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Analysis of Customer Fulfilment with Process Mining: A Case Study in a Telecommunication Company

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This paper presents results of process mining implementation in a characteristically unstructured customer fulfilment process in a real Telecommunication Company. The aim of process mining implementation is firstly to discover the typical customer fulfilment business process. It is also aimed at assessing the current rate of completed customer fulfilment, the typical component required for the process and the lead time for different types of customer requests. The steps to achieve the goals are to prepare, extract the data and construct the event log from the company's in house built Customer Relationship Management systems. The event log is then processed using Disco and PROM tools. The complete event log when model with Disco results in a Spaghetti-like process model with 673 different variants. In order to identify typical process, the log is filtered to include only business variants with 1% case occurrence of the total case. This enables the identification of 18 typical business variants, which differ based on the order requested, sequence of activities and occurrence of Return Work Order. Based on the typical variants, the components required to fulfil a certain order are identified. Another important findings are the fact that the completion rate is very low (only 8%). This may due to the fact that the issues faced by the field officer in processing the order and the resolution are either recorded manually or in a different systems. Finally, findings from this study can be used by the company to improve their current business process. It also stressed out the importance of resolving data integration issues in implementation of process mining in real cases.
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Procedia Computer Science 72 ( 2015 ) 588 596
1877-0509 © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer
-review under responsibility of organizing committee of Information Systems International Conference (ISICO2015)
doi: 10.1016/j.procs.2015.12.167
ScienceDirect
Available online at www.sciencedirect.com
The Third Information Systems International Conference
Analysis of Customer Fulfilment with Process Mining: A
Case Study in a Telecommunication Company
Mahendrawathi ER
a
*
, Hanim Maria Astuti
a
, Ayu Nastiti
a
a
Institut Teknologi Sepuluh Nopember, Sukolilo, Surabaya, 60111, Indonesia
Abstract
This paper presents results of process mining implementation in a characteristically unstructured customer fulfilment
process in a real Telecommunication Company. The aim of process mining implementation is firstly to discover the
typical customer fulfilment business process. It is also aimed at assessing the current rate of completed customer
fulfilment, the typical component required for the process and the lead time for different types of customer requests.
The steps to achieve the goals are to prepare, extract the data and construct the event log from the company’s in house
built Customer Relationship Management systems. The event log is then processed using Disco and PROM tools. The
complete event log when model with Disco results in a Spaghetti-like process model with 673 different variants. In
order to identify typical process, the log is filtered to include only business variants with 1% case occurrence of the
total case. This enables the identification of 18 typical business variants, which differ based on the order requested,
sequence of activities and occurrence of Return Work Order. Based on the typical variants, the components required
to fulfil a certain order are identified. Another important findings are the fact that the completion rate is very low
(only 8%). This may due to the fact that the issues faced by the field officer in processing the order and the resolution
are either recorded manually or in a different systems. Finally, findings from this study can be used by the company
to improve their current business process. It also stressed out the importance of resolving data integration issues in
implementation of process mining in real cases.
© 2015 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the scientific
committee of The Third Information Systems International Conference (ISICO 2015)
Keywords: Process Mining, Unstructured Process, Customer Order Fulfilment
1. Introduction
Telecommunication industry is one of the most competitive industries in today’s business environment.
Like many service industry, companies competing in Telecommunication industry must put great attention
on customers. In addition, Telecommunication industry is one of the most data intensive industries and
therefore the implementation of Enterprise Systems such as Enterprise Resource Planning (ERP) or
* Corresponding author. Tel.: +62-31-599-9944; fax: +62-31-5964965
E-mail address: mahendrawathi.er@gmail.com
© 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of organizing committee of Information Systems International Conference (ISICO2015)
589
E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
Customer Relationship Management (CRM) to support the business process is essentials in providing
service to the customer.
PT. ABC Indonesia is a leading Telecommunication Company in Indonesia. The company provides a
w
ide range of telecommunication services to the customers. The company has implemented in-house
developed CRM systems to support various business processes including customer order fulfilment. The
custo
mer order fulfilment process starts
with new or existing customers contacting the company’s call
center to order for services related with network and home telephone installation. The company will
process th
e requests until the service is installed and ready to use. However, the fulfilment of customer
requests can vary significantly from one request to another depending on the condition of the customers.
T
his type of process is relatively unstructured, since the company responds will very much depend on the
customer requests and condition.
Ideally, the customers want their ser
vice to be installed immediately. The company must provide a
service level agreement to satisfy the customer’s request. T
he company’s ability to meet the request
swiftly will depend on the capacity of workforce and the availability of component needed to provide the
service. The company has not been able to set a standard procedure nor service level agreement for
customer fulfilment mostly because they do not have detail insights on how the current business process is
actu
ally conducted. But, the history of customer requests is recorded in the company’s database and thus
can be utilized to address the issues.
Process mining is a technique that develops business process model from the co
mpany’s information
systems event logs [1]. It can be used to discover the business process, identify co
mpliance of business
process practiced in reality to the standard procedure, and/or identify business process enhancement [2].
P
rocess mining has been implemented in various sectors including healthcare, insurance, government, and
manufacturing [3-7]. However, more research needs to be don
e to understand the potential of process
mining to solve real problems and to compare the performance of algorithms used in process mining [8,
9].
This paper presents the works on implementation of process
mining to analyze the customer fulfilment
process. The contribution of the paper is twofold. First, it demonstrates the use of process mining to
discover typical business process from a less-structured process of a real company. Secondly, it provides
in
sights to the case company on how the customer fulfilment business process is actually conducted. The
findings are expected to provide foundation for the company
to develop standard procedures, set service
level agreement and propose potential planning proces
s to better meet the customer requests.
2. Process Mining
The wide-range use of information systems is expected to bring benefits for an enterprise to integrate
th
e disperse information across the enterprise, streamline its daily business processes, fasten the delivery
time to market and increase customers’ satisfaction. However, information systems are not always
working smoothly as expected. To some extent, information system might execute business processes
w
hich are against the desired outcome defined by an enterprise. An evaluation on the real execution of
bu
siness processes performed by an information system is necessary. This necessity has prompted the rise
of process mining research.
Process Mining is getting more attention in the literatu
re to date as a methodology to model process
based on the data recorded in an information system [1]. Process mining is typically used to discover
process
model, determine compliance to the standard model and identify potential for process
enhancement. A process mining tool called PROM [10] has been developed by a group of researchers
w
hich aims to help practitioners as well as academicians to implement process mining technique based on
various algorithms such as genetic algorithm, heuristic miner, et cetera. This tool enables to model
business process and analyze the model in a great detail.
590 E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
The implementation of process mining depends on the availability to obtain or construct high quality
event log [11]. Event log contains activities performed by specif
ic person in a specific time. Therefore,
event logs are also called the representation of a business process. Event log can be obtained directly from
th
e systems or extracted from the information systems’ databases [11]. In process mining, this event log is
req
uired to be converted into a MXML [12] or XES format [13] before being processed using PROM
tool. The conversion can be done using supporting tools such
as DISCO or NITRO provided by Fluxicon
[14].
There are two extreme of business processes as suggested by [2]: Lasagna and Spaghetti. Lasagna
proces
ses are relatively structured and the cases f
lowing through such processes are handled in a
controlled manner. In this type of process, most cases can be handled in a pre-determined manner. Since
all activities are repeatable and have a well-defined input and output, most of the activities can be
auto
mated. Spaghetti process on the other spectrum is less structured and therefore it is difficult to
determine exactly the pre- and post-conditions for activities. Spaghetti process, acco
rding to Aalst [2],
”…are driven by experience, intuition, trial-and-error, rules-of-thumb, and vague qualitative
inf
ormation”.
Researchers have implemented process mining to
model process of real cases in various types of
organization. Process mining implementation was conducted by [3] in order to discover knowledge from
h
ospital’s business processes for the purpose of hospital’s careflows improvement. Another scholar [5]
implemented process mining to perform internal fraud
mitigation in a SAP based procurement while [4]
used process mining to evaluate document manage
ment system in financial services organization. Our
previous works [6, 7, 15] also implemented process mining to model the real execution of business
processes in
cluding incoming materials, production planning and material movement in a manufacturing
company.
3. Customer Relationship Management
The ad
vance development of technology in today’s globalization era has urged enterprises to shift
their focus from company-centric to customer-centric. While in the past, the focus of an enterprise was
m
ostly on “market driven by companies”, nowadays, the focus has also changed into customers-driven
m
arket. All enterprises efforts are directed to achieve c
ustomer satisfaction. As customers are the heart of
any enterprise in doing business, to get closer with customers is essential. This idea marked the beginning
of various studies in Customer Relationship Management.
According to [16], CRM is
the strategic use of information, processes, technology and people to
manage the customers’ relationship with company in all customer life cycle”. The ultimate goal of CRM
based on the CRM hierarchy of Maslow [16] is the increase in customer loyalty. The practice of CRM
req
uires the involvement of many functions across an enterprise. Several activities are performed by four
main functions in a company namely Marketing, Sales, Product Support and Customer Service.
Marketing is the function that responsible for branding, messaging and other communication strategies
with customers. Sales function is dealing with selling and closing an agreement. Product support or
cu
stomer support focuses on answering customers’ questions and resolving customers’ problems with
co
mpanies’ products or services. The last, customer service function provides additional services to
custo
mers such as consulting and integration. Usually, this function has a direct interaction with
customers. This study focuses on evaluating the business processes of
sales function, more specifically
customer fulfilment process.
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E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
4. Methodology
The first step in conducting process mining is to define the goal of the project. The aim of process
m
ining implementation is firstly to discover the typical customer fulfilment business process. It is also
aimed at assessing the current rate of completed customer fulfilment, the typical component required for
the process and the order fulfilment time. Interviews with representatives from the case companies are
held to obtain understanding of the customer fulfilment business process. Then, the data is mapped,
prepared and extracted. Based on the data extracted the event log is constructed. The event log is
processed using Disco and PROM tools.
Two stages of analysis are conducted. In the first stage t
he complete log is analyzed to obtain the
completion rate of the cases. In the second stage of analysis, the complete log is filtered to obtain the most
frequent business variants. Business variants analysis are conducted to understand the common service
requested and components required to fulfil the request. Finally, bottleneck analysis are done to obtain
length of time required to satisfy diff
erent types of customer orders.
5. Data Extraction and
Preparation
5.1. Case Description
PT. ABC Indonesia is a Telecommunication company that implements in-house developed Enterprise
S
ystems called ISISKA to manage its customer relationship management process. ISISKA manages
information related to customers, networks, produ
cts, services and customer bills in eight (8) modules that
conduct different functions in CRM process. PT. ABC determines five main phases in fulfilling customer
orders: 1) Date En
try, 2) Date Feasible, 3) Date Validate, 4) Backroom and 5) Completion. In date entry
ph
ase customer requests are received and registered in the system by inputting customer identity
including name, address as well as the service requests. In the second phase, the request is determined to
be f
easible in a sense that it can be satisfied. In the third phase the request is considered valid and then
activated.
The next phase is backroom when t
he request is being handled and the status of the service is “Put into
service”. In backroom phase, the services are classified into four types that relate to the components of the
telephone and network service required to fulfil the request including: 1) Switch or central telephone, 2)
ma
in distribution frame (MDF), 3) line or local lin
e that connects MDF to distribution point and 4)
installation which include the installation from distribution point to the customer home. Each type of
serv
ices has two main status in backroom: Print Work Order (WO) and Return Work Order (WO). Print
WO status means a work order is released
to obtain the necessary component and conduct the requested
actions to satisfy the customer request. Return WO means that the field officer returned the work order
assigned to him due to various reasons. The status marks the progress of a certain request fulfilment. The
activ
ities in this process does not have a strict sequence, which means there are many possible variants
based on a combination of components required by th
e service and their status in the backroom. The final
phase is completion where the process ends with the delivery of
service bill to the customer.
5.2. Event Log Construction
To have an event log, the first step is to prepare and extrac
t data from database. This step consists of
identification of the activity, tables and attributes related to customer fulfillment business process and
m
apping to the DEMANDE table in ISISKA database. Mapping of
activities and attributes in the table is
shown in Table 1. Results from the data preparation
are used to extract the data from ISISKA Database.
Table 1. Mapping of attributes for each activity
592 E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
Activity
Attributes
Description
Notation
Date Entry
DATEN_DE
Request is registered
Date Entry
Date Feasible
DATFS_DE
Request is feasible
Date Feasible
Date Validate
DATVA_DE
Request is validated
Date Validate
Backroom
DATOTL_DE
DATOTC_DE
DATOTR_DE
DATOTI_DE
DATROT_L
DATROT_C
DATROT_R
DATROT_I
Work Order for Line is printed
Work Order for Central (switch) is printed
Work Order for MDF is printed
Work Order for Installation is printed
Work Order for Line is returned
Work Order for Central (switch) is returned
Work Order for MDF is returned
Work Order for Installation is returned
Print WO Line
Print WO Switch
Print WO MDF
Print WO Installation
Return WO Line
Return WO Switch
Return WO MDF
Return WO Installation
Request Finished
DATOP_DE
DATEN_OP
The telephone line is On
Operation Completed
Line On
Operation Complete
The event log is then constructed by structuring the activity data extracted from the previous stage.
Event log at least has three main attributes to identify an activity: 1) identifier or ID, 2) activity name and
3) time attribute that is known as timestamps. The case in this process mining implementation is the
custo
mer request or demand. The activities are extracted from DEMANDE table, which has one unique
identi
fier, NDEM or Demand Number. This number is set as Case ID for the event log and the
co
rresponding activities and timestamp are obtained from the same table. After event log with three
attributes are obtained, it is converted i
nto MXML format with Disco tool.
5.3 Process Mining with Heuristic Miner
First, the entire log is processed. However, as
will be described in detail later, it resulted in a “spaghetti
like” process with so many business process variant (673 business variant). Using Disco tool, we are able
to identify
business variant with less than 1% frequency out of the total cases. These business variant may
ap
pear because of a very specific customer request or situation. As the main goal of the study is to
u
nderstand the ‘typical’ process and components for customer fulfilment, the event log is filtered by
determining business variant with at least 50 case occurrence (frequency over 1%). Using this setting, the
n
umber of business process variants reduced significantly to 18, which can be considered as a good
rep
resentation of typical customer requests.
The filtered event log is then processed using Heuristic Miner Plugin in PROM tool for further
anal
ysis. As explained in [17], Heuristic Miner Algorithm uses several parameters including: dependency
th
reshold, positive observations and relative-to-best-threshold. In this study, the parameters are set as
f
ollow: dependency threshold is 1, positive observations is 0.9 and relative-to-best-threshold is 0.05.
6. Results
In this part, we present two parts of process models
as the result of the use of process mining. First
model was obtained from the complete event log and the next model was executed from the filtered event
log
. The complete event log consists of 5809 cases and 673 business process variant. Each variant
represents a certain combination and sequence of activities. The complete log result in a Spaghetti-like
m
odel which is quite unstructured in nature to depict th
e customer fulfilment process. The activities
depend on the type of the customer requests and the component used to satisfy the request. In order to
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E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
find typical processes, further filtering needs to be done. Nevertheless, result from the complete log can
be used to analyze the customer fulfilment rate. Filtered log consists of 3304 cases, 18 business process
v
ariant. The variant included in the filtered log contain at least 50 cases.
7. Analysis
This part presents three analyses that were condu
cted in the study. First analysis is performance
analysis which aimed to determine the customer fulfilment completion rate. The second analysis is to find
out the typical business process occurred in the case study. The last analysis is aimed to analyze
bottleneck.
Performance analysis was conducted because i
t is important for the company to understand the current
service level that they have provided for their customers. In order to determine the completed fulfilment
rate the complete log is analyzed with Disco Tool by determining whether the case have completed i.e.
en
d with payment. The result shows that only 418 out of 5503 can be declared as complete according to
th
e standard set by the company. This means that only 8% out of the total case is complete. This number
is very low and need further investigation.
The next analysis is to find the typical business process. It can be obtained by analyzing the filtered log
using Disco Tool. Each business variant represent th
e sequence of activities conducted for a case. The
first interesting finding is the fact that for all the cases the operation complete activity is not recorded.
Secondly, the sequence of activities are consistent from Date Entry Æ Date Feasible Æ Date Validate.
Variation occurs after this sequence depending on the component required for the service.
In order to understand the fulfilment time for the t
ypical service, a bottleneck analysis in PROM is
conducted. First, the process model is obtained by running th
e Heuristic Miner Plugin in PROM. The
process model is shown in figure 1. This model is considered to be a g
ood representation of the filtered
log because the fitness is 0.94. Based on the Bottleneck Analysis, it can be seen that the bottleneck
ap
pears between date validate and backroom activities. In order to analyze the lead time for each types of
customer order, the business variants are further grouped based on the component required for the service:
1) cases req
uiring only one of the component (switch, installation, line or MDF), 2) cases requiring a
co
mbination of installation and line, 3) cases requiring installation, line and switch, and 4) cases requiring
a combination of installation, line and MDF. The time required for these group of cases are summarized
in
table 2.
Fig. 1. Bottleneck Analysis of Filtered Log with PROM
DATEN_DE
complete
DATFS_DE
complete
DATVA_DE
complete
DATOTR_DE
complete
DATOTC_DE
complete
DATOTL_DE
complete
DATROT_R
complete
DATOTI_DE
complete
DATROT_I
complete
DATROT_L
complete
END
complete
0.59
0.13
0.27
0.35
0.65
1.00
0.00
0.00
0.00
0.09
0.91
0.00
0.00
1.00
0.00
594 E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
It can be seen from Table 2 that in most of the business variant there are high gaps between the
minimum and maximum fulfilment time, which shows that the process are highly uncontrolled.
Table 2. Time from first to last activity in customer fulfilment
Groups of
Business Process
Variants
Number
of cases
Components or
materials used
Lead Time
Minimum
(days)
Maximum
(days)
Average
(days)
Standard
Deviation
1
783
Switch
1
306
52
66.31
2
565
Installation
1
323
99
91.37
3
131
Line
1
277
62
73.66
4
239
MDF
1
334
42
69.71
5
179
Line, Installation
1
303
90
85.77
6
77
Switch, Line,
Installation
1
244
26
45.17
7
1330
MDF, Line,
Installation
1
306
63
78.45
8. Discussion
8.1. Typica
l Processes
The main goal of process mining implementation is to disco
ver typical process from a highly
unstructured business process where 673 business variants are originally discovered. A huge number of
v
ariants means it is difficult to determine the typical process needed to satisfy customer requests. With
proper filtering of the business variants focusing only on the most frequent ones, 18 typical processes
with frequency of case occurrence more than 1% out of the total cases (minimal 50 cases in each variant),
are obtained.
Based on this typical variants, it can be identified th
e sources of variation between the variants. First,
the fulfilment of customer request may include combination of one to three different types of services and
there are no standard sequence for the field officer in processing the activities. This is further complicated
by the presence of Return WO activities, which appear after the field officers start to do a certain activity
and encounter some issues.
For example, business process variants 9 and 11 both require works on Line and Installation. In both
bu
siness variants the sequence of activities are: Date Entry Æ Date Feasible Æ Date Validate Æ Print
W
O Installation Æ Print WO Line. However, unlike variant 9 where no further activities are recorded, in
v
ariant 11 the sequence continues with Return WO Line and then Return WO Installation. Similarly,
variants 13 18 all entail work on installation, line and MDF bu
t with different occurrence of Return WO
and sequence of the activities.
Several findings can be obtained from Table 3. First,
with respect to work involving a single
component, Switch is the most frequently needed service with 24% of the total cases. This is followed by
order requiring only Installation (17% of the total cases). Installation always appear to be needed when
customer requests require combination of two or three services. It is also found that 40% of the total cases
Installation work also require work on Line and MDF.
These findings can help the company understand the “standard” req
uest from the customers. From
there, they can identify standard time required to service for the request and create service level
agreement. Furthermore, the company can use the information to plan for their capacity. For example,
knowing that 40% of the cases require work on Installation, the company can give higher priority for the
field officer with the necessary capability and components required to serve Installation requests.
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E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
8.2. Low Completed Customer Request and Long Lead Time
Results described also highlight several striking diagnostics
on the customer fulfilment process in PT.
ABC. First, the number of cases recorded as complete is only 8%. The typical business variants described
in section 7.1 do not end with operation complete activity, but the length of time required from the first to
the last activities are quite long. As shown in table 3, the entire variants show wide range of minimum and
maximum lead time which results in very high standard deviation. The fastest average lead time (from the
first to the last activity) is 26 days for business variant that involve Installation, Line and Switch. The
average lead time for the rest of business variants are ov
er 42 days. Counter intuitively, while work
related to Installation, Line and Switch took relative fast, the work on only Installation, Installation and
Line or Installation, Line and MDF all took longer than 60 days. These findings need further investigation
in
order to understand the work involved in each type of service and the standard time needed to fulfil the
service.
In order to understand the ca
use of a very low completion rate of customer requests further interview
are held with representatives from the case company. While they are startled with the results, they provide
several possible cause. The first cause is that the field officers encounter some issues while processing the
work order and force them to return the WO with a “Not OK” status. Unfortunately, the continu
ation and
resolution of the Returned WO are recorded in a different systems which make it difficult to be traced.
There are also some cases when the requests are cancelled by the customers. The last possible cause is
that the field officer or staff record the activities manually and not in the Enterprise Systems.
Nevertheless, these findings highlight that the Customer
Fulfilment Process in the case company are far
from standardized. There is a need for a more standard procedure for the entire staff conducting the
operations. This will result in a more consistent service delivery for the customers.
9. Concluding Remarks
This paper highlights how process mining can
be used to discover typical process in a
characteristically unstructured customer fulfilment process. The findings presented in this paper
contribute insights for management of the case company and further implementation of process mining in
real case. Manager in PT. ABC can use the findings as a foundation to improve their business process.
First the fact that the completion rate of the customer req
uests are found to be very low deserves further
investigation. Findings regarding typical processes can be used to set standard sets of services which will
be useful for prediction and planning of capacity. The long lead time from the start of the process until the
last activity must be investigated further to understand root cause of the problem. This paper also
h
ighlights one of the main issue that could hinder the potential use of
process mining in real case i.e. data
integration. Process mining depends very much on the availability of accurate and high quality event log
as the input. When the data used to construct the event log
are stored in different databases and systems,
then it is not a very easy task to obtain and link these data. This stressed the importance of mapping the
overall activities at higher level before extracting the event log. It also highlights the importance of taking
a more holistic view of Business Process Management in implementation of Enterprise Systems.
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manufacturing Technology, 20
06.
... The benefits of process mining for improving business processes have been demonstrated in different industries [24,29], such as logistics [16], manufacturing [27], telecommunication services [20], and auditing [15]. To this end, methodologies for applying process mining, such as PM 2 framework [30], have been proposed. ...
... Process mining has been applied to real-life event logs for discovery and analysis. Such case studies have been conducted in different domains, such as the customer fulfillment process of a telecommunication company [20], IT management services [31], library information systems [19], agile software development [21], and the cargo release process of a logistic company [16]. Such studies focus on reporting the results obtained by applying process mining and illustrate the value of process mining in industry [7]. ...
... When exploring, analysts use filtering and variant analysis to find connections between various process parameters that suggest potential improvement opportunities. These findings are aligned with experiences reported in process mining case studies, such as [16,20,31]. However, analysts do not seem to use thematic analysis templates such as those proposed in Djurica et al. [9]. ...
Conference Paper
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Organizations have a vital interest in continuously improving their business processes. Process analysts can use process mining tools that provide data-driven discovery and analysis of business processes to achieve this. Current research has mainly focused on creating and evaluating new tools or reporting process mining case studies from different domains. Although usage of process mining has increased in industry, insights into how analysts work with such methods to identify improvement opportunities have consequently been limited. To reduce this gap, we conducted an exploratory interview study of seven process analysts from different domains. Our findings indicate that process analysts assess improvement opportunities by their impact, the feasibility of required implementation, and stakeholders’ input. Furthermore, our results indicate that process mining tools, when used to identify improvement opportunities, do not provide sufficient support for analysis, requiring process analysts to use additional tools. Lastly, analysts use storytelling to frame and communicate their findings to various stakeholders.
... Moreover, domain expertise is deemed indispensable, both to obtain readable outcomes and to identify improvement options ). Similar to Andrews et al. (2020), Mahendrawathi et al. (2015) also observe that the composition of an event log was far from trivial. By studying the customer fulfillment process at a telecommunication company, they especially highlight the challenge of integrating data which is dispersed over different systems (Mahendrawathi et al. 2015). ...
... Similar to Andrews et al. (2020), Mahendrawathi et al. (2015) also observe that the composition of an event log was far from trivial. By studying the customer fulfillment process at a telecommunication company, they especially highlight the challenge of integrating data which is dispersed over different systems (Mahendrawathi et al. 2015). In his recent compendium, Reinkemeyer (2020) compiles twelve PM cases from various application areas such as manufacturing and finance. ...
Article
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Process Mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of Process Mining in organisations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using Process Mining in organisations. Such an understanding has the potential to guide research by highlighting barriers for Process Mining adoption and, thus, can contribute to successful Process Mining initiatives in practice. In this respect, this paper provides a holistic view of opportunities and challenges for Process Mining in organisations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of Process Mining, both as a research field and regarding its application in organisations.
... Moreover, domain expertise is deemed indispensable, both to obtain readable outcomes and to identify improvement options ). Similar to Andrews et al. (2020), Mahendrawathi et al. (2015) also observe that the composition of an event log was far from trivial. By studying the customer fulfillment process at a telecommunication company, they especially highlight the challenge of integrating data which is dispersed over different systems (Mahendrawathi et al. 2015). ...
... Similar to Andrews et al. (2020), Mahendrawathi et al. (2015) also observe that the composition of an event log was far from trivial. By studying the customer fulfillment process at a telecommunication company, they especially highlight the challenge of integrating data which is dispersed over different systems (Mahendrawathi et al. 2015). In his recent compendium, Reinkemeyer (2020) compiles twelve PM cases from various application areas such as manufacturing and finance. ...
Article
Full-text available
Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using process mining in organizations. Such an understanding has the potential to guide research by highlighting barriers for process mining adoption and, thus, can contribute to successful process mining initiatives in practice. In this respect, the paper provides a holistic view of opportunities and challenges for process mining in organizations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of process mining, both as a research field and regarding its application in organizations.
... Boenner [44] focuses on how PM can support Source Data Scope Type of PM Goal P E C D CC PE [40] • • Discover interactions of web services [41] • • Discover process model [21] • • Accelerate the process of servitization [42] • • Discover process model [43] • • Analyze lead time and where to reduce complexity [44] • • • Analyze deviations, identify contact person [17] • • • • Analyze correlation of instances of different service, waiting time [45] • • Discover process model (+ Correlation analysis) [36] • • Analyze occurrence and duration of waiting time [46] • • Discover process model; Analyze speed to answer request [47] • • Improve process coordination, root cause analysis [48] • • Improve customer satisfaction [49] • • Analyze customer journey for customer support [50] • • Discover and analyze process model [51] • • Choreography conformance checking [19] • digital transformation in internal auditing. Besides process discovery, they also conduct conformance checking and analyze process deviations. ...
... Rowlson [49] aims at providing better visibility of their business processes by analyzing the variation in customer support, which resulted in increased efficiency. Mahendrawathi et al. [50] discover a process model from real-life event data, analyze the different variants and gather valuable insights for process improvement. ...
Conference Paper
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With the ongoing trend of servitization nurtured through digital technologies, the analysis of services as a starting point for improvement is gaining more and more importance. Service analytics has been defined as a concept to analyze the data generated during service execution to create value for providers and customers. To create more useful insights from the data, there is a continuous need for more advanced solutions for service analytics. One promising technology is process mining which has its origins in business process management. Our work provides insights into how process mining is currently used to analyze service processes and how it could be used along the service process. We find that process mining is increasingly applied for the analysis of the providers' internal operations, but more emphasis should be put on analyzing the customer interaction and experience.
... van der. Aalst, 2016;Mahendrawathi, Astuti, & Nastiti, 2015;Van Cruchten & Weigand, 2018). Among these papers, a significant proportion are based on data collected from hospitals and healthcare systems (Kurniati, Rojas, Hogg, Hall, & Johnson, 2018;Rebuge & Ferreira, 2012;Rojas, Munoz-Gama, Sepúlveda, & Capurro, 2016), some relate to education and training fields (David et al., 2015;Juhaňák, Zounek, & Rohlíková, 2017), and very few discuss industrial applications (Greyling & Jooste, 2017;Stelzer et al., 2016;Suriadi, Wynn, Ouyang, ter Hofstede, & van Dijk, 2013). ...
... Regular PM data load relates to "case ID" versus "activity". Usually, the "case ID" when applying this technique would be a form, such as a purchase order or an insurance policy (Mahendrawathi, Astuti, and Nastiti, 2015;Rebuge and Ferreira, 2012). DISCO utilizes Activity-Based Decomposition (W. ...
Article
Process Mining (PM) uses event logs extracted from process-oriented IS in order to uncover, analyse, diagnose and improve processes. However, the number of studies demonstrating PM applicability is limited, particularly in the field of logistics. This paper presents a methodological framework for a multi-faceted analysis of real-life event logs based on PM and the usefulness of its techniques, combined with traditional IE&M methods, thus offering an innovative approach on multiple levels by combining the use of PM and more traditional methods; using PM to demonstrate the actual movement of goods and generate a physical map of movements inside the warehouse; and enabling continuous tracking. A case-study, implemented on the cargo release process of a large Israeli logistics company, demonstrates this approach. Results reflect a major gap between the actual and the described processes, as an automatic creation of the process from logs shows that 64% of the customers received their goods after 4.5 hours (instead of 90 minutes, as service standard requires). Practical implications include detailed steps and a recommendation for additional analyses. Research value analysis shows that PM techniques constitute an ideal means to tackle organizational challenges by reflecting real-time situations, suggesting process improvements and creating companywide process awareness.
... From the perspective of the automotive sector analyzed units, the interest of researchers in the application of the process mining in the after-sales service process [22] and in car production processes [3] has been identified. In turn, the use of the process mining on the level of assessing customer relations has been presented in the following issues: analysis of customer fulfilment [11,15], analysis of customers journey [19,20] and customer integration in service processes [10]. ...
Chapter
The main goal of the article was to present the pre-sales of the customer service process using process mining, i.e. assessing the course of activities related to establishing contact with the customer and presenting the sale offer. As a result of the completed proceedings, it was noticed that the examined pre-sales process requires optimization due to such parameters as: long time of implementation of activities in the process, failure to take actions aimed at presenting the offer and incompatible with the clients inquiry presentation. The proceedings described in this article proved that it is possible to analyse the pre-sales process described using the combination of hidden non-participant observation methods and process mining. Unlike expost studies based on data provided by the surveyed organizations, the proposed solution eliminates errors related to data quality, but it is prone to errors related to IT infrastructure, which include problems with e-mail recipients or problems with delivery of messages.
Chapter
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Batch processing reduces processing time in a business process at the expense of increasing waiting time. If this trade-off between processing and waiting time is not analyzed, batch processing can, over time, evolve into a source of waste in a business process. Therefore, it is valuable to analyze batch processing activities to identify waiting time wastes. Identifying and analyzing such wastes present the analyst with improvement opportunities that, if addressed, can improve the cycle time efficiency (CTE) of a business process. In this paper, we propose an approach that, given a process execution event log, (1) identifies batch processing activities, (2) analyzes their inefficiencies caused by different types of waiting times to provide analysts with information on how to improve batch processing activities. More specifically, we conceptualize different waiting times caused by batch processing patterns and identify improvement opportunities based on the impact of each waiting time type on the CTE. Finally, we demonstrate the applicability of our approach to a real-life event log.
Chapter
Process Mining stellt einen der jüngeren Ansätze im Geschäftsprozessmanagement dar und dient der Visualisierung und Optimierung von Prozessen. Mithilfe von Event Logs sollen Abweichungen vom Ist‐Prozessmodell erkannt und im Zuge von Verbesserungsmaßnahmen eliminiert werden. In der wissenschaftlichen Literatur findet Process Mining in unterschiedlichsten Bereichen Anwendung, jedoch kaum in industriell geprägten Supply‐Chain‐Prozessen. Ziel dieses Beitrags ist es daher, Process Mining pilothaft im Purchase‐to‐Pay‐Prozess für Rohmaterial in einem chinesischen Werk von Bosch Power Tools (PT) anzuwenden. Hierzu werden zwei Kennzahlen (Automation Rate und Non‐Change Rate) definiert, um auf dieser Basis eine Prozessbewertung vorzunehmen und die Effekte von Process Mining zu bewerten. Es werden alle drei Formen (Process Discovery, Conformance Checking und Process Enhancement) angewendet und anschließend einer kritischen Würdigung im Hinblick auf Vorgehensweise und Ergebnisse unterzogen. Eine Verallgemeinerung der Erkenntnisse gewährleistet den Transfer auf weitere Untersuchungsbereiche wie andere Supply‐Chain‐Prozesse oder andere Branchen.
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Mevcut iş akışı yönetim sistemleri belirli bir iş sürecini uygulayabilmek için önceden bütün adımların ve diğer gerekli tanımların yapılmasını gerektirir. İş akışlarını tasarlamak karmaşık ve zaman alıcı bir iştir. Çoğunlukla gerçekleşen iş süreçleri ile olması istenen süreçler birbirinden farklıdır. Bu nedenle, ideal süreçlerden ziyade gerçekleşen süreçlerin keşfi için çeşitli algoritmalar geliştirilmiştir. Bu algoritmalar için ilk adım, uygulamada olan süreç hakkında detayları içeren olay kayıtlarının elde edilmesidir. Bu çalışmada bilişim teknolojileri (IT) sistemlerinde kayıtlı olan olay kayıtlarını kullanan alfa algoritması tanıtılmıştır. İşe alım süreçlerinde gerçekleştirilen temel faaliyetler ile süreç modelleri oluşturulmuştur. Alfa algoritmasının bir iş akışını başarılı bir şekilde modelleyebildiği gösterilmiştir. Current process management systems require a fully defined workflow design to implement a specific business process. Designing a business process is a complex and time-consuming task. Mostly workflow processes are different from those that are desired. Therefore, various algorithms have been developed for the discovery of actual processes rather than ideal processes. The first step for the discovery algorithms is to extract event logs that contain details about the process. In this study, Alpha algorithm which is used to generate a process model in event logs recorded in information technology (IT) systems is introduced. Process models are discovered using the basic activities performed in the recruitment processes. It has been shown that the alpha algorithm can successfully model a business process. Anahtar kelimeler: Süreç madenciliği, Olay kayıtları, Alfa algoritması, Süreç keşfi, İş akışı.
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Conference Paper
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More and more supply chain business processes are now supported by IT, but there are still ongoing debate on the exact value of IT in supporting supply chain operation and eventually performance. As supply chain emphasizes on managing interconnected business processes that cut across functions within firm and across partner firm, then it is reasonable to think that management of supply chain business process as part of the Business Process Management (BPM) implementation. The first step in BPM is evaluation of current condition which can be done through process mining. This paper attempts to investigate the viability of implementing process mining to model supply chain process through real case study. The case company addressed in this paper is an international manufacturing company that use SAP ERP in conducting their business processes. First a face-to-face interview with representatives from the company are conducted to understand the overall business processes. This is followed by defining and collecting the data necessary to conduct process mining. Based on the interview and initial data collection, two main challenges in implementing the process mining in the case company are identified: breakdown in business processes and information availability. Result from the case company highlights two disconnects in the business processes i.e. between source and make and between make and deliver that makes it difficult to capture and analyze the overall order fulfilment process of the case company. Finally, disconnect between physical flow of materials with the information flow is another main challenge in implementing process mining in a supply chain that has not implement RFID.
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To gain competitive advantage, hospitals try to streamline their processes. In order to do so, it is essential to have an accurate view of the “careflows” under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to discover typical paths followed by particular groups of patients. This is a non-trivial task given the dynamic nature of healthcare processes. The paper demonstrates the applicability of process mining using a real case of a gynecological oncology process in a Dutch hospital. Using a variety of process mining techniques, we analyzed the healthcare process from three different perspectives: (1) the control flow perspective, (2) the organizational perspective and (3) the performance perspective. In order to do so we extracted relevant event logs from the hospital’s information system and analyzed these logs using the ProM framework. The results show that process mining can be used to provide new insights that facilitate the improvement of existing careflows.
Conference Paper
Process mining is a technique to model and analyze business process based on traces of activities performed and stored in the database of any information systems being operated by the company i.e. event logs. This paper aims to add literature on the implementation of process mining in warehouse management process. First, extraction of all related activities from LTAK and LTAP tables of SAP Warehouse Management module is conducted. The event log are processed with Heuristic Miner Algorithm in PROM. The output of the mining is the actual business process model. The analysis of the output discover deviation from the standard procedure set by the company with the presence of additional activities and nonstandard flow of materials. The bottleneck analysis shows that the material spent a long time in the high rack and transferred between high racks. The average lead time from material receipt until issued is slightly higher than the target set the company. Further analysis of the issues revealed root-cause of the problems which are insufficient warehouse related capacity such as forklift, racks and staffs to serve incoming materials. Finally, recommendations are provided to deal with the warehouse management issues facing the case companies.
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Most organizations have some kind of process-oriented information system that keeps track of business events. Process Mining starts from event logs extracted from these systems in order to discover, analyze, diagnose and improve processes, organizational, social and data structures. Notwithstanding the large number of contributions to the process mining literature over the last decade, the number of studies actually demonstrating the applicability and value of these techniques in practice has been limited. As a consequence, there is a need for real-life case studies suggesting methodologies to conduct process mining analysis and to show the benefits of its application in real-life environments. In this paper we present a methodological framework for a multi-faceted analysis of real-life event logs based on Process Mining. As such, we demonstrate the usefulness and flexibility of process mining techniques to expose organizational inefficiencies in a real-life case study that is centered on the back office process of a large Belgian insurance company. Our analysis shows that process mining techniques constitute an ideal means to tackle organizational challenges by suggesting process improvements and creating a company-wide process awareness.