Towards the development of a process mining service offering for the Luxembourg market

Abstract

This thesis, which is the concretization of nine month of researches and analyses within KPMG Luxembourg, allowed us to gain insight into the specificities, the market and the possible utilizations of process mining, an artificial intelligence technology that turns data into insights.

This work, that analyzes on various aspects this technology, falls within the development framework of a process mining service offering by KPMG Luxembourg, and aims at providing answers to the following question “Which aspects need to be considered in the development of a process mining service offering, and what are their implications?”. Answers for this question are studied thanks to four main parts in this work, that gradually evolve from a theoretical framework towards a practical utilization of process mining.

The state of art of the technology was our first concern. Process mining allows organizations to discover, compare and enhance their actual processes by using the data generated in their information systems during their operations. The growing interest for this technology led to a booming market that is estimated to be, in five years, ten times bigger than now. To confirm the insights we have got in the first part, we have subsequently constituted and analyzed a database of 135 process mining projects in Europe in order to study their characteristics and gain insights into the European market. Healthcare, financial and industrials appears to be the areas where the technology is the most used to enhance various type of projects.

We then analyzed the practical aspects and conditions for KPMG in investing in Celonis, the market leader in process mining software. The last part constitutes a practical analysis of a credit application process with the Celonis software, for the purposes of demonstrating some process mining capabilities and assessing the interface. Finally, this work ends with the summary of our findings and some future recommendations.