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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.
References
[1] W. Van Der Aalst, et al., "Process mining manifesto," in Business process management workshops, 2012, pp. 169-194.
[2] W. Van Der Aalst, Process mining: discovery, conformance and enhancement of business processes: Sp
ringer Science &
Business Media, 2011.
[3] R. S. Mans, et
al., Application of process mining in healthcarea case study in a dutch hospital: Springer, 2009.
596 E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
[4] J. De Weerdt, et al., "Process Mining for the multi-faceted analysis of business processesA case study in a financial services
organization," Computers in Industry, vol. 64, pp. 57-67, 2013.
[5] M. Jans, et a
l., "A business process mining application for internal transaction fraud mitigation," Expert Systems with
Applications, vol. 38, pp. 13351-13359, 2011.
[6] E. Mahendrawathi, et a
l., "Material Movement Analysis for Warehouse Business Process Improvement with Process Mining: A
Case Study," in Asia Pacific Business Process Management, ed: Springer, 2015, pp. 115-127.
[7] M. ER and H. M. Astuti, "A Case Study on Process Mining Implementation in Modeling Supply Chain Business Process: A
L
esson Learnt," presented at the 6th International Conference on Operations and Supply Chain Management, Bali, 2014.
[8] W. M. van der Aalst, et
al., "Business process mining: An industrial application," Information Systems, vol. 32, pp. 713-732,
2007.
[9] A. Rozinat, et
al., "The need for a process mining evaluation framework in research and practice," in Business Process
Management Workshops, 2008, pp. 84-89.
[10] P. M. Group. (2009). Pr
oM. Available: http://www.processmining.org/prom/start
[11] J. Buijs, "Mapping Data Source to XES in a Generic Way," Master, Mathematics and Computer Science, Eindhoven University
of T
echnology, Eindhoven, 2010.
[12] P. M. Group. (2009). MXML (Mining eXtensible Markup Language). A
vailable: http://www.processmining.org/logs/mxml
[13] P. M. Group. (2009). X
ES (eXtensible Event Stream). Available: http://www.processmining.org/logs/xes
[14] Fluxicon. (2012). Pr
ocess Mining for Professionals. Available: http://fluxicon.com/
[15] M. Er, et al
., "Modeling and Analysis of Incoming Raw Materials Business Process: A Process Mining Approach,"
International Journal of Computer and Communication Engineering, vol. 4, p. 196, 2015.
[16] J. W. Kincaid, C
ustomer Relationship Management: Getting it Right!: Prentice Hall PTR, 2003.
[17] A. J. M. M. Weijters and A. K. A. d. Medeiros, "Process Mining with the Heuristics Miner-algorithm," Cir
p Annals-
manufacturing Technology, 20
06.
... Process mining has gained popularity as an analysis tool (Dumas et al., 2018). For instance, process mining is used in telecommunications (Mahendrawathi et al., 2015), IT management services (V azquez- Barreiros et al., 2016), library information systems (Kouzari and Stamelos, 2018), agile software development (Marques et al., 2018) and logistics (Kedem-Yemini et al., 2018). These studies report on results obtained and illustrate the value of process mining in industry (Corallo et al., 2020). ...
... This finding is aligned with results reported in process mining case studies (e.g. Mahendrawathi et al. (2015)). It can be expected that organisations are interested in addressing those issues that have more impact. ...
... Although case studies on process mining (e.g. Mahendrawathi et al. (2015), V azquez- Barreiros et al. (2016)) report on addressing issues that can entail large savings, we note that process analysts specifically consider the proportion of cases affected by the change. In other words, it is essential to explicitly denote the proportion of cases that ought to be changed by addressing the improvement opportunity. ...
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Purpose When improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities. Design/methodology/approach The study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN). Findings A set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined. Originality/value Prior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.
... Also, with recent developments in IT, there exist more sophisticated computer systems to collect, store, process, and exchange data. This has led to the emergence of Process Mining, as a bridge between data mining and process modelling [3], which has already been applied to diverse areas, such as manufacturing (Lorenz et al. [7]), telecommunications (Mahendrawathi et al. [8]), and healthcare (Rojas et al. [9]). ...
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Generally, these days people live longer but often with increased impairment and disabilities; therefore, they can benefit from assistive technologies. In this paper, we focus on the completion of activities of daily living (ADLs) by such patients, using so-called Smart Homes and Sensor Technology to collect data, and provide a suitable analysis to support the management of these conditions. The activities here are cast as states of a Markov-type process, while changes of state are indicated by sensor activations. This facilitates the extraction of key performance indicators (KPIs) in Smart Homes, e.g., the duration of an important activity, as well as the identification of anomalies in such transitions and durations. The use of semi-Markov models for such a scenario is described, where the state durations are represented by mixed gamma models. This approach is illustrated and evaluated using a publicly available Smart Home dataset comprising an event log of sensor activations, together with an annotated record of the actual activities. Results indicate that the methodology is well-suited to such scenarios.
... Process mining is a relatively young discipline that has exponentially advanced in recent years. Since its inception, it has been applied in a wide range of industries, including healthcare [11], security [12], telecommunications [13], education [14], software [15], and aviation [16], among others [17]. This has permitted the development of specialized software, both commercial-e.g., Celonis, Disco, Minit-and open-source-e.g., ProM, BupaR, pm4py- [18]. ...
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Inefficiencies in mine equipment maintenance processes result in high operation costs and reduce mine sustainability. However, current methods for process optimization are limited due to a lack of access to structured data. This research aims to test the hypothesis that process mining techniques can be used to optimize workflow for mine equipment maintenance processes using low-level data. This is achieved through a process-oriented analysis where low-level data are processed as an event log and used as input for a developed process model. We present a Discrete-Event Simulation of the maintenance process to generate an event log from low-level data and analyze the process with process mining. A case study of the maintenance process in an underground block caving mine is used to gain operational insight. The diagnosis of the mine’s maintenance process showed a loss of 23,800 equipment operating hours per year, with a non-production cost of about 1.12 MUSD/year. Process mining obtained a non-biased representation of the maintenance process and aided in identifying bottlenecks and inefficiencies in the equipment maintenance processes.
... In this paper, the researcher tries to investigate the feasibility of applying process mining to model supply chain processes through real case studies. The researcher determined that supply chain business process management as part of the implementation of Business Process Management (BPM) [14]. Then still by the same researcher, the research concept was improved by discussing CRM in 2018. ...
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Most businesses today use ecommerce stores and/or ecommerce platforms to carry out online marketing and sales activities. The rapid increase in the volume of E-commerce sales transactions normatively causes various problems that occur, especially in this case the buyer or consumer. Consumers expressed dissatisfaction in their e-commerce delivery experience. Customers often complain to sellers in the marketplace about the delay in sending the ordered package. This paper proposes a research model that is proposed in analyzing the datasets generated from the Downstream Supply Chain Management process, especially the process of selling and shipping E-Commerce goods to end customers. The mechanism used is collaborating process mining and data mining so that the resulting analysis becomes more powerful and better information is obtained compared to only analyzing separately. The results of the analysis in the case study of the E-commerce Costumer to Customer (C2C) marketplace show that process mining related to shipping goods can be explained by adding the results of data mining analysis from the datasets obtained, especially the processes in the Downstream Supply Chain Management Section.
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The study aims to explain the impact of service quality on customer loyalty, satisfaction, and switching behaviors for insurance companies in Saudi Arabia. The study used the descriptive research approach. The questionnaire was developed with the help of reliable and validated constructs taken from earlier studies for service quality, customer loyalty, customer satisfaction, customer switching. Stratified sampling method was used to collect the data (N = 211). The results indicate that service quality has a statistically significant impact on customer loyalty and satisfaction for insurance companies in Saudi Arabia.
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Purpose As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website. Design/methodology/approach PROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing. Findings This article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits. Originality/value In this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.
Chapter
For more than fifteen years, we have witnessed the effervescence of video games, first considered by a handful of scholars this hobby knew how to penetrate most homes. Video games are no longer necessarily considered a simple hobby, players have been able to take advantage of this enthusiasm to make a business, the birth of e-sport, community channels, and various monetized videos are proof of this. However, the news shows us that for some people video games can become a real addiction and bring with it its share of the problem.Since video games are by nature computer programs, we may wonder if it is not possible to retrieve information about their users in real-time to detect any addictive behavior and thus protect the player. In this paper, we will use process mining techniques to analyze player logs to try to find a behavioral pattern of addiction.KeywordsAddictionVideo GamesProcess Mining
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Process mining can help acquire insightful knowledge and heighten the system's performance. In this study, we surveyed the trajectories of 1050 sepsis patients in a regional hospital in the Netherlands from the registration to the discharge phase. Based on this real-world case study, the event log comprises events and activities related to the emergency ward, admission to hospital wards, and discharge enriched with data from lab experiments and triage checklists. At first, we aim to discover this process through Heuristics Miner (HM) and Inductive Miner (IM) methods. Then, we analyze a systematic process model based on organizational information and knowledge. Besides, we address conformance checking given medical guidelines for these patients and monitor the related flows on the systematic process model. The results show that HM and IM are inadequate in identifying the relevant process. However, using a systematic process model based on expert knowledge and organizational information resulted in an average fitness of 97.8%, a simplicity of 77.7%, and a generalization of 80.2%. The analyses demonstrate that process mining can shed light on the patient flow in the hospital and inspect the day-to-day clinical performance versus medical guidelines. Also, the process models obtained by the HM and IM methods cannot provide a concrete comprehension of the process structure for stakeholders compared to the systematic process model. The implications of our findings include the potential for process mining to improve the quality of healthcare services, optimize resource allocation, and reduce costs. Our study also highlights the importance of considering expert knowledge and organizational information in developing effective process models.
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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.
Article
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.