=Paper=
{{Paper
|id=Vol-2420/paperDC3
|storemode=property
|title=Process Mining as a Strategy of Inquiry: Understanding Design Interventions and the Development of Business Processes
|pdfUrl=https://ceur-ws.org/Vol-2420/paperDC3.pdf
|volume=Vol-2420
|authors=Bastian Wurm
|dblpUrl=https://dblp.org/rec/conf/bpm/Wurm19
}}
==Process Mining as a Strategy of Inquiry: Understanding Design Interventions and the Development of Business Processes==
https://ceur-ws.org/Vol-2420/paperDC3.pdf
Process Mining as a Strategy of Inquiry:
Understanding Design Interventions and the
Development of Business Processes
Bastian Wurm
Vienna University of Economics and Business,
Welthandelsplatz 1, 1020 Vienna, Austria
bastian.wurm@wu.ac.at
Abstract. Process (re-)design and improvement are important aspects
of the Business Process Management (BPM) life-cycle. Yet, there is lit-
tle empirical evidence on how design interventions materialize in actual
process execution, leading to repeated failure of such initiatives. In this
dissertation I use the emerging affordances of process mining algorithms
to address this important limitation. In particular, I devise a method
that combines process mining and grounded theory to study processual
phenomena. Consequently, this method is applied to investigate change
in business processes. This thesis contributes to the body of knowledge
in BPM and bordering disciplines by demonstrating how process min-
ing can be used as a method to study processual phenomena. Further
this research sheds light on the impact of design interventions on actual
process execution and vica versa.
Keywords: Process Mining · Methods · Computational-intensive The-
ory Development · Stability and Change · Process Design · Business
Process Management.
1 Motivation and Related Work
Business processes and organizational routines can both be described as ”struc-
tured set of action”. While both phenomena deal with how work is being ex-
ecuted in organizations, there is little empirical evidence on how their design
and redesign influences actual execution and vice versa. Thus, when processes
and routines are (re-)designed, companies stick to guidelines that are based on
experience, at best.
As a consequence, this limited understanding of how design interventions
materialize in process execution has led to repeated failure of such initiatives
[18] and the questioning of the role of artifacts in achieving process change [16].
In my dissertation I want to address this limitation by investigating the
research question: How does change in business processes take place?
I aim to answer this research question using a combination of traditional
grounded theory methodology and traditional computational theory develop-
ment [3]. On the one hand, I will use process mining algorithms [1, 2] to identify
2 B. Wurm
process variants [11] and evolutionary drifts in business processes [14]. On the
other hand, I will employ grounded theory methodology [19, 21] to complement
the computational theory development process and make sense of the data by
considering context information derived in interviews. With this work I expect to
identify motors of change in business processes [22] that will be used to explain
how process change takes place.
The remainder of this Ph.D. research proposal is structured as follows. In
the next section, I present an initial draft of the method I want to employ for
analyzing business processes, i.e. a combination of automated and manual theory
development [3]. In particular, I elaborate on the different types of data I plan
to use and how I intend to interpret them. Additionally, I show how process
mining algorithms can be used to detect change in business processes. Finally, I
provide a brief summary and outline the expected contribution of this work.
2 Process Mining as a Strategy of Inquiry for Processual
Phenomena
In this dissertation I suggest the complementary use of traditional grounded the-
ory methodology [7, 20] and computational theory development [8]. In a recent
article, Berente and associates [3] outlined the advantages of such computationally-
intensive theory development approaches that make use of the opportunities that
the ubiquity of digital trace-data provides.
2.1 Data and Sense-making
For this research, three types of data will be used: Trace-data in form of log-
files, qualitative interview data, and data on process documentation, i.e. process
models, process guidelines and other documentation materials. Table 1 gives and
overview over the different types of data employed, how they will be analyzed,
and what kind of information each of them provides for theory generation.
Fig. 1. Overview of Data and Analysis Techniques
Process Mining as a Strategy of Inquiry 3
First, trace-data will be analyzed using process mining techniques. Employing
variant analysis [11] and drift detection [14] allows to compare different process
variants and understand how a process evolves over time. At this stage, the main
goal is to derive a descriptive overview of the relevant processes.
Second, process documentation, i.e. process models, process guidelines, and
the like, are examined. Here, the main questions are of a teleological and nor-
mative nature. I.e., I want to collect information about the goals of a process
and how the process should be performed according to its designated design. For
example, different goals of a business process can be considered [5, 6].
Third, qualitative interviews with process experts and process managers pro-
vide contextual knowledge. The interviews will be interpreted using the grounded
theory method [7], relying on a lexicon [3] from BPM and routines research. This
knowledge further enriches the insights gained in the prior stages. In this stage,
I focus in particular on explanations about why the process is executed as it is
the case and why certain changes in the process occurred.
2.2 Process Mining Techniques for Detecting Patterns of Stability
and Change
Process mining is usually used for process discovery, conformance checking, and
enhancement [1]. However, more and more algorithms are developed that can be
used to compare different variants of the same process [11, 13] or detect changes
in processes over time [10, 12]. Both of these types of algorithms are fundamental
when it comes to detecting and understanding change in business processes.
Fig. 2. Example of Process Drift [10]
Figure 2 presents an example for (concept) drift [10]. Instead of analyzing
the whole log, the log is broken down in multiple parts, each of which is analyzed
individually. For this reason, it is essential to detect the change point (tc), i.e.
4 B. Wurm
the point in time when the change takes place, and accordingly divide the log-file
[4]. Based on this procedure, differences between different process versions can
be mapped out.
Drifts, i.e. changes, in processes can either take place gradually or suddenly
[4, 14]. Sudden drifts are major changes that emerge at a particular point in
time. They can be an indicator for major changes in the design of the business
process, e.g. when a newly designed process version is introduced. Gradual drifts
are small changes that appear over a stretched period of time [14]. They suggest
a slight alteration to the process behavior. This change in process execution can
be attributed to smaller design changes or to changes that can be attributed
to process participants. In fact, gradual drifts can be a hint for the presence of
positive deviance [15, 17].
2.3 Contextualization of derived Patterns
The presented algorithms give an example how process mining can enable in-
sights about how change and stability in business processes occur. However,
process mining alone can only determine that changes took place. Why changes
occur, the exact dynamics behind these changes, and the motivation for these
changes currently remain a black box. Together with interviews and process
guidelines/ documentation, a sense-making process can take place that contextu-
alizes the detected patterns and gives reason to not only that changes happened,
but provide additional knowledge how and why certain changes came about.
3 Expected Contribution
In this Ph.D. research proposal, I outlined the research background and design
of my doctoral dissertation. I presented a synthesis of process mining techniques,
qualitative interviews, and supplementary document analysis I want to employ.
This combination of computational and traditional techniques for inductive the-
ory development will be used in order to inductively generate theory that ex-
plains patterns of stability and change in business processes.
The contribution of this Ph.D. twofold. First, the devised method can be
used to study organizational processes. By iterating between trace-data and
qualitative data analysis, researchers can zoom in and out [9] on investigated
patterns; they can study patterns of actions as observed through process mining
and enrich this information by qualitative deep-dive. Second, the identification
of motors of change [22] in business processes sheds light on the impact of process
design on process execution and vica versa. Having those motors identified, future
studies can investigate further conditions for each motor to occur and the exact
mechanics how each motor operates. Consequently, guidelines can be specified
that help to support (re-)design initiatives.
This work is relevant for practice as well. Practitioners can use the identified
motors of change to anticipate how changes in process design affect changes
in process execution and the underlying routines. This enables management to
proactively accompany business process change initiatives.
Process Mining as a Strategy of Inquiry 5
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