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
-review under responsibility of organizing committee of Information Systems International Conference (ISICO2015)
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
, Hanim Maria Astuti
, Ayu Nastiti
Institut Teknologi Sepuluh Nopember, Sukolilo, Surabaya, 60111, Indonesia
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
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: firstname.lastname@example.org
© 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of organizing committee of Information Systems International Conference (ISICO2015)
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
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
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
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.
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
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
systems event logs . 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 .
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,
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
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
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
hich are against the desired outcome defined by an enterprise. An evaluation on the real execution of
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 . Process mining is typically used to discover
model, determine compliance to the standard model and identify potential for process
enhancement. A process mining tool called PROM  has been developed by a group of researchers
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 . 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
e systems or extracted from the information systems’ databases . In process mining, this event log is
uired to be converted into a MXML  or XES format  before being processed using PROM
tool. The conversion can be done using supporting tools such
as DISCO or NITRO provided by Fluxicon
There are two extreme of business processes as suggested by : Lasagna and Spaghetti. Lasagna
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
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 ,
”…are driven by experience, intuition, trial-and-error, rules-of-thumb, and vague qualitative
Researchers have implemented process mining to
model process of real cases in various types of
organization. Process mining implementation was conducted by  in order to discover knowledge from
ospital’s business processes for the purpose of hospital’s careflows improvement. Another scholar 
implemented process mining to perform internal fraud
mitigation in a SAP based procurement while 
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
cluding incoming materials, production planning and material movement in a manufacturing
3. Customer Relationship Management
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
ostly on “market driven by companies”, nowadays, the focus has also changed into customers-driven
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 , 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  is the increase in customer loyalty. The practice of CRM
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
stomer support focuses on answering customers’ questions and resolving customers’ problems with
mpanies’ products or services. The last, customer service function provides additional services to
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.
E.R. Mahendrawathi et al. / Procedia Computer Science 72 ( 2015 ) 588 – 596
The first step in conducting process mining is to define the goal of the project. The aim of process
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
5.1. Case Description
PT. ABC Indonesia is a Telecommunication company that implements in-house developed Enterprise
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
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
easible in a sense that it can be satisfied. In the third phase the request is considered valid and then
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)
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
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
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
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
Request is registered
Request is feasible
Request is validated
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
The telephone line is On
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
mer request or demand. The activities are extracted from DEMANDE table, which has one unique
fier, NDEM or Demand Number. This number is set as Case ID for the event log and the
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
business variant with less than 1% frequency out of the total cases. These business variant may
pear because of a very specific customer request or situation. As the main goal of the study is to
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
umber of business process variants reduced significantly to 18, which can be considered as a good
resentation of typical customer requests.
The filtered event log is then processed using Heuristic Miner Plugin in PROM tool for further
ysis. As explained in , Heuristic Miner Algorithm uses several parameters including: dependency
reshold, positive observations and relative-to-best-threshold. In this study, the parameters are set as
ollow: dependency threshold is 1, positive observations is 0.9 and relative-to-best-threshold is 0.05.
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
. 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
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
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
ariant. The variant included in the filtered log contain at least 50 cases.
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
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.
d with payment. The result shows that only 418 out of 5503 can be declared as complete according to
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
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
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
Fig. 1. Bottleneck Analysis of Filtered Log with PROM
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
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
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),
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
siness variants the sequence of activities are: Date Entry Æ Date Feasible Æ Date Validate Æ Print
O Installation Æ Print WO Line. However, unlike variant 9 where no further activities are recorded, in
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.
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
order to understand the work involved in each type of service and the standard time needed to fulfil the
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
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
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
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