=Paper= {{Paper |id=None |storemode=property |title=Professional Use of Process Mining for Analyzing Business Processes |pdfUrl=https://ceur-ws.org/Vol-1052/paper9.pdf |volume=Vol-1052 |dblpUrl=https://dblp.org/rec/conf/bpm/Martens13 }} ==Professional Use of Process Mining for Analyzing Business Processes== https://ceur-ws.org/Vol-1052/paper9.pdf

Professional use of Process Mining for analyzing Business
Processes

Josef K.J. Martens

Capgemini, Cluster TRAILS, Reykjavikplein 1,
3543 KA Utrecht, The Netherlands
jef.martens@capgemini.com
j.k.j.martens@alumnus.utwente.nl

Abstract: A professional application of Process Mining has been established in
the context of a methodology as defined by a consultancy firm. The results of
the research show where in the context of consultancy Process Mining is used
and how clients can benefit from expertise and standardized work.

Keywords: Process Mining, Consultancy, Business Analysis, SEMBA,
Business Process Management.

1 Introduction

1.1 Reason for research
Luftman et al. analyze IT management issues and multiple topics have been
identified and known to shift in importance over time [1][2][3][4][5]. Results by
Luftman and Ben-Zvi [1] shows that the topic of Business productivity and cost
reduction is the most important issue for C-level management in 2010. From the top 5
topics from 2010, three issues (Business productivity and cost reduction, IT and
Business alignment, IT reliability and efficiency) are presented in some form in the
BPI challenge 2013 [6]. Process Mining allows for analysis of raw data sets to
discover process flows and analyze the important elements related to such flows [7].

1.1.1 Business productivity and cost reduction
Business productivity is measured by Key Performance Indicators (KPI) which are
common in mature businesses, aligned to strategic and tactical goals and drive the
decision making processes [8]. Common KPIs are constructed by evaluating data
against a benchmark value about input, output and throughput of business processes
and their related waste and outage.
Having an excellent productivity performance with maximized effectiveness of
expenses on the operation of assets and employees allow for margin translates to a
maximization of profit in the case of for profit organizations. To achieve this
2 Josef K.J. Martens

optimum state, it is essential to know what the operational performance is of the
complete- and sub-system of the organizational processes.

1.1.2 IT and Business Alignment
Bridging the gap between Business and IT is one of the most challenging activities
for IT and Business professionals, it has been “…a top concern of IT managers for
almost 30 years” [1]. Blum et al. [9] researched the position of the information
manager, a role which is concerned with many of the issues as described by Luftman
et al. and concluded that the organizational maturity defines the position and
importance acceptance of higher management to solve such issues.
The Business Analyst (BA) is the role that inhibits a set of competences that allow
BA professionals to close the gap [10]. Therefore, Business Analysts of Capgemini
present you these research results for the BPI Challenge 2013.

1.1.3 IT reliability and efficiency
The case for 2013 in the BPI Challenge is based on information of an IT system
which is part of the IT department of Volvo [11] responsible for problem and incident
management in combination with a call center. Call centers are the de facto standard
for efficiency studies [12] and their performance is highly reliant on supporting IT
systems for providing applicable knowledge. Because the Volvo call center as subject
of analysis is the problem solving unit for incidents, reliability and efficiency are
applicable topics of research for this analysis.

1.2 Aim
There are multiple aims for this BPIC ’13 research:
1. To position Process Mining in the collection of competences of Business
Analysts in relation to Business Process Management
2. To position the research method characteristics in the context of Business
Analysis
3. To provide proof that Process Mining is beneficial in a methodological approach
of analysis in context of Business and IT gaps and the SEMBA method
4. To provide insight in how Business Analysis is applied and what next steps are
with Process Mining outcomes.
The aims as presented are positioned by answering the questions as stated by Volvo
[11], where we assume that the author is the requestor.

1.3 Added value

There are two sides for the added value of this paper. For professionals, this paper
shows how Process Mining and facilitating tools can be applied and how the rationale
is defined when handling complex customer cases. For Science, this paper allows to
Professional use of Process Mining for analyzing Business Processes 3

relate the insight of the requirements of consulting professionals for non-standard
expertise and how Process Mining is validated as an important method for Business
Analysts and their profession.

1.3.1 Professionals – positioning in practice
The consulting profession is a field of expertise that is highly reliant on academic
work and evidence driven. Customers grow in their insights and requirements and
demand factual decision making solutions, from one-off decisions to continuous
business management tooling.
Capgemini established the Structured Expert Method for Business Analysis
(SEMBA) as method for Business and IT analysis. SEMBA consists of four phases
(Focus & Direction, As-Is Analysis, To-Be Design and Migration Design) and
multiple streams (Business Context, Business Process, Information, Application
Landscape and Requirements Engineering), depicted in figure 1. SEMBA is
established with predefined deliverables, which allow for a consistent, predictable
outcome of complex analysis. The method is a standard, however, the content and
interpretations are customer tailored. The combination of evidence driven tools with a
standardized methodology of analysis resulting in predictable delivery is developed
for client satisfaction [13].
4 Josef K.J. Martens

Fig. 1. The Structured Expert Method for Business Analysis (SEMBA)

1.3.2 Science – positioning in literature
The BPI Challenge is a great way to present and combine science with application
in practical settings. Where the BPI Challenge is a challenge for academics and
professionals, this paper is a presentation of analysis and professional positioning for
anyone interested in the field of process mining and business process management. As
Business Analysts bridge the gap between Business and IT, this paper bridges the gap
between science and business by applying research findings.

2 Research Design

2.1 The case positioned in SEMBA
For this analysis and the deliverable (this paper) the limitations are based on time
restrictions and client contact. The time restriction is 18 hours and there cannot be
client interaction because of the design of the BPI challenge.

2.1.1 Focus and Direction
In the focus and direction phase, there are seven steps followed. As SEMBA is a
Capgemini proprietary approach to analysis, not all details are presented in this case.
The common result is that the problem is defined as the combination of questions as
stated in the Volvo case description in the context of the Volvo IT department related
to incident and problem management with the use of a call center in multiple
Professional use of Process Mining for analyzing Business Processes 5

countries. The client expects answers to the stated questions with the use of the
provided input: datasets and descriptions of the dataset and the system where the
dataset is obtained from.
The approach is described from paragraph 2.2, normally this phase describes the
approach and scope of the activities. The outcome of this phase is a formal and
exhaustive overview of what is to be done, who does it, how activities are done, when
activities take place, where and why. The scope for this research has no objective to
capture requirements, therefore the stream Requirements Engineering will be left out
of this paper.

2.1.2 As-Is Analysis
For the BPI Challenge 2013, the As-Is situation is established for some of the
streams.

Business Context
The business context is related to the IT department of Volvo. The unit of analysis
is the functioning of the VINST system, its users and the registrations in the system
across a limited timeframe. The VINST system is used globally by multiple support
organizations.
Due to limitations in report size this item is kept condensed and refer to the VINST
context description [11] and VINST user guide [XX] for more detail.

Business Processes
The business processes for analysis are related purely to the registration of
activities within the VINST system. There are no satellite systems or procedures in
scope. The higher hierarchy process could be captured under “Incident and Problem
Management”. The classes of activities can be defined as Incident solving and
Problem solving activities. Activities can be handled by first, second and third line
support employees. Support employees have each a specific area of expertise related
to technology.

Information
Information is stored and transformed in the VINST System. Information is related
to the employees working for Volvo on a global scale, their position in the
organizational hierarchy, their expertise, products and geographical position.
Furthermore, information is assumed to be present in the VINST system which
enables knowledge transfer for storing, retrieving and adding solutions to problems
related to products and services.
6 Josef K.J. Martens

Application Landscape
There is no formal description available other than the VINST system. No
peripheral system, interface or other element is mentioned in the context other than an
e-mail facility.

2.1.3 To-Be Design
The phase To-Be design is not applicable for this research. For applicable cases,
the outcomes and decisions of the As-Is phase are used to create at least one To-Be
design. The design elements can be prescreened to design only one most feasible
solution, or multiple scenarios are considered. In case of multiple scenario’s, the
individual scenarios are scored using multiple optional activities such as Multi-
Criteria Decision Analysis (MCDA) techniques covering related aspects or
Simulation for business performance, for example. The outcome is a so called Gap
Analysis which covers the difference between the As-Is situation and the To-Be
design(s). The Gap Analysis covers each of the aforementioned streams: Business
context, Business Processes, Information and Application Landscape.
Creation of To-Be designs can be accelerated by usage of reference models such as
the Supply Chain Operations Reference (SCOR®) [17], Process Classification
Framework by APQC [18], Frameworx [19] and the Banking Industry Architecture
Network (BIAN) [20].

2.1.4 Migration Design
The phase “Migration Design” is not applicable for this research. For applicable
cases, the outcome of the To-Be design phase is used to review the methods of how
the As-Is situation can be migrated to the To-Be design. Common scenarios are i.e.
Big-Bang, Pilot location, Linear migration and Exponential migration amongst others.

2.2 Research method
As discussed in chapter 2, the basic steps for Process Mining are followed as
described by van der Aalst et al., to cover the exploratory element of this research.
Then professional insights on what to analyze or ask the problem owner in a next
activity to proceed towards a To-Be phase or suggestions for improvement.

2.2.1 Research design
Because of the characteristics of Process Mining mainly consisting of exploratory
research, the limited interaction for research by the researchers, the data type being
Quantitative and a setting which resembles a Laboratory, the research method is
determined as Non-reactive research, as presented in table 1.
Professional use of Process Mining for analyzing Business Processes 7

Method Setting Data Type Researcher Role
Action Research Field Qualitative Active
Case Study Field Qualitative Passive
Experiment Laboratory Quantitative Active
Non-reactive Laboratory Quantitative Passive
Survey Field Quantitative Passive

Table 1. Non-reactive research selected as research method based on multiple criteria

2.2.2 Exploratory research
Process mining for processes is mainly exploratory research [7]. First, the researcher
needs to get a feeling for what the data represents. Second, assumptions and
statements about the dataset need to be stated to test which part of the data is relevant
for the desired answer. The two aforementioned elements are attained iteratively by
doing small experiments and testing.
For the research on the stated (sub)question, three basic topics will be stated:
Research scope, Filters used and the results of the research with optional elaboration
for each of the topics.

Research scope
The research scope limits the unit of analysis to least possible number of attributes
to consider with the relevant subset of the data. The research scope is limited through
some elements: the dataset, the assumption, the method and a threshold. The dataset
element shows which dataset is used. The assumption is the description of which
assumption(s) would lead to the right subset of the data. The method element
describes how the assumption is translated into the subset. The threshold is set for
limiting the results as presented for this research.

Filters used
The filters used give a description on how the tool was set and which settings were set
to obtain the subset results.

Results
The results show in either figure or table form the results using the aforementioned
limitations, settings and scope.

2.2.3 Explanatory research
The explanatory element in the research is highly limited, due to no client
interaction, no strategic and tactical information about the company and no baseline
information about performance or access to operational teams and systems.
Possible explanations will be provided as suggested research topics based on
previous commercial engagements of the researcher. These explanatory contents are
8 Josef K.J. Martens

not tested to be applicable in the Volvo situation and should be tested with a proper
hypothesis which is refined by client interaction.

2.3 Data usage
Provided data sets Incidents [14], Open Problems [15] and Closed Problems [16].
Sets are provided in the XES data format, however for this research the prepared
combined dataset by Fluxicon is used for the Disco tool [22]. Because a non-primary
source is used for the data, a comparison has been run between exports of the
Fluxicon dataset and the provided XES dataset. There have not been found any
inconsistencies.

2.3.1 Assumptions
On the topic of data, there are many possible issues resulting in an incomplete or
sometimes unusable dataset. Because of the nature of the BPI Challenge and the
available prepared data, the assumption is that the dataset is fit for research purposes.

2.4 Tooling

In this paragraph multiple toolsets for analysis of the process mining category are
discussed. Four software candidates are discussed about features and applicability for
use in this research case.

2.4.1 ProM 5
ProM [23] is the acronym which stands for Process Mining. The tool is open
source and mainly aimed at researchers and scientific application. It is a collection of
custom written plugins for various insights that can be obtained from datasets.
Version 5 is the last version that has a certain interface which is more complex but
powerful for the experienced user.

2.4.2 ProM 6
ProM 6 [23] is a continuation of the ProM application which has been overhauled
on the UI and activity design so analysis is more straightforward and entry-user
friendly. The package is a platform which can be upgraded with multiple plug-ins for
several types of analysis depending on the requirements of the user.

2.4.3 Fluxicon Disco
Fluxicon is the company which creates the commercial tool Disco for process
mining analysis of datasets [22]. Disco is capable of delivering quick analysis results
on desktop computers and is optimized for the areas of process discovery and a set of
Professional use of Process Mining for analyzing Business Processes 9

statistical overviews. It has multiple options to filter data into subsets and quickly trim
sets for specific analysis.

2.4.4 Perceptive Process Mining
Perceptive is the company which creates the product Perceptive Process Mining
(PPM) for analysis of datasets [24]. PPM is capable to analyze datasets in both social
network and process flow methods, using cloud technology to provide performance
beyond desktop computers. The tool is powerful and feature rich but requires more
experienced researchers to use the tool to its maximum effectiveness.

2.4.5 Tool selection
The tool(s) will be selected based on the availability of the tool, the user
friendliness and timely analysis results whilst working with the tool.
Based on the access to the tool, Perceptive Process Mining is not used, it would
require a license or accredited access to the tool for analysis. Due to time restrictions
the researcher did not contact Perceptive to consider this opportunity.
Based on previous experiences with ProM 5 and 6, the applications are not used for
this research.
The research tool for this paper is Disco by Fluxicon, the demo product with the
prepared dataset made available.

2.5 Process discovery methodology

2.5.1 Social Network Analysis

Social network analysis is a representation of the dataset which uses the people or
departments as the unit of analysis instead of the events. This allows for another
dimension of outlier and deviant activity analysis.

2.5.2 Process Network Analysis after Process Discovery
Process Network analysis is the analysis of sequential events that form some sort
of network based on the number of similar cases and flows of events. The flow of
events is constructed using Process Discovery, in this research based the fuzzy mining
technique. Some tools allow for automated generated models to be derived from
datasets for further use. There is a limitation on the discovered processes in such
forms, as events are the result of a process, not the process itself.

2.5.3 Methodology selection
Due to the restricted timeframe as discussed in paragraph 2.1 and the dismissal of
ProM, Social Network analysis will not be applied for this research. Process Network
10 Josef K.J. Martens

analysis will be applied with the notion that the discovered processes might not be the
processes but the sequences of end-states per process step.

3 Results

3.1 Questions

In this paragraph, the questions are answered in the described methodology from
chapter 2.

3.1.1 Q1.1 Push to front: For what Products is the push to front mechanism
most used and where not?

Research scope: For what Products Push to Front is used
Element Description
Dataset Incidents
Assumption Events have a specific sequence and the scope is limited to these
events.
Method Analyze the distribution of products
Threshold All results with a relative percentage of <1% of the cases is not
represented

Filters used on attributes of the dataset (in sequence)
Filter name Filter by: Event values:
Attribute Org:group NOT ({A..Z}{1..99} 2nd OR
{A..Z}{1..99} 3rd )
Endpoints Activity – Mode discard cases Start event values:
All
End event values:
Completed / In Call
The resulting set has N=1854 cases

Results
Product Relative Absolute Cumulative
Frequency Frequency* Percentage**
PROD424 14,57% 270 14,57%
PROD660 11,00% 204 25,57%
PROD566 5,61% 104 31,18%
PROD494 5,16% 96 36,34%
PROD13 3,84% 71 40,18%
Professional use of Process Mining for analyzing Business Processes 11

PROD453 3,16% 59 43,34%
PROD321 2,76% 51 46,10%
PROD544 2,06% 38 48,16%
PROD832 1,96% 36 50,12%
PROD253 1,80% 33 51,92%
PROD369 1,46% 27 53,38%
PROD104 1,39% 26 54,77%
PROD434 1,37% 25 56,14%
PROD363 1,27% 24 57,41%
PROD328 1,24% 23 58,65%
PROD423 1,20% 22 59,85%
PROD815 1,17% 22 61,02%
PROD698 1,00% 19 62,02%
* Absolute frequency is calculated using the N=X number and multiplied by the
relative frequency. The resulting set (with disregard of the set threshold) can add up to
another number than N=X.
** Cumulative percentage is obtained by adding up the various (rounded) results
from the relative frequency. The resulting set (with disregard of the set threshold) can
add up to another number than 100%.

Research scope: For what Products Push to Front is NOT used
The standard assumption would be that you would select the product list from the
previous sub-question and would subtract it from the total list of products. However,
when doing that, you will never discover whether there are duality issues with the
same product(s). Therefore, the following research approach is followed:
Element Description
Dataset Incidents
Assumption Events have a specific sequence and the scope is limited to these
events.
Method Analyze the distribution of products
Threshold All results with a relative percentage of <1% of N cases is not
represented

Filters used on attributes of the dataset (in sequence)
Filter name Filter by: Event values:
Attribute Org:group {A..Z}{1..99} 2nd AND {A..Z}{1..99} 3rd
Endpoints Activity – Start event values:
Mode discard All
cases End event values:
Closed / {Cancelled, Closed, Resolved}
The resulting set has N=2198 cases
12 Josef K.J. Martens

Results
Product Relative Absolute Cumulative
Frequency Frequency* Percentage**
PROD424 5,56% 122 5,56%
PROD542 4,86% 107 10,42%
PROD698 3,24% 71 13,66%
PROD607 3,00% 66 16,66%
PROD802 2,73% 60 19,39%
PROD805 2,14% 47 21,53%
PROD660 1,95% 43 23,48%
PROD604 1,84% 40 25,32%
PROD617 1,62% 36 26,94%
PROD243 1,37% 30 28,31%
PROD253 1,33% 29 29,64%
PROD544 1,23% 27 30,87%
PROD325 1,19% 26 32,06%
PROD631 1,18% 26 33,24%
PROD267 1,08% 24 34,32%
PROD337 1,06% 23 35,38%
* Absolute frequency is calculated using the N=X number and multiplied by the
relative frequency. The resulting set (with disregard of the set threshold) can add up to
another number than N=X.
** Cumulative percentage is obtained by adding up the various (rounded) results
from the relative frequency. The resulting set (with disregard of the set threshold) can
add up to another number than 100%.

Q1.2 Where in the organization is the push to front process most implemented?

Research scope: differences between organizations A2 and C
Element Description
Dataset Incidents
Assumption Events have a specific sequence and the scope is limited to these
events.
Method Compare input and output by case numbers.

Filters used on attributes of the dataset (in sequence)
Filter Filter by: Event values:
name
Professional use of Process Mining for analyzing Business Processes 13

Attribute Organization Org line A2 [set 1]
involved Org line C [set 2]
Endpoints Activity – Mode Start event values:
discard cases Accepted / In Progress OR Queued / Awaiting
Assignment
End event values:
Completed / Closed OR Completed / In Call

Results
The most important organization elements are C and A2, as stated in the VINST
data set description are confirmed by the data set with a distribution by organization
of 67% of the cases handled by organization C and 17% of the cases handled by
organization A2. The resulting breakdown of how many cases are receiving the status
“Completed / In Call” show a difference (not calculated for significance) of 35,5%
cases solved by organization C and 2,4% by organization A2.

Organization A2 (17%) C (67%)
[set 1] [set 2]
Input Cases (N=) % Cases (N=) %
Accepted / In Progress N=595 46,20% N=4445 87,80%
Queued / Awaiting N=694 63,80% N=619 12,20%
Assignment
Total N= 1289 100,00% N=5064 100,00%
Output Cases (N=) % Cases (N=) %
Completed / Closed N=1258 97,60% N=3264 64,50%
Completed / In Call N= 31 2,40% N= 1800 35,50%
Total N=1289 100,00% N=5064 100,00%

3.2 Q1.3 What functions are most in line with the push to front process?

Research scope: the functions which solve first line support calls
Element Description
Dataset Incidents
Assumptions All support desks are investigated (not only A2 and C)
Status Completed / In Call is the correct end state
Method Research on attribute org:role
Threshold All results with a relative percentage of <1% of the cases is not
represented
14 Josef K.J. Martens

Filters used on attributes of the dataset (in sequence)
Filter name Filter by: Event values:
Attribute Organization involved All
Endpoints Activity – Mode discard Start event values:
cases All
End event values:
Completed / In Call
The resulting set has N=1882 cases

Results

Organizational Role Percentage Absolute Frequency*
(Function)
V3_2 91,78% 1727
A2_1 6,24% 117
(unknown) 1,67% 31
A2_4 0,10% 2
E_6 0,10% 2
A2_2 0,10% 2
* Absolute frequency is calculated using the N=X number and multiplied by the
relative frequency. The resulting set (with disregard of the set threshold) can add up to
another number than N=X.

3.3 Q2: Ping pong behavior

2.1: What are the …

Research scope: responsibles for ping-pong behavior in incidents?
Element Description
Dataset Incidents
Assumptions Cases with >8 events are ping-pong cases (based on sampling
ping pong case flows
1-627819166 / 1-621825480 / 1-650013051)
Cases cannot have the end-state Completed / In-Call
Method Research on attributes functions / organizations / support teams /
products
Threshold All results with a relative percentage of <4% of the cases is not
represented
Professional use of Process Mining for analyzing Business Processes 15

Filters used on attributes of the dataset (in sequence)
Filter name Filter by: Event values:
Performance Number of Minimum number of events = 9
events Maximum number of events = 124
Endpoints Activity – Mode Start event values:
discard cases All
End event values:
NOT Completed / In Call

Results: functions
The resulting set has N=2601 cases.
Organizational Role Percentage Absolute
(Function) Frequency
V3_2 43,01% 1119
A2_1 18,35% 477
(unknown) 10,61% 276
E_10 9,47% 246
A2_2 4,67% 121

Results: organizations
Organization involved Percentage Absolute Frequency
Org line C 61,46% 1599
Org line A2 20,57% 535
Org line B 8,18% 213

Results: support teams
Support teams Percentage Absolute Frequency
G97 15,96% 415
G96 5,82% 151

Results: products
Product Percentage Absolute Frequency
16 Josef K.J. Martens

PROD424 15,36% 400
PROD660 4,23% 110
PROD542 4,01% 104

Research: responsibles for ping-pong behavior in closed problems?
Element Description
Dataset Closed Problems
Assumptions Cases with >8 events are ping-pong cases (based on sampling
ping pong case flows
1-736351127 / 1-653989471 / 1-563477371)
Mean time is >=23d (1st block is <23 days and averaging 7d
which is non-exceptional)
Method Research on attributes functions / organizations / support teams
/ products
Threshold All results with a relative percentage of <4% of the cases is not
represented

Filters used on attributes of the dataset (in sequence)
Filter name Filter by: Event values:
Performance Number of eventsMinimum number of events = 9
Maximum number of events = 36
Performance Case duration Minimum duration = 23 d
Maximum duration = 6 years, 87 days
The resulting dataset has N=114 cases

Results: functions
Organizational Role Percentage Absolute Frequency
(Function)
(unknown) 20,48% 23
A2_2 14,07% 16
E_10 13,09% 15
C_6 12,10% 14
E_8 6,90% 8
Applying another filter to the dataset of N=114 cases where the Function
(unknown) has been selected results in a subset of N=43 cases.
Professional use of Process Mining for analyzing Business Processes 17

The characteristics of these cases are (with the <4% threshold still applied)
Group Org. Country Org. Involved Product
Value % Value % Value % Value %
Org line G3 79,73 Us 77,66 G199 3rd 79,73 PROD97 37,80

Org line G4 14,78 Se 19,59 G51 2nd 5,50 PROD98 27,84

S30 2nd 4,81 PROD96 14,09

Results: organizations
Organization involved Percentage Absolute Frequency
Org line C 49,19% 56
Org line A2 25,26% 29
Org line G3 16,33% 19
Org line B 5,21% 6

Results: support teams
Support team Percentage Absolute Frequency
G199 3rd 16,33% 19
G21 2nd 7,18% 9

Results: products
Products Percentage Absolute Frequency
PROD97 11,26% 13
PROD98 8,02% 9
PROD802 5,91% 7
PROD96 4,15% 5

Open problems are not reviewed due to their incomplete state. However, the
dataset can be handled in the same manner as presented in the previous two
exhibitions to obtain the results.
18 Josef K.J. Martens

3.4 Q3 Wait User

3.4.1 Q3.1: Who is making most use of the state Wait / User?

Research: Most use of state Wait / User
Element Description
Dataset Incidents
Assumptions A subsequence is present which is Accepted / In Progress followed
in time by Accepted / Wait User
Method Research on attributes impact / support teams / products
Threshold All results with a relative percentage of <4% of the cases is not
represented

Results: Who is making most use of this status?
The answer from the selection and breakdown to the resource that uses this
function the most provided only because we assume that the name is related to a
system name: Siebel. This is furthermore assumed as the status changes produced by
this user are mainly at a specific time (01:19 – 01:22) each day, which is assumed to
be an automated script.
Because of limitations of sharing personal details based on legal protection of
employees and their performance assessment [21] we will not publicize the names of
individual employees making use of the Accepted / Wait User status.

Results: Impact
Impact Major High Medium Low
Cases 0 83 1216 1186
Professional use of Process Mining for analyzing Business Processes 19

3.4.2 Q3.2: What is the behavior per
A: Support team
B: Function
C: Organization
No results have been produced because this part is best researched using Social
Network analysis. As described in paragraph 2.5.3, Social Network analysis is not
included in this research.

3.4.3 Q3.3: Is there overuse of the Wait / User state by location?

Research: overuse of the Wait / User state
Element Description
Dataset Incidents
Assumptions A subsequence is present which is Accepted / In Progress followed
in time by Accepted / Wait User
Method Compare the wait / user resulting country breakdown to the general
country breakdown of cases

Filters used on attributes of the dataset (in sequence)
Filter name Filter by: Event values:
Follower Activity Reference event value: Accepted / In Progress
Reference event Follower event values: Accepted / Wait User
must be eventually
followed by
The resulting dataset for Wait / User has N=2485 cases, the total dataset has
N=7554 cases
The results for the breakdown of the Wait / User state by location is presented in
table x.

Country Wait / User Total Distance If > 1,2647
= Yes
se 0,2852 0,3214 0,8874 No
pl 0,2649 0,2341 1,1316 No
in 0,1484 0,1047 1,4174 Yes
be 0,0966 0,0907 1,0650 No
us 0,0648 0,0774 0,8372 No
fr 0,0406 0,0482 0,8423 No
20 Josef K.J. Martens

br 0,0316 0,0406 0,7783 No
nl 0,0221 0,0198 1,1162 No
cn 0,0178 0,0181 0,9834 No
kr 0,0098 0,0088 1,1136 No
gb 0,0029 0,0041 0,7073 No
ca 0,0029 0,0061 0,4754 No
SE 0,0028 0,0083 0,3373 No
ru 0,0024 0,0053 0,4528 No
jp 0,0023 0,0023 1,0000 No
de 0,0016 0,0008 2,0000 Yes
au 0,0014 0,0029 0,4828 No
my 0,0009 0,0019 0,4737 No
0 0,0009 0,0037 0,2432 No
th 0,0003 0,0004 0,7500 No

There is a rudimentary evaluation applied with respect of the ‘distance’ between
the percentage of the handled incidents by country versus the usage of the Wait / User
state. Distance is calculated by dividing the Total incident percentage by the Use
Wait/User percentage. A threshold of 1,26 (126%) is used to distinguish if further
analysis into the Wait / User state use for that particular country would be useful. The
threshold is based on the average of the Distance (0,8548) added with one Standard
Deviation (0,4099).
The resulting countries for further analysis are “in” and “de”.

3.5 Q4 Process Conformity per Organization
Research: Do organization A2 and C work in the same way?
Element Description
Datasets Incidents & Closed Problems
Assumptions Test this for the top product with issues, as this will be the
largest impact if optimized.
Only successfully closed cases are compared. This allows for
standard flow to be expected.
Method Compare the process flows of A2 and C. Research on attributes
organization AND product AND endpoint
Threshold No threshold is applicable in process flow analysis
Professional use of Process Mining for analyzing Business Processes 21

Filters used on attributes of the dataset (in sequence)
Filter name Filter by: Event values:
Attribute Organization A2 | C
Attribute Product PROD424
Endpoint Filter by activity, Start event values: All
discard cases End event values: Completed / Closed
The resulting dataset has N=7 cases for A2 and N=448 for C

Results:
These resulting sets did not give enough information to be conclusive if both
organizations have similar operations, because the spread in the case variants was
huge, as well as a too small subset for Organization A2. The attribute Product was
removed from the filterset, resulting in other datasets: N=1258 for Organization A
and N=3274 for Organization C.

Results:

The short answer to the question is no; the organizations do not work in the same
way. When comparing the process flows derived from the resulting datasets, settings
for Activities at 100% and Paths for 0%, a direct difference is visible:
Organization A2 (figure 2) starts recording cases using the status Queued /
Awaiting assignment, whereas Organization C (figure 3) starts recording cases with
the status Accepted / In Progress. Furthermore, the event Wait / User is used more
frequently in Organization C3. This is a different way of working.
22 Josef K.J. Martens

Fig. 2. Process flow for organization A2

Fig. 3. Process flow for organization C

4 Conclusions & Recommendations

This chapter discusses the conclusions and recommendations, about the research,
the aims for this research paper and the limitations about the results.

4.1 Conclusions

The data provided in this challenge was prepared well, but not yet cleaned, as some
result sets show empty fields. The conclusion is that in such cases a choice should be
made such as rework should be done to augment the dataset to become most effective,
or strip the inconclusive cases for example. However, the researcher will best use the
most pure dataset and remark deviations found.

The questions as asked by the problem owner are answered, except for the social
network analysis question, behavior in various ways. From the results of the various
questions, there can be concluded that there is not ‘one’ way of working around the
globe whilst using the VINST system. Furthermore, within a product line there are
differences as concluded from the most and least use of the push to front mechanism.
There are manu loops found in the dataset, of unknown cause. Analysis on the
timespans shows that there are short periods of time between looping steps in most
cases.
Professional use of Process Mining for analyzing Business Processes 23

The SEMBA approach can benefit from Process Mining in creating an unbiased
insight in how processes are used within a company. The Process Mining technique is
therefore a valuable competence for Business Analysts, to complement basic
modeling based on anecdotal or more formal process registrations.
Process Mining can be used to do exploratory and in lesser fashion explanatory
research. In the situation that there is no interaction, it is not possible to do
explanatory research. Process Mining is highly related to practical use, based on the
source of the data. However, the interpretation of the data owner and data
manipulating operators is required to make founded statements and conclusive
hypothesis testing feasible.

4.2 Recommendations
These recommendations stem from the insights and musings coming from the
research. They show some insight in where consultants would go next and where
further research is recommended.
The first recommendation is to get access to the problem owner and system operators
in order to provide meaningful answers. The feeling that arose during this research is
that there might be questions behind these questions of higher importance. For
example, issues with outsourcing could be driving the questions, or Service Level
Agreement performance.

The second recommendation is to inquire about the procedural approach between the
different organizations. The loops as presented in the data are a typical symptom of a
missing event or misuse by employees of a status. The other approach would be to
strip the dataset of the first iteration of “Accepted / In Progress” to make better sense
of the volumes of cases and events.

The third recommendation is about the data itself. In some cases fields did not contain
content or double entries or otherwise inconsistent content. This could be user errors,
however a choice should be made upon including or excluding such cases.

The fourth recommendation is about the system automated actions. During the
research multiple mutations were found which were executed by the user Siebel
which is assumed to be the system. There are many events where it is unclear if the
system ever should be able to set a case to such a state, i.e. “Waiting / User”.

4.2.1 Practical Implications and Limitations

The question behind the question.
Improving outsourcing? Contract issues? Performance improvement? As mentioned
at the first recommendation, the question behind the question is something more
valueable to be answered. Using the Process Mining technique researchers and
consultants get the opportunity to spot ‘the elephant in the room’ and work towards a
situation which has high client benefit.
24 Josef K.J. Martens

What research can do.
Research in the field of Process Mining is still in high flux. The role of such a
developing field is that it gives guidelines on how to proceed and discover what
works and what not. The author would like to thank and encourage anyone pushing
forward to set the boundaries, however (in)feasible the results.

What research cannot do.
Provide specific answers, sometimes. This is exactly why Business Analysis is up and
coming as a profession. Business Analysis allows for business improvement and
setting up the requirements for what the desired state would be. But moving towards a
To-Be design is always limited by time, money or quality, so there will never be a
best fitting solution. Specific and a high fit between Business demand and IT delivery
are only possible when the requirements are identified, assessed and put into context.
Then the result will be realistic on the elements of time, budget and required quality.
This realism allows for high customer satisfaction with limited resources.

About the author:

Jef Martens is a Business Analyst for Capgemini and Business Process
Management consultant. He is the curator of reference material for Capgemini on an
international scale.
Professional use of Process Mining for analyzing Business Processes 25

References
1. Luftman, J., Ben-Zvi, T.: Key Issues for IT executives 2010: Judicious IT Investments
Continue Post-Recession. MIS Quarterly Executive, Vol. 9, No.4, pp. 263-273 (2010)
2. Luftman, J., Ben-Zvi, T.: Key Issues for IT executives 2009, MIS Quarterly Executive, Vol.
9, No. 1, pp. 49-59. (2010)
3. Luftman, J., Kempaiah, R., Rigoni, E.H.: Key Issues for IT Executives 2008, MIS Quarterly
Executive, Vol. 8, No. 3. (2009)
4. Luftman, J, Kempaiah, R.: Key issues for IT executives 2007. MIS Quarterly Executive Vol.
7, No. 2 pp. 151-159 (2008)
5. Luftman, J.: Key Issues for IT Executives 2005, MIS Quarterly Executive Vol. 4 No.2, pp.
269-286, (2006)
6. Van Dongen, B. et al.: Business Processing Intelligence Challenge (BPIC) – Third
International Business Process Intelligence Challenge
http://www.win.tue.nl/bpi2013/doku.php?id=challenge (2013)
7. van der Aalst, W. M. P.: Process Mining: Discovery, Conformance and Enhancement of
Business Processes. Springer-Verlag, Berlin (2011)
8. Bonakdar, Amir, et al.: Transformative Influence of Business Processes on the Business
Model: Classifying the State of the Practice in the Software Industry, System Sciences
(HICSS), 2013 46th Hawaii International Conference on. IEEE (2013)
9. Blum, K., Landkroon, D., Tjon Tjauw Liem, J.: Informatiemanagersurvey 2010-2011 – Are
you in the driver’s seat of your information vehicle? Whitepaper by Capgemini Nederland
B.V. (2012)
http://www.nl.capgemini.com/sites/default/files/resource/pdf/Informatie_Managers_Survey_
0.pdf
10. Schreiner, K.: The Bridge and Beyond: Business Analysis Extends Its Role and Reach. IT
Professional vol.9,iss.6, pp. 50-54. IEEE Computer Society (2007)
http://dx.doi.org/10.1109/MITP.2007.122
11.Steeman, W., Volvo IT: VINST Data Set - VINST information needed to understand the
dataset, pp. 1-12 (2012)
12.Batt, R., Moynihan, L.: The viability of alternative call centre production models. Human
Resource Management Journal, Volume 12, Issue 4, pages 14–34 (2002)
DOI: 10.1111/j.1748-8583.2002.tb00075.x
13.Capgemini Nederland B.V.: SEMBA Structured Expert Method for Business Analysis,
Whitepaper by Capgemini Nederland B.V. pp. 1-4, (2011)
http://www.capgemini.com/sites/default/files/resource/pdf/SEMBA_Structured_Expert_Met
hod_for_Business_Analysis.pdf
14.Steeman, W.: BPI Challenge 2013, incidents. Ghent University. Dataset.
http://dx.doi.org/10.4121/uuid:500573e6-accc-4b0c-9576-aa5468b10cee (2013)
15.Steeman, W.: BPI Challenge 2013, open problems. Ghent University. Dataset.
http://dx.doi.org/10.4121/uuid:3537c19d-6c64-4b1d-815d-915ab0e479da (2013)
16.Steeman, W.: BPI Challenge 2013, closed problems. Ghent University. Dataset.
http://dx.doi.org/10.4121/uuid:c2c3b154-ab26-4b31-a0e8-8f2350ddac11 (2013)
17. Supply Chain Council, What is SCOR? Retrieved 10-07-2013 https://supply-chain.org/scor
18. APQC, Process Classification Framework, Accessed 10-07-2013,
http://www.apqc.org/process-classification-framework
19.TM Forum, Frameworx, eTOM and other Frameworks, Accesed 10-07-2013,
http://www.tmforum.org/TMForumFrameworx/1911/home.html
20.Banking Industry Architecture Network, About BIAN, Accessed 10-07-2013
http://bian.org/about-bian/
26 Josef K.J. Martens

21.van den Brand, P.: Training Perceptive Process Mining, inhouse for Capgemini and
Capgemini Consulting, Training date 26-04-2012, Utrecht
22.Fluxicon, Fluxicon Blog Entry, BPI Challenge 2013, Accessed 02-06-2013
https://fluxicon.com/blog/2013/06/bpi-challenge-2013/
23.Process Mining group, Accessed 10-06-2013 http://processmining.org
24.Perceptive Software: Perceptive Process Mining, Accessed 10-06-2013
http://www.perceptivesoftware.com/products/perceptive-process/process-mining