglinger1,2[0000000347434511] , Christopher van
Dun1,2[0000000243178592] , Tobias Fehrer1[0000000287985724] , Dominik A.
Fischer1,2[0000000252186463], Linda Moder1,2[0000000323624839] , and
Wolfgang Kratsch1,2[0000000198150653]
1FIM Research Center, Universities of Augsburg and Bayreuth, Germany
2Project Group Business & Information Systems Engineering of the Fraunhofer FIT,
Bayreuth, Germany
{maximilian.roeglinger,christopher.vandun,tobias.fehrer,dominik.fischer,
linda.moder,wolfgang.kratsch}@fim-rc.de
1 Introduction
Business process management (BPM) continuously attracts academia and prac-
tice, 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].
Thus, it is considered an essential phase in the BPM lifecycle [15].
Today, organizations must overthink their business processes at an increas-
ingly fast pace, consider continuously rising customer needs, create novel process-
based 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 organiza-
tional 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.
While automation is prevalent in other BPM lifecycle phases (e.g., in process
execution) [1], process (re-)design commonly requires manual activities such as
traditional creativity techniques [15,22], making it time-consuming and labor-
intensive. Thus, automated process (re-)design holds high yet unexploited poten-
tial for long-term corporate success since it could accelerate process (re-) design
and make it more efficient as well as less dependent on human creativity.
2 Research Problem
2.1 Problem Description
Along the BPM lifecycle, many data-driven methods have recently emerged.
Enabled by the increasing volume of data, process mining techniques have been
developed to identify and discover process models based on process logs [2].
Predictive and prescriptive process monitoring techniques nowadays allow for
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2 oglinger et al.
Automated
process
(re-)design
Automated Improvement
Incrementally improve an existing
process automatically
Automated Innovation
Radically create a new process
automatically
Manual
process
(re-)design
Manual Improvement
Incrementally improve an existing
process manually
Manual Innovation
Radically create a new process
manually
Incremental Radical
process improvement process innovation
Table 1. Process (re-)design matrix
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 adop-
tion 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]. Although some
research obstacles still need to be overcome, ever more organizations adopt RPA
to reduce manual efforts when performing specific tasks in processes [20].
Despite all these automation efforts, it is remarkable that the BPM lifecycle
phase process (re-)design remains a manual task with a high level of cognitive
effort. To illustrate the level of automation in the context of process (re-)design,
we propose a 2x2 matrix along two continua (Table 1). The first continuum con-
cerns the degree of automation (manual to automated process (re-)design), the
second concerns the scope of process (re-)design (incremental process improve-
ment to radical process innovation). In the following, we describe the state-of-art
of process (re-)design within the introduced quadrants.
With a lens on incremental process improvement, various collections of pro-
cess redesign patterns and methods have been developed [8,12]. These collections
reduce the cognitive effort and guide process stakeholders in process improve-
ment. However, they neither replace manual effort nor do they leverage the
potential of tools in the redesign process. Initial approaches for semi-automated
process improvement have been developed (see [3]). Yet, these methods are at
the lower end of automation, as they generally guide improving processes in a
user-interactive way. Thus, there is still great potential to increase the level of
automation. Research is already striving to further automate process improve-
ment, enabling automatic exploration of beneficial process changes [9].
Focusing on radical process innovation, efforts also have been made to de-
velop guidance for creating new processes with new value propositions [13]. For
instance, Grisold et al. [14] created the “Five Diamond” method, which aims
to guide organizations in identifying opportunities from business and technol-
Automated Process (Re-)Design 3
ogy trends and integrating them into processes with novel value propositions.
Nonetheless, equivalent to manual improvement, the introduced method does
not support replacing manual efforts with automation. While automating pro-
cess improvement seems easier to realize, the automation of process innovation
proves to be an unsolvable problem to date. There is certainly still huge potential
in the area of automated innovation that has barely been exploited.
Overall, we conclude that process (re-)design is still predominantly a manual
task. The automation of process (re-)design, especially with a focus on process
innovation, undoubtedly remains a major hurdle to overcome.
2.2 Challenges to Overcome
Several characteristics of business processes and the complexity of the process
(re-)design task itself make the BPM lifecycle phase of process (re-)design stand
out and, therefore, prevent or at least complicate its automation. Such charac-
teristics are described here in broad strokes:
Process (re-)design requires creativity. Process (re-)design often requires
breaking with existing structures and routines within the process to create some-
thing new. Falling back on existing concepts might be a good idea for evolu-
tionary process improvements. Still, radical (re-)design relies on going beyond
what has already been there and exploring the whole solution space of (possibly
unknown) process (re-)design opportunities. In contrast to data-based improve-
ment, such explorative and innovative efforts mostly rely on “creativity”, i.e., the
use of imagination or original ideas to create something new. Creativity is often
described as an inherently human capability. Therefore, automating (re-)design
efforts requires advances in computational creativity.
Processes are multi-dimensional. (Re-)designing processes is not as straight-
forward as simply rearranging the sequence of activities within the investigated
process. Business processes are commonly conceptualized using five fundamen-
tal perspectives [23]. Besides the above-mentioned control-flow or behavioral
perspective, these perspectives relate to the functional elements of a process
(functional perspective), the assignment of tasks to human participants (organi-
zational view), the implementation of atomic activities (operational perspective),
and the information entities handled during individual tasks (informational per-
spective). All perspectives have to be considered in automated (re-)design efforts.
Processes are executed in context. Business processes are often part of an
organization’s process landscape and, therefore, situated within a complex net-
work of dependencies such as restricted resources, logical relationships [17], and
domain-specific characteristics [4]. This makes it very hard to consider process
(re-)design as a clearly delimited activity and, thus, complicates automation.
Processes are socio-technical. Processes are sets of activities in which humans
and technology co-create value [10]. Automated approaches in every phase of the
BPM lifecycle are constricted by what data is available on these activities. Pro-
cess mining can, e.g., only discover processes when their activities have left traces
in the involved information systems or have otherwise been recorded [16] and
when these traces are of high quality [4]. In return, this data represents only the
4 oglinger et al.
technical perspective on the process. Process and domain knowledge of human
agents participating in or being responsible for the process is essential in guiding
any (re-)design effort, making full automation impractical, if not unfeasible.
Processes are at drift. All organizational concepts are subject to unintentional
change, i.e., the deviation from their planned purpose over time. In a VUCA
world, processes are no exception, constantly suffering from gradual and incre-
mental changes over time called process or concept drift [6] or being radically
changed by disruptive shocks [18]. This impacts automated process (re-)design
activities twofold: First, due to such drift, processes are dynamic, constantly
changing, and event-driven artifacts that are difficult to fully capture, define, and
reinvent using high-level process models. Second, in a dynamic and changing en-
vironment, attempting to (re-)design business processes is “subject to resistance,
deals, side effects, and the properties of the IT landscape” [6, p. 193].
3 Directions Towards a Solution
Initial ideas towards a solution may involve approaches that leverage advances
in computational creativity, e.g., evolutionary computation [3] or generative ma-
chine learning [11]. Further, to accelerate process (re-)design, organizations could
automatically incorporate feedback into design suggestions to shorten reaction
cycles, e.g., via artificial intelligence-enabled process improvement tools and com-
plex predictive models that capture trends from data.
These initial ideas are beset with challenges themselves. For example, relying
on historical data could lead to new processes already being outdated at the time
of implementation. This demonstrates the need to address ancillary issues such
as real-time data deployment [7]. A fully automated workflow environment would
also be necessary to implement new process designs without delay. Additionally,
artificial intelligence often works as a black box and lacks explainability.
In conclusion, automated process (re-)design remains a relevant research gap
that should be explored further. However, even if fully automated process (re-
)design became feasible, new challenges would arise since organizations could
then easily develop new processes. The focus of competition could move from
talent to access to data or to the best forecasting models estimating the impact
of changes on the future or identifying the next relevant time for re-evaluation.
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... However, traditional approaches to process improvement are often subjective and biased as they rely heavily on human intuition and creativity. They also often involve significant cognitive effort and can be too rigid (Gross et al. 2020;Limam Mansar et al. 2009;Röglinger et al. 2021). Rarely is the full solution space of process improvement explored. ...
... Therefore, the issue of data-driven business process improvement remains a key gap in research Röglinger et al. 2021; van der Aalst 2013; Zuhaira and Ahmad 2020). ...
... Traditional process improvement methods are often associated with mostly manual, creative work, and high investments in terms of cost and time Gross et al. 2020;Huang et al. 2015;Limam Mansar et al. 2009). Thus, Röglinger et al. (2021) distinguish between manual and automated, data-driven process improvement methods and position the latter as a key challenge for future BPM research. In the meantime, some early concepts for data-driven process improvement have been developed: For example, Afflerbach et al. (2017) deploy evolutionary algorithms based on structured data on process activities and properties to generate improved processes. ...
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.
... While this research gap has been recognized in the liter-ature, no interactive and assistive approach combines both worlds in a guided process [13,11]: tool-based automation and guidance of BPR tasks on the one hand and the incorporation of domain expertise on the other hand. Thus, we formulate our research question as follows: How can assistive tools improve BPR? ...
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