11institutetext:  Process and Data Science (Informatik 9), RWTH Aachen
University, Aachen, Germany 22institutetext: Fraunhofer-Institut für
Angewandte Informationstechnik, Sankt Augustin, Germany
22email<EMAIL_ADDRESS>
# Removing Operational Friction Using Process Mining

Challenges Provided by the Internet of Production (IoP)

Wil M.P. van der Aalst 1122 Tobias Brockhoff 11 Anahita Farhang Ghahfarokhi 11
Mahsa Pourbafrani 11 Merih Seran Uysal 11 Sebastiaan J. van Zelst 1122

###### Abstract

Operational processes in production, logistics, material handling,
maintenance, etc., are supported by cyber-physical systems combining hardware
and software components. As a result, the digital and the physical world are
closely aligned, and it is possible to track operational processes in detail
(e.g., using sensors). The abundance of event data generated by today’s
operational processes provides opportunities and challenges for process mining
techniques supporting process discovery, performance analysis, and conformance
checking. Using existing process mining tools, it is already possible to
automatically discover process models and uncover performance and compliance
problems. In the DFG-funded Cluster of Excellence “Internet of Production”
(IoP), process mining is used to create “digital shadows” to improve a wide
variety of operational processes. However, operational processes are dynamic,
distributed, and complex. Driven by the challenges identified in the IoP
cluster, we work on novel techniques for _comparative_ process mining
(comparing process variants for different products at different locations at
different times), _object-centric_ process mining (to handle processes
involving different types of objects that interact), and _forward-looking_
process mining (to explore “What if?” questions). By addressing these
challenges, we aim to develop valuable “digital shadows” that can be used to
remove operational friction.

###### Keywords:

Process mining Internet of Production Operations Management.

##  1 Introduction

Data are collected about anything, at any time, and at any place. Of course,
operational processes in production, logistics, material handling, and
maintenance are no exception. In [5] the term _Internet of Events_ (IoE) was
introduced to reflect that machines, products, vehicles, customers, workers,
and organizations are increasingly connected to the internet and that our
capabilities to track and monitor these entities advanced in a spectacular
manner. Notions such as the _Internet of Things_ (i.e., all physical objects
that are able to connect to each other and exchange data) and _Industry 4.0_
(i.e., the fourth industrial revolution enabled by smart interconnected
machines and processes), reflect our abilities to monitor operational
processes.

The DFG-funded Cluster of Excellence “Internet of Production” (IoP) joins the
efforts of over 200 engineers and computer scientists with the goal to
fundamentally transform the way industrial production takes place by using
data-driven techniques (www.iop.rwth-aachen.de). A key concept within IoP is
the notion of a _digital shadow_ , i.e., automatically generated models that
can be used to control and improve production processes. Although IoP also
considers novel production technologies and materials science and engineering,
the lion’s share of IoP activities is devoted to improving production
processes. This position paper focuses on _operational processes_ that involve
_physical objects_ (products, machines, etc.) and have _discrete_ process
steps. One of the main goals of IoP is to remove _operational friction_ in
such processes. Much like physical friction, operational friction refers to
phenomena that consume additional effort and energy and lead to less-than-
optimal results. Examples of operational friction are unnecessary rework,
delays, waste, deviations, fraud, missing products, unresponsiveness, and
communication loops.

_Process mining_ can be used to remove operational friction by making
conformance and performance problems visible in such a way that it becomes
clear what the root-causes of such problems are [5]. Starting point for
process mining are the event data mentioned before. Events may take place
inside a production facility, inside a transportation system, inside a
product, or inside an enterprise information system. Events may be triggered
by people, machines, or organizations. _Process discovery_ techniques use
event data to create process models describing the operational processes in
terms of their key activities. These process models reveal the actual
processes and can be extended to show bottlenecks and outlier behavior.
_Conformance checking_ techniques compare observed behavior (i.e., event data)
with modeled behavior (i.e., process models). These techniques can be used to
show deviations, i.e., behaviors different from what is expected or desired.
Process models may also include probabilities, time distributions, and
business rules. Therefore, process mining also includes a range of techniques
enabling _predictive and prescriptive analytics_.

The process models created and used by process mining techniques form the
_digital shadows_ that are intended to manage, control, and improve
operational processes. The notion of a digital shadow is at the core of IoP.
The notion of a digital shadow is closely related to the notion of a _digital
twin_ [30]. Digital twins are complex models providing digital counterparts to
physical production artifacts (i.e., the physical twins), e.g., detailed
simulation models that update and change as their physical counterparts
change. The digital shadow concept is broader and includes the traces of the
actual production processes, i.e., _digital shadows based on process mining
include both process models and actual event data_. For example, when
analyzing a bottleneck, it is possible to use the process model to drill-down
into event data that caused the problem. Moreover, the process model and event
data can be used to predict the trajectory of current process instances and
anticipate the effect of interventions. This makes process mining a key
ingredient of IoP.

The process mining discipline emerged around 20 years ago [5], and today there
are over 35 commercial process mining tools (e.g., Celonis, Disco,
UiPath/ProcessGold, myInvenio, PAFnow, Minit, QPR, Mehrwerk, Puzzledata,
LanaLabs, StereoLogic, Everflow, TimelinePI, Signavio, and Logpickr). Many of
the larger organizations (especially in Europe) have adopted this technology.
For example, within Siemens over 6000 employees are using Celonis Process
Mining to remove operational friction and increase automation. Despite the
widespread adoption of process mining and the availability of easy-to-use
commercial tools, the process mining discipline is relatively young and there
are still many open challenges. In this position paper, we focus on three
questions particularly relevant for IoP:

  * •

_How to compare different process variants (over time, over locations, over
different case types)?_ The same production process may be performed at
different locations. It is valuable to understand why operational friction is
less at some of these locations. Also, the performance may not be constant
over time and we may see drifts, e.g., bottlenecks are shifting. Therefore, we
need to support comparative process mining [2, 3, 17, 25]

  * •

_How to deal with processes involving different interacting objects?_
Traditionally, each event refers to a single case (e.g., an order). This is
made possible by flattening event data. This may lead to convergence and
divergence problems. The same event may be replicated for multiple cases or
unrelated events may appear to be related. This leads to misleading
diagnostics. Moreover, depending on the question at hand, the event data need
to be extracted differently from systems such as SAP, Oracle, and Microsoft
Dynamics. Therefore, we need to support _object-centric_ process mining
techniques that are able to handle different types of objects (i.e., case
notions) in one model [7].

  * •

_How to improve the process using forward-looking techniques?_ The initial
focus of process mining was on diagnosing historical event data. However, the
ultimate goal is to improve processes and remove operational friction.
Therefore, we need process mining techniques that are _forward-looking_ and
that provide _actionable_ results, e.g., automated alerts, interventions,
reconfigurations, policy changes, and redesign. To anticipate the effect of
process interventions, a tighter integration with simulation is needed [1, 6,
39, 46].

The remainder of this position paper is organized as follows. Section 2
introduces an overview of process mining techniques. Section 3 introduces a
running example based on which we discuss the potentials and challenges of
applying process mining to operational processes from a research and process
owner’s perspective. Section 4 uses the running example to illustrate the
capabilities of existing process mining tools. Section 5 discusses the need
for comparative process mining and presents some of the initial capabilities
to support process cubes, concept drift, and process comparison using advanced
concepts such as the earth mover’s distance. Section 6 presents the problems
related to convergence and divergence in processes due to events that refer to
multiple objects. Initial support for object-centric process mining techniques
based on process cubes is presented. In Section 7, we advocate a shift from
backward-looking process mining to more forward-looking forms of analysis
combining simulation and process mining. Next to traditional discrete event
simulation with models generated from event data, we also need simulation
approaches operating at a higher abstraction level. For this purpose, we
propose to combine process mining and system dynamics. Section 8 concludes the
paper.

##  2 Process Mining: Removing Friction in Operational Processes

To help organization to remove operational friction, process mining reveals
unnecessary rework, delays, waste, deviations, fraud, missing products, lost
responses, etc. This is not so easy. As an example, take the Order-to-Cash
(O2C) process of a large multinational that processes over 30 million ordered
items per year. These 30 million cases (i.e., instances of the O2C process)
generate over 300 million events per year. Over 60 different activities may
occur. Although the O2C process is fairly standard, over 900,000 process
variants can be observed in one year! These variants describe different ways
of executing this process. In such processes, often 80% of all cases are
described by just 20% of the process variants. However, the remaining 20% of
the cases generate 80% of the process variants and are often responsible for a
disproportional part of an organization’s operational friction. Such problems
cannot be tackled by traditional process modeling or workshops, because these
focus on the dominant 20% of the process variants. Process mining aims to
provide insights into real-life operational processes using the event data at
hand.

Figure 1: An overview of process mining showing how event logs, process
models, and diagnostics relate.

The starting point for traditional process mining is an _event log_ extracted
from the information system(s). Each _event_ in such a log refers to an
_activity_ possibly executed by a _resource_ at a particular _time_ and for a
particular _case_ [5]. An event may have many more attributes, e.g.,
transactional information (e.g., start, complete, and abort), costs, customer,
location, and unit. Typical challenges are _data quality_ and finding a
_suitable case notion_. A typical data quality problem is the granularity of
the timestamps in the event log. Some events may only have a date (e.g.,
29-9-2020) and are manually recorded, whereas other events may have a
millisecond precision (e.g., 29-9-2020:15.36.45.567) and are captured fully
automatic. As a result, the precise ordering is unknown or uncertain. Events
are grouped into traces using the selected case notion. However, as detailed
in Section 6, this is not always easy. One event may refer to one order, ten
parts, two machines, one person, and a location. Different events may share
objects and one event may refer to many objects.

As Figure 1 shows, event data are used for process discovery, conformance
analysis, and performance analysis [5]. _Process discovery_ techniques
automatically learn process models based on an event log. A process model can
be represented using different modeling languages, e.g., Petri nets, BPMN
models, UML activity diagrams, DFGs, automata, and process trees. Such a
process model specifies how a case can be handled. The simplest model is a so-
called Directly Follows Graph (DFG). The nodes in such a model are the
activities and two special nodes: the start and end of the process. The arcs
describe how the process transitions from one node to another. Arcs can be
decorated with frequencies and mean durations. For example, 500 times activity
“send invoice” was followed by activity “make payment” and the average time
between both activities was 2.45 days. DFGs are simple and can be constructed
efficiently, also for huge events logs. However, DFGs fail to capture
concurrency and often lead to spaghetti-like underfitting models. Petri nets,
BPMN models, UML activity diagrams, and process trees are able to express
concurrency and there are many process discovery algorithms to discover such
models. Here, we abstract from the actual representation of process models and
focus on the more general concepts.

Conformance checking techniques take as input both a process model and an
event log. The event log is replayed on the model to uncover discrepancies
between the observed and modeled behavior. It may be the case that an activity
occurs although this is not possible according to the model, or an activity
should have occurred according to the model, but did not happen in reality. It
is also possible to see that behavior allowed by the process model never
happens. If the process model is extended with temporal or resource
constraints, conformance checking may reveal that something takes too long or
is performed by the wrong person.

By replaying the event data on the model, it is also possible to show
performance problems. Since events have timestamps, it is possible to measure
delays, next to routing probabilities. Even when the event data do not
completely fit the model, it is possible to “squeeze” the data into to model
using alignment computations. Process models with performance information can
be used to analyze bottlenecks and other performance problems. Moreover, by
combining stochastic models learned from historic event data with event data
from running cases, it is possible to predict performance and compliance
problems.

Figure 2: Different research directions relevant for the improvement of
operational processes: _comparative_ process mining, _object-centric_ process
mining, and _forward-looking_ process mining.

Although existing process mining techniques and tools have proven to be a
powerful means to remove operational friction, several challenges remain. In
the context of our Cluster of Excellence “Internet of Production” (IoP), we
address the three challenges already mentioned in the introduction. Figure 2
sketches these challenges and corresponding solution directions. To compare
different process variants (over time, over locations, over different case
types) we use process cubes and novel techniques to compare subsets of events
(see Section 5). Process cubes store event data using any number of
dimensions. Novel techniques based on the Earth Mover’s Distance (EMD) notion
are used to compare two different event logs (or a log and model) while also
considering time and resources (also detailed in Section 5). To deal with
processes involving different interacting objects, we use object-centric event
logs and corresponding process mining techniques (detailed in Section 6).
Traditional process mining techniques require the selection of a single case
notion and each event refers to precisely one case. However, for the types of
processes we have in mind, this is often too restrictive. Each event may refer
to any number of objects and the case notion is just a view on the process.
Objects may refer to physical products (raw materials, end products,
intermediate products, etc.), resources, or information. Resources include
machines, people, space, etc. To make process mining more forward-looking, we
support different types of simulation. This allows us to anticipate the
effects of process interventions and use process models as highly informative
digital shadows. Next to traditional Discrete Event Simulation (DES), we also
support System Dynamics (SD) techniques that view the process at a higher
aggregation level (see Section 7). For SD models, we do not simulate the
individual events, but use aggregated steps representing different time
periods (e.g., days or weeks).

##  3 Running Example

Various types of operational processes can be found in industry. Automotive
production lines are a common type of production lines that are used to
execute operational processes. Automotive production lines are highly
structured and activities therein are tightly coupled. In a simple production
line, multiple assembly lines are coordinated in order to join in a single
major assembly line. Due to the variability of activities in the automotive
production lines and the strong dependencies between them, they are suitable
for process mining analysis. Therefore, in order to illustrate process mining
techniques for operational processes, this section introduces an extendable
automotive production line model. The model is based on a production line at
e.GO Mobile AG, a local car manufacturer located in Aachen. Due to reasons of
confidentiality, we resort to simulated data and present our results without
utilizing the real data of e.Go. A fragment of the simulated model is shown in
Figure 3. It shows the production line is built around a sequence of general
assembly stations where some stations depend on additional sequential sub-
assembly stations. For example, the station _SA9_ is a prerequisite for the
general assembly station _GA17_. Each station can have at most one car at a
time, during which the human operators execute specific tasks before the car
proceeds to the next station. A car can only move along the general assembly
line if the subsequent station is empty. We consider 28 stations in the
general assembly line and 33 stations in the sub-assembly line. Several
activities are executed at one station and most of the time the order of
activities at the stations does not matter. The production line is active
between 8:00-17:00 o’clock and 5 days a week.

Figure 3: A part of the simulated model of the production line, used for
designing the simulation model. The production line includes one general
assembly line with 28 stations and 33 sub-assembly stations.

In the following sections we demonstrate how process mining is able to help a
company to steer and evolve their operational process. To this end, we extend
the basic model in order to illustrate the challenges mentioned in the
introduction. In Section 5, we present _comparative process mining_ techniques
to answer the following questions:

  * •

How to compare performance-related factors of two different factories in two
different locations?

  * •

How to investigate resource allocation in two different factories?

In Section 6, we discuss _object-centric process mining_ approaches to answer
following questions:

  * •

How to deal with real-life data of the car factory extracted from information
systems such as ERP systems in process mining?

  * •

How to address challenges arising from having multiple interacting objects,
e.g., order, products, and customers in processes?

In Section 7, we present _forward-looking process mining_ methods to answer
following questions:

  * •

How to increase the monthly production rate of cars?

  * •

Does a temporary queuing station for the cars help to increase the overall
production rate?

Whereas the emphasis of this paper is on advanced process mining techniques,
we first present a few common process mining techniques, e.g., basic
approaches for process discovery and conformance checking. These examples
diagnostics also help us to obtain an overview of the process used throughout
this paper.

##  4 Applying Existing Process Mining Techniques

Process mining provides a wide variety of techniques for process analysis [5].
Examples of process mining techniques include process discovery algorithms
that are able to find process models that describe the event log, conformance
checking algorithms that compare process models with the event logs, and model
enhancement algorithms that enrich the process model with additional
information extracted from the event log. To gain more insights about the
process, we apply standard process mining techniques (see [5] for a
comprehensive introduction). First, in Section 4.1, we perform an explorative
analysis by means of process visualization techniques, i.e., the dotted chart.
In Section 4.2, we discover a process model of the generated data. In Section
4.3, we highlight deviations between the discovered process model and the
event log by applying conformance checking techniques. In Section 4.4, we
address the performance problems in the production line through bottleneck
analysis.

###  4.1 Exploring Event Data

Explorative process analysis is a common starting point in order to obtain an
overview of the execution of the process. A widely adopted tool that
interactively visualizes multiple perspectives of an event log is the dotted
chart [49]. Figure 4 presents the dotted chart for the generated data where
each event is shown by a dot, colored according to the station it originated
from. The x-axis and y-axis show time and the car id respectively. There are
different ways of sorting the events in the dotted chart. However, in this
dotted chart, events are sorted based on the case duration. As indicated in
the figure, there are gaps between events that show the period in which no
event occurs such as nights and weekends. We can observe 80/20 rule, also
known as the Pareto principle in the dotted chart. Approximately 20% of the
cases last exceptionally long. Following the Pareto principle, 80% of the
performance problems (e.g., delay in the production line) are caused by 20% of
the cases. Clearly, these 20% of all the process instances are interesting
cases for further analysis.

Even though process visualization by means of a dotted chart helps the
analysis, most of the analysis by using the dotted chart is empirical and not
quantified. Thus, other process mining techniques such as process discovery
and conformance checking techniques are required to find actionable results
for improving operational processes.

Figure 4: Dotted chart for the generated event log. Working hours and working
days are demonstrated in the dotted chart. Besides, we observe a big gap
between events in 20% of the cases, lasted longer than normal.

###  4.2 Process Discovery

Although the visualization approach, discussed in Section 4.1, provides an
overview of the event data, it does not show the relationship between
different activities. Process discovery techniques take an event log as an
input and generate a process model that captures the relation between
activities and abstracts the observed behavior in the event log.

There are various techniques for process discovery, e.g., the Alpha-algorithm
[12], the Inductive Miner [26], Region-based approaches [5], and the
Heuristics Miner [51]. For the resulting models, there are multiple different
notations, e.g., Petri nets [14], BPMN [21], YAWL [24], UML [11], etc. Figure
5 shows a fragment of the BPMN model for the example log by applying Inductive
Miner. As the model shows, the general assembly stations are sequential and
some of them have prerequisites, i.e., sub-assembly stations. For example, we
start triggering the execution of activities at the station _GA14_ , when
execution of activities at the stations _SA1_ and _GA13_ have been finished.
Another aspect, captured by BPMN model, is concurrency. While the execution of
the activities at the stations _GA0_ , _GA1_ , …, and _GA13_ is sequential,
they are all in parallel with the execution of the activities at the station
_SA1_. Therefore, in addition to the sequential relations, this discovery
reveals the concurrency in activity executions. Note that in this discovery,
we focused on the control-flow perspective. However, process discovery is not
limited to the control flow perspective and it can also be applied to other
perspectives of the process, such as the organizational perspective [5].

Figure 5: An example process model of the production line using BPMN notation
[21]. The flow of activities for cars during the production line is shown.
Activities at general assembly stations are always executed in a sequence.
Also, activities at sub-assembly stations, required for a specific general
assembly station, are executed sequentially.

###  4.3 Conformance Checking

Given a process model, conformance checking assesses the similarity of the
behavior described by the model and the real behavior as recorded in the event
log. Therefore, conformance checking techniques [13] are used to identify
deviations of the production plan. Since the event data used originate from an
idealized simulation model, there are no deviations in the initial setting.
However, we manipulate the simulation model such that for some stations
deliberately deviations are injected. One of the common techniques, used in
conformance checking, are alignments. In alignments, we check the
synchronization between the behavior in the event log and behavior according
to the model [8]. An example of alignments is depicted in Figure 6 which shows
a fragment of the mined BPMN model with the results of conformance checking.
As this figure shows, using conformance checking, deviations of the example
process from the previously mined model are easily detected and visualized.
Two numbers in parenthesis, below the name of the station, clarify the number
of deviations. For instance, for 27 cars, activities at the station _SA4_ have
not been executed before activities at the station _GA16_ and after the
activities at the station _SA3_. These cars have skipped the station _SA4_ and
they may cause quality problems after the production. Accordingly, identifying
these types of deviations in the production line can help the production line
managers to control the quality and steps of the process accurately.

Figure 6: A part of the detected deviations in the process. Activities with
red square show deviations while activities with green square show that there
was no deviation in that activity.

Figure 7 shows the results of conformance checking using the alignments-based
technique. In alignments, we check for a car, whether the behavior of the
model and the behavior of the events related to that car match with each other
or not. We have two types of misalignment which are model move and log move.
In model move, a move in the log cannot be mimicked by a move in the model. In
Figure 7, there are two model moves (purple color). For example, based on the
model, we need the station _SA7_ in the event log between the stations _SA6_
and _SA8_. However, it is missed in this case. In log move, a move in the
model cannot be mimicked by a move in the log. In Figure 7, there is a log
move (yellow color). Based on the event log, this car moves from the station
_GA21_ to the station _SA7_ and from the station _SA7_ to the station _GA22_.
However, it cannot happen according to the model. Typically, by capturing such
misstatements, we can discover the deviations that affect the quality of the
final product. However, in order to increase the performance of the production
line we need to analyze the performance of the stations to find the
bottlenecks.

Figure 7: An alignment for a specific car. A car ideally passes 61 stations.
However, there are mismatches between the discovered model and the event log,
captured in the alignment by different colors.

###  4.4 Performance Analysis

The performance of production lines is one of the important metrics which
needs to be observed and improved continuously [8]. After verifying the
suitability of the given model using conformance checking, this section
assesses the performance of the process by means of bottleneck analysis. In
Figure 8, we depict a projection of performance diagnostics, on top of the
discovered process model. Within the figure, the darker colors of the stations
show performance problems and bottlenecks in the process. For instance, the
dark red color of the stations _GA3_ , _GA4_ , and _GA5_ indicates the long
service times at these stations (i.e., the time actually worked on the car).
Most of the time, improving the performance of bottlenecks such as the station
_GA6_ improves the overall performance of the process. In general, process
mining bottleneck analysis reveals this kind of problems and subsequent
actions can be taken accordingly. In this performance analysis, we considered
service time of the stations. However, it is possible to analyze the
performance based on the waiting times for the different stations (i.e., the
time that a car is waiting for the station to become available).

Figure 8: Snapshot of the performance view of the general assembly line. The
average service times of the stations are shown. Figure 9: Process cubes are
built on the event logs extracted from information systems. We detect the
change point in the regular patterns of the process by concept drift
detection. Therefore, based on the change point, we apply process cube
operations such as _slice_. The output of such process cube operations are
sublogs, used for process comparison.

##  5 Comparative Process Mining

The techniques presented in Section 4 capture the current state of the process
using the complete event log. In this analysis, we do not consider variability
in the processes which can be derived from the heterogeneity of demand in
different seasons. In order to isolate process variations, process comparison,
which systematically investigates the presence and absence of systematic
differences, has recently gained interest. The application is not limited to a
single process instance but can also consider multiple instances of similar
processes. For example, suppose that we have two car factories in two
different locations, implementing the production lines described in Section 3.
Comparing performance-related metrics of these two factories with each other
or even comparing the performance metrics of a single company in different
time windows of a year is valuable from the business perspective.
Characteristics related to the performance, such as the duration of the
underlying production process for the comparison of the two factories, are of
importance in operational management. This comparison can be addressed by
splitting event data into process variants by using existing process mining
approaches in a way that differences between variants are exposed. To provide
better insights into current approaches for process comparison, we first show
how event data can be organized considering different dimensions of
variability, which enables basic process comparison in Section 5.1. While the
former method assumes prior knowledge about the expected variability, Section
5.2 assesses how this can naturally be complemented by process change
detection approaches and the emerging challenges for operational processes.
Finally, in Section 5.3, given the methods to organize the data, we introduce
a challenging process comparison scenario, which motivates further research on
performance-oriented operational process comparison.

###  5.1 Process Cubes

Classical process mining techniques focus on analyzing a process through
processing its corresponding event log as a whole. However, there may exist
variability in the process. To consider this variability in the process, we
use process cubes which isolate the different processes. As shown in Figure
10, a process cube consists of multiple dimensions that refer to the
properties of the event log, e.g., time, color, and location. Each cell in
this process cube refers to all events related to the cars with a specific
color, in a specific region, and in a particular time window. To gain more
insights about the specific cell, process mining techniques, e.g., process
discovery and conformance checking techniques can be applied to a collection
of events extracted from the cells of the process cube [17].

Figure 10: An example of a three-dimensional process cube. The selected cell
contains all events related to white cars produced in Brussels in _2017_.

The notion of process cubes is inspired by application of Online Analytical
Processing (OLAP) in process mining [44]. OLAP is a well-known concept in
Business Intelligence (BI) that facilitates data discovery and management
reporting. First, OLAP considers simple numerical analysis, e.g., plotting car
production duration in a factory against the months of the year. In [20, 29,
31], OLAP operations, i.e., _slice_ , _dice_ , _drill-down_ , and _roll-up_
were also applied to non-numerical data. Although the idea of the process cube
is related to OLAP, there are significant differences between OLAP and process
cubes. We can not use OLAP for process data directly, because, for example, as
a design choice in a process cube, cells are associated with both process
models and event data. In a process cube, event data and process models are
directly related. We can discover models from event data extracted from cells
of the cube and compare observed and modeled behavior [2]. Different
implementations of process cubes are provided in [16, 33]. In these
implementations, we can apply OLAP operations such as _slice_ , _dice_ ,
_roll-up_ , and _drill-down_ on the event data. Assume we are interested in
discovering the model of cars produced in the years _2017_ and _2018_. As
shown in Figure 11, we can slice the cube for the _time_ dimension, for the
years _2017_ and _2018_ and find the process model of the remaining events. In
fact, through _slicing_ the cube, we zoom into a _slice_ of the data and
remove that dimension from the cube. However, by _dicing_ , we apply a filter
on multiple dimensions. Assume we are interested in discovering the model for
cars with white and blue color produced in years _2017_ and _2018_. As shown
in Figure 11, we can dice the cube for dimensions _color_ , and _time_ and
discover the model of the remained events.

Figure 11: Example of _slicing_ and _dicing_ operations in process cubes. A
_slice_ of the cube for the _time_ dimension and _dice_ of the cube for the
_time_ and _color_ dimensions are shown.

_Roll-up_ and _drill-down_ operations, show the cube with different levels of
granularity. An example is shown in Figure 12. We can drill down the
_location_ dimension from _country_ to _city_. _Roll-up_ is the opposite of
_drill-down_ operation. It performs aggregation on a dimension. As shown in
Figure 12, we can roll up the _location_ dimension from _city_ to _country_.

Figure 12: Example of _rolling up_ and _drilling down_ operations in process
cubes.

A brief example of the application of process cube operations for the
generated data is shown in Figure 13. In this figure, the models of two
different _slices_ corresponding to two different seasons of a year are
compared to each other. We can compare performance of the stations with each
other through performance analysis. As indicated in the figure, the average
time of execution of activities at the station _GA5_ has increased in the
second season of the year.

Figure 13: Example of comparing the company in two different time windows.

Although there are several implementations of process cubes, some challenges
remain. In OLAP, we reduce a set of values into a single summarized value such
as the average. However, process cubes deal with event data and we cannot
simply reduce many events into one event. The ways that are used for
summarizing the events affect the performance of the process cube.
Consequently, defining a way for summarizing the events in the process cube
with high performance is an open issue.

###  5.2 Concept Drift

Even though operational processes should ideally be stable, complex internal
and external dependencies will typically lead to continuous changes of the
process. The resulting variability is, on the one hand, problematic for
traditional process mining techniques, which implicitly assume steady
behavior. On the other hand, changes can also require an immediate adaptation
by the process managers. In principle, we can attribute the aforementioned
changes to external factors, e.g., suppliers, market demand, or political
regulations, and internal factors, e.g., operators, minor changes of the
_Bill-of-Material_ (BOM), or machines. While the former are under limited
control of the company, evaluating their effect by means of comparative
process mining can support mid- and long-term decision making. In addition,
controlling the latter, in particular, has the potential to continuously
improve and steer the operational process. Therefore, _concept drift
detection_ , as a field of research that is concerned with detecting
systematic changes in processes, is important for monitoring operational
processes and providing entry points for further analysis.

A general challenge for methods that systematically assess changes in the
execution of a process arises from the different natures of occurrence and
duration [18]. For example, _sudden drifts_ are caused by rapid process
changes and might require instant action while _gradual drifts_ describe
slowly changing processes where different behaviors overlap. Concerning the
long-term development of the process, sequences of multiple changes, i.e.,
_incremental drifts_ and _recurring drifts_ , which describe periodical
behavior changes, can be distinguished. On the one hand, detecting them is
essential in order to understand the overall process evolution. On the other
hand, being aware of recurring, e.g., seasonal changes enable process owners
to anticipate and adapt while also setting the context for additional
analyses.

While existing work focuses on the data [25] and especially the control-flow
perspective [32, 18, 19], it neglects time, such as waiting times or activity
service times, as a factor of major concern in operational processes. As in
many operational processes activities are highly structured and tightly
coupled, the impact of small delays can accumulate causing a significant
decrease in the overall performance. For instance, in our running example
delays at sub-assembly stations can temporarily stop significant parts of the
entire production line. However, changes such as a battery shortage do not
change the ground truth control flow but increase the sojourn times at
affected stations making time-aware drift detection essential. For example,
consider the rolling mean sojourn times over the last ten vehicles for a
selected number of stations that are depicted in Figure 14. While it shows two
increases for the stations _GA4_ and _GA5_ after the first 350 and 600
vehicles, respectively, the mean sojourn times for station _GA6_ are not
affected. This indicates a concept drift that worsens the situation at the
bottleneck station _GA5_ blocking the preceding stations and, therefore,
increasing their sojourn times. Using additional background knowledge, this
drift can be attributed to problems with the battery supply and, later on, a
severe battery shortage.

Figure 14: Mean sojourn time for stations _GA4_ , _GA5_ , and _GA6_ over the
last ten vehicles. It shows two drifts for _GA4_ and _GA5_ after 350 and 600
cars, respectively.

In general, detecting time drift and its causes becomes especially challenging
if changes are not local to a station but distributed over the production
line.

In addition to the time perspective, change point detection methods that
generally allow for different perspectives on the operational process are
essential in order to incorporate domain knowledge, e.g., from the BOM.
Consequently, this enables the user to focus the sensitivity of the detection
method on specific parts of the process while reducing the sensitivity for
irrelevant changes. For example, the user might focus on changes of important
quality metrics and changing impacts of preceding production steps on the
former instead of detecting changes in operator-dependent activity orderings.
Apart from the change identification, a good visualization showing the nature
of the change [18] is crucial to make changes actionable. While change
characterization of the control flow has been addressed in [36, 37], change
characterization of operational processes should acknowledge additional
perspectives.

Consequently, in order to address these challenges and unravel the process
dynamics, holistic and especially time-aware change point detection approaches
with a multi-perspective view on the operational process are needed.

###  5.3 Comparison

Applying the concepts from Sections 5.1 and 5.2, allows us to organize the
data into process cubes, potentially refined by concept drift detection
results. Subsequently, this organization provides us a framework and entry
points for process comparison along the dimensions of variability which can be
challenging even in case of a stable control flow. Depending on the inter-
activity dependencies, pairwise activity comparison between two process
variants can be insufficient and activity relations, i.e., the process
knowledge need to be incorporated.

In order to illustrate the challenges for time-centered process comparison
introduced by inter-activity dependencies, we complement the baseline model by
a human factor, which expresses in operator-dependent service times, and
compare two factories of the same car manufacturing company at different
locations. Similar to the workforce assignment in the underlying real-world
process of the electric vehicle manufacturer, we divide the production
pipeline into multiple sections. Each section has a separate pool of operators
from which free operators are assigned to the manufacturing tasks. Moreover,
we also model operator preferences for certain stations. Accordingly, when a
vehicle arrives at an assembly station, a free operator is randomly assigned
based on the current list of preferences in the pool. We generate events for
two factories located in the Netherlands and in Belgium and store them in a
process cube.

The process owner is interested in comparing the two implementations of his
manufacturing process in order to identify possibilities for improvement. To
this end, we first _slice_ and _dice_ the process cube in order to generate a
sublog for each location. As we show next, comparison methods should consider
the inter-activity dependencies in order to provide a holistic analysis to the
company. Activity-wise comparison, as depicted in Figure 15(a), yields that
the station duration distributions for the station _GA6_ are similar to each
other, while the distributions differ for the stations _GA4_ and _GA5_. For
the station _GA5_ , the station duration distribution has a longer tail in the
Dutch factory, which indicates inefficiencies for the Dutch location.
Considering station _GA4_ , the distribution for the Netherlands has two
peaks(but not for the location in Belgium) suggesting that it is possible to
considerably improve processing times at this station.

(a) Activity-wise comparison.

(b) Relations between the station durations in the Netherlands.

(c) Relations between the station durations in Belgium.

Figure 15: A comparative analysis of the times spent in the different stations
in both the Netherlands and Belgium.

However, a process-oriented view, as shown in Figures 15(b) and 15(c), that
considers additional context information, yields a different conclusion. The
figures show the relations between subsequent stations where traces are
colored according to their time at the station _GA5_. Figure 15(b) shows that
the time improvement for the station _GA4_ correlates with an increased
duration at the station _GA5_. Quick but imprecise work at this station that
requires additional rework increases the overall effort. Process comparison
reveals this process insight and the absence of this relation in the Belgian
factory. Furthermore, the modes of station _GA5_ in the Belgian factory seem
to be uncorrelated with the time for station _GA4_ suggesting the existence of
an additional latent variable. Comparison with Figure 15(b) and the absence of
a corresponding pattern in Figure 15(c), further suggest that this latent
variable is not inherent in the baseline process, but related to the location.
For example, different operators working at different speeds could cause this
behavior rather than imprecisely fitting parts, causing problems in the next
step.

This example shows that, although simple comparison approaches can provide
insights into the differences between the processes, they might not consider
all the relevant context information. This motivates the use of flexible
comparison methods such as the _Earth Mover’s Distance_ (EMD) [23, 47]. On the
one hand, these methods should be sensitive to the frequency of the occurring
patterns and thus to their relevance. On the other hand, they should also
allow for different perspectives on the process.

By considering the car as the case identifier, we examined the process using
general process mining techniques (e.g., process discovery, and conformance
checking) and process comparison approaches. However, in reality, the data
extracted from the information system of a car factory contains other case
identifiers, e.g., orders, and customers. Therefore, in our analysis, we need
to provide a more holistic vision of the process by considering multiple case
notions, as is discussed next.

##  6 Object-Centric Process Mining

Object-centric process mining is an emerging branch of process mining aiming
to apply process mining techniques on event logs which are closer to the data
extracted from information systems. Multiple objects such as customers,
orders, and deliveries are involved in the car factory, described in Section
3. Analyzing the processes covering all these aspects is addressed in object-
centric process mining.

Companies record their information in information systems such as the ERP
(Enterprise Resource Planning) systems of SAP, which do not have the structure
of traditional event logs. In traditional event logs, each event refers to a
single case notion (i.e., a process instance), an activity, a timestamp, and
any number of additional attributes (e.g., costs, resources, etc.). However,
in operational processes with many interactions (i.e., an event is related to
multiple objects), it may be problematic to create and analyze traditional
event logs with a single case notion [7]. Information systems that support
production, such as SAP, store information in the related tables of a
database. In the production planning module of SAP, multiple objects (e.g.,
planned order, supplier, product, component, and delivery) are involved. Each
planned order consists of many products, each comprising a range of components
(e.g., based on the BOM a car is composed of the frame, engine, battery,
etc.). Accordingly, we are able to study the process from multiple different
angles and dimensions. Extracted event logs from SAP systems usually suffer
from _convergence_ and _divergence_ [7]. These two problems are of high
importance to discuss, because they cause challenges in applying process
mining techniques, e.g., process discovery, on these event logs. To illustrate
these two problems, as an example, consider the simplified process of
production planning shown in Figure 16 with three case notions (i.e., planned
order, component, and product) and three activities (i.e., _place order_ ,
_confirm products_ , _check the inventory_), respectively. The mentioned
problems in this process are:

  * •

_Convergence_ : Events referring to multiple objects of the selected type are
replicated, possibly leading to unintentional duplication [7]. For example, an
order may contain many products and each product may contain different
components. Assume that there is an order comprising 10 products, e.g., cars.
In order to apply classical process mining techniques, we need to flatten the
event data by picking a case notion. If we select the product as a case
notion, then this leads to 10 replications of the same _place order_ event.
The duplication of events for different cases may lead to an explosion of the
number of events. Moreover, when time and costs are associated to events, this
may lead to very misleading insights In object-centric event logs, an event
may contain references to many objects of the same case notion, thus avoiding
the duplication problem.

  * •

_Divergence_ : Events referring to different objects of a type not selected as
the case notion are considered to be causally related but are executed
independently [7]. Assume that we pick order as a case notion (to avoid
convergence problems). A single order may contain multiple products and each
product may contain different components. However, there are activities
executed for a single product or a single component, e.g., _check inventory_.
Many _check inventory_ events may refer to the same order although they
actually independent and refer to different objects. Since we picked order as
a case notion, these events cannot be disentangled, typically leading to
Spaghetti models. Things that happen in a strict order for both components and
products become blurred when using a coarser case notion.

Figure 16: Overview of the relationship among case notions (i.e., _planned
order, product_ , and _component_) and activities (i.e., _place planned order_
, _confirm product_ , and _check inventory_). For example, there is one-to-one
relationship between _planned order_ and _place planned order_ and one-to-many
relationship between _planned order_ and _product_.

Object-centric process mining aims to provide a solution for the
aforementioned challenges. The interest in this subdiscipline is rapidly
increasing, because organizations are in need of a more holistic way to
interact with event logs extracted from information systems [15, 34, 28].
Several techniques were developed to deal with object-centric event logs for
process analysis:

  * •

Extracting object-centric event logs from information systems: This includes
several contributions related to the storage format and some work on the
extraction from SAP logs or ERP systems in general [15, 34, 48].

  * •

Discovering process models from object-centric event logs: Artifact-centric
modeling is an approach to model processes with multiple case notions by
combining process and data [22, 35]. The techniques proposed based on the
artifact-centric process modeling do not show the process as a whole.
Therefore, in [27] a discovery algorithm was proposed to discover Object-
Centric Behavioral Constraints (OCBC) models from object-centric event logs.
These models show interactions between the data and behavioral perspectives on
the attribute level in one diagram. The main challenge of OCBC is scalability
and complexity, which led to the development of MVP (Multiple Viewpoint)
Models. MVP models are Directly-Follows Graphs (DFG) with colored arcs
annotated by frequency and performance information. MVP models show the
process model without omitting any of the case notions [15]. MVP models cannot
capture concurrency well and the diagnostics may be misleading. This led to
the development of techniques to discovered colored Petri nets. Object-centric
Petri nets are another type of object-centric process models that can be
extracted from object-centric event logs and provide the execution semantics
[9].

Figure 17: Directly-Follows Multigraph for the case notions order, product,
component, and delivery. A fragment of the production line is captured that
consists of the stations _GA1_ , _GA2_ , and _GA3_.

A baseline discovery model for object-centric processes is the Directly-
Follows Multigraph, inspired by DFGs [7]. DFGs are graphs where the nodes
represent activities or events in the event log. Nodes are connected through
edges if there is at least one case in the event log where the source event or
activity is followed by the target event or activity [5]. For generating
Directly-Follows Multigraph, we merge the DFGs that are generated for each
case notion into an overall DFG where the arcs with different colors
correspond to different case notions. In Figure 17, a Directly-Follows
Multigraph for the extended model of the car factory with multiple case
notions (e.g., planned order, products, component, and delivery), is shown.
Following the green arcs, the order goes through _place planned order_ ,
_check inventory_ , _confirm products_ , and _pay order_. The product and
component enter the production line which consists of the stations _GA0_ ,
_GA1_ , and _GA2_. The component leaves the production line at the last
station (_GA2_), while the last step for any product is _complete delivery_.

Using object-centric process models such as Directly-Follows Multigraph shown
in Figure 17, we gain insights into object-centric processes. However,
techniques developed for object-centric process mining can still focus on
single location, time period, or process variant. There may exist multiple
variants of the same process, which motivates the use of process comparison
techniques such as process cubes, supporting the analysis of object-centric
event logs. To adapt the concept of process cube operations to object-centric
process mining is not trivial since an event may refer to any number of
objects. _Slicing_ for dimensions related to case notions may lead to
convergence and divergence problems. For example, events related to a specific
component also contain other components. _Dicing_ suffers from the same
problem. Therefore, traditional process cubes cannot handle object-centric
event logs and it is worthwhile to bridge the gap between process comparison
approaches and object-centric process mining. This needed to compare the
processes in a more holistic setting.

##  7 Forward-Looking Process Mining

The value of process mining in analyzing the past is clear and widely
accepted. However, just diagnosing the past is not a goal in itself. The
actual goal is to continuously improve processes and respond to changes.
Operational processes are subject to many changes, e.g., a sudden increase in
the number of orders, and therefore the managers require an extended vision of
the future in order to deal with changes in the process. Due to the high cost
of operational processes, in the face of deliberate changes in order to
improve the operational processes performance or unexpected changes, having
the ability to look forward is of paramount importance. Simulation is capable
of enabling process mining to look forward. At the same time, data-driven
support provided by process mining, e.g., past executions of the process and
process model, can make the simulation models more realistic. In order to
answer forward-looking questions regarding the future of processes using
process mining, the first step is to demonstrate how process mining and
simulation can complement each other. The knowledge gained about the process
(including the discovered process model and the current performance of the
process) can be used to predict future states of the running cases (e.g., the
remaining flow time) under the assumption of a stable process, i.e., no
changes in the process. However, process mining is backward looking and cannot
be used for “What if?” questions. Hence, process mining and simulation
perfectly complement each other [6]. Among the different simulation
techniques, we consider two different approaches: discrete event simulation or
system dynamics simulation. These techniques are both able to model the
operational processes at different levels of detail by capturing process
events and variables.

###  7.1 Discrete Event Simulation

One of the well-known simulation techniques which can be used for simulating
operational processes is Discrete Event Simulation (DES). DES uses predefined
rules according to which the simulation process generates events. Whenever an
event occurs, the state of the system changes and the new state is recorded.
Each state enables new events. Each possible way is described by a simulation
run which shows the result of the play-out of the model [6]. Tools such as
Coloured Petri Nets (CPNs) can be used for simulating a process including all
the details of a process, e.g., number of resources for each activity and
duration of each activity [43]. Predefined key performance indicators such as
average waiting time for each process instance can be calculated and compared
in different situations by redesigning the simulation models and experiments
[6]. Despite the capable simulation tools along with the provided forward-
looking approaches, the real-life application of simulation is limited [4].
Detailed simulation models like DES may be very time-consuming to build.
Interpreting simulation results is not an easy task and often models need to
be tuned to behave similar to the real process (coffee breaks, visits to the
toilet, holidays, illness, etc. lead to lower performance than expected based
on the initial simulation model). Furthermore, simulation models tend to
capture only some aspects of a process or use an oversimplified model of
reality [1]. Therefore, organizations try to use the evidence-based approaches
in which the previously captured state of the organization can be used for
more accurate simulation models [6]. In order to achieve more realistic
results using simulation techniques, different approaches have been proposed
[45, 46].

Discrete Event Simulation (DES) requires considerable domain knowledge of the
process. For instance, to answer the forward-looking question for our example
production line, we need to implement all the details of the production line
to see the effect of inventory options for one of the stations on the overall
production line. Also, it is not easy to consider the context in which the
simulation is running [10]. Incorporating effects of external factors in the
models, e.g., human behaviors or environmental variables are other aspects
that are missing from current simulation techniques in processes [39].
Moreover, selecting the right level of abstraction and avoiding too much
detail are other important aspects of creating a simulation model and
performing simulation. Unfortunately, the inherently required level of detail
in these types of approaches does not allow for high-level modeling and long-
term predictions. As opposed to the existing simulation techniques, System
Dynamics (SD) allows us to assess the impact of changes in the process from a
global perspective as well as the effects of external factors. Using different
levels of granularity in the modeling, we can address the major drawbacks of
discrete event simulation techniques. Therefore, the goal is to assess and
predict the future behavior of operational processes including the effects of
potential changes as well as the roles of external factors, e.g., human
behavior at an aggregated level.

###  7.2 System Dynamics

System Dynamics (SD) techniques are used to model dynamic systems and their
relations with their environment. Using system dynamics, the factors affecting
a system’s behavior are captured [50]. Stocks, flows and variables are
fundamental elements in modeling a system as a stock-flow diagram, i.e., one
of the main system dynamics diagrams.

Figure 18: A sample stock-flow diagram for the running example. The value of
the stock _number of cars in the production line_ is calculated from the value
of _arrival rate_ and _production rate_ as flows per time step, e.g., per day.
Also, the value of the _production rate_ is affected by the value of _average
production duration_.

Figure 18 shows a simple stock-flow diagram for the running example in which
_arrival rate_ and _production rate_ as flows add/remove to/from the values of
_number of cars in the production line_ as stock, also, _average production
duration_ (a variable) affects _production rate_. System dynamics provide the
opportunity to add exogenous factors, e.g., variables in stock-flow diagrams
as well to capture the hidden relationships among the players in the
operational processes [39]. As shown in Figure 1, different executions of
operational processes that are stored in the information systems can be
captured in the form of event logs. The provided event logs for operational
processes can be transformed in other formats to be used as inputs for
simulation techniques. These transformed event logs include the performance
metrics of the processes over different time steps, e.g., arrival rate,
average service time and the number of waiting items over steps of time in the
process [41].

Figure 19: A general framework for using process mining and simulation for
improving operational processes. Using event logs and transforming them to
System Dynamics logs (SD-logs) along investigating the relations between
different parameters in the process over time, the simulation regarding
answering “What if?” questions is possible.

The authors in [42] exploit the general proposed approach in [39], i.e., a
simulation approach for the business process using system dynamics and process
mining techniques, which is designed for the specific purpose of simulating
the production lines w.r.t performance metrics. Therefore, as shown in Figure
19, for operational processes, the simulation model in the form of a system
dynamics model using trained variables based on event logs can be generated.
We use system dynamics and process mining together to answer “What if?”
questions, e.g., what will happen if we add one more resource to one of the
stations? We transform the generated event log into a _System Dynamics log_
(SD-Log) which describes the values of a collection of variables of the
process over time using the time window selection technique in [40]. Then, we
use the calculated values of the variables to detect the relationships between
metrics and generating a stock-flow diagram which is used for simulation [41].

Figure 20: A sample stock-flow diagram generated based on the production line
event log. The purpose of the model is to simulate the effect of adding an
inventory option to the doors sub-assembly on the final production rate of the
production line at an aggregated level, e.g., per month.  Figure 21: A
comparison between the actual production rate (PR) and the simulated PR using
our approach along actually changed production rate (CPR) and simulated CPR
for adding an inventory to the doors sub-assembly, e.g., in August 3 more cars
will be produced.

###  7.3 Forward-Looking in Production Lines

As indicated in Section 3, the stations in the base model do not have an
inventory or queuing option. As a simplified example, consider the station
_GA24_ , at which the cars at this station require the doors prepared in _SA7_
, i.e., a door preparation sub-assembly station. Therefore, in the production
line, there can be situations in which a car is at the station _GA24_ and
cannot leave the station because it is waiting for the doors to be prepared.
One of the scenarios to improve the above-mentioned situation is to prepare
the doors for each car beforehand and keep them at the sub-assembly station.
The possibility of having a temporary queue, especially for sub-assembly
stations, is one of the possible actions which potentially leads to an
improved production rate of the whole production line. The question to be
answered is: How does the temporary queue for the door assembly stations
affect the overall production line? Using the approach presented in [38], we
can measure the effect of adding an inventory option to only one of the sub-
assembly stations at the production rate of the whole production line. We do
not need to consider the details required by DES approaches.

We also populate the generated simulation model, i.e., the stock-flow diagram,
with the values generated from the event log of the current status of the
production line. We use the data generated in a regular situation in which
there is no inventory option for the sub-assembly station, and after
transforming it to the system dynamic log, we generate the model using the
proposed approach in Figure 19. The result of simulation after adding the
inventory option is shown in Figure 21. As expected, the whole production rate
has increased and the production line manager can decide whether to invest in
adding the possibility to keep inventory for the door preparation sub-assembly
station. The described scenario shows the potential of using a combination of
process mining and SD-based simulation in order to improve operational
processes. Using process mining and simulation techniques we are able to
answer “What if?” questions in the operational processes.

##  8 Conclusion

In this paper, we discussed how process mining can be used to remove
_operational friction_ (e.g., rework, delays, waste, and deviations). We
discussed the capabilities provided by existing tools and highlighted some of
the main challenges addressed in the DFG-funded Cluster of Excellence
“Internet of Production” (IoP). Process mining is widely used to improve
standard processes like the Order-to-Cash (O2C) and the Purchase-to-Pay (P2P).
However, in other areas (e.g., in production, materials handling, and
logistics) process mining is not yet widely used. We believe that by
addressing the challenges discussed in this paper, adoption can be
accelerated.

We highlighted three important research directions: comparative process
mining, object-centric process mining, and forward-looking process mining.

_Comparative process mining_ aims to visualize and show root causes for
differences over time or between different organizational units (e.g., two
factories producing the same or similar products). It is valuable to identify
differences and generate insights. These may help to detect problems early
(e.g., using concept-drift detection) and establish so-called “best practices”
(i.e., routines that are considered to be correct or most effective).

_Object-centric process mining_ addresses one of the main limitations of
today’s process mining tools. In many applications it is not reasonable to
assume a single case notion. Consider for example the Bill of Materials (BOM)
in an assembly process. A single car has about 2000 parts (or even up to
50,000 parts, counting every part down to the smallest screws). When an engine
is produced it may not be clear in what type of car it will end up. Both the
car and the engine have unique identifiers are are related somewhere in the
process. This example shows that assuming a single case notion automatically
leads to a particular view on the process. Such views are valuable, but at the
same time incomplete and potentially misleading. Object-centric process mining
can be used to provide a more holistic view, also avoiding the convergence and
divergence problems described.

_Forward-looking process mining_ techniques aim to not only diagnose
performance and compliance problems in the past, but to predict and change
processes. We are particularly interested in answering “What if?” questions.
Therefore, simulation plays an important role. Next to traditional Discrete
Event Simulation (DES), we also proposed the use of System Dynamics (SD). SD
tools simulate the process at a higher abstraction level (i.e., the steps are
days or weeks rather than individual events). To learn SD models we need to
convert traditional event logs to SD-Logs. This is far from trivial, but
allows extending the model with contextual factors that are not directly
related to individual events.

We strongly believe that progress in comparative process mining, object-
centric process mining, and forward-looking process mining will assist in the
creation of realistic _digital shadows_ that can be used to manage, control,
and improve operational processes. Digital shadows based on process mining
include both process models and actual event data. The combination of process
models and actual event data allows us to drill-down into the actual process
when a problem emerges, predict the trajectory of current process instances,
and anticipate the effects of interventions (e.g., adding resources or
reordering production steps).

#### Acknowledgments

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation) under Germany’s Excellence Strategy–EXC-2023 Internet of
Production – 390621612. We also thank the Alexander von Humboldt (AvH)
Stiftung for supporting our research.

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