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

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. (C) 2015 Published by Elsevier B.V.