Process Mining the Requisition-to-Pay Process: utilising QPR ProcessAnalyzer in Wärtsilä’s Indirect Procurement Department

Abstract

Process mining is on trend as organisations aim to make their operational processes more efficient. To do so, companies need a clear understanding of current processes. Research shows that people tend to be aware of 40% of a process, and reality is far more complex and inconceivable due to variations, this is where data-driven flows can create valuable insight. Complete overviews of processes can be analysed via algorithms. Thus, outlining business priorities, allowing flow simulation and automation opportunities for further progress.

The purpose of this research was to examine how process mining, more specifically QPR’s ProcessAnalyzer, can be utilised and applied to a procurement function. The goal was to analyse Wärtsilä’s Indirect Procurement Requisition-to-Pay (R2P) process, pinpoint its inefficiencies and bottlenecks, test development project ideas and key performance indicators (KPI) with real-time data, to enhance business case proposals and bring these findings to management, thus being able to prioritise developments in the correct areas.

This thesis reports methods for applying a process mining project in a business. The overall background, process mining steps and purposes, its benefits and challenges, as well as the project team’s experiences and lessons learned are presented. As well as practical information regarding insights into the process, how to identify bottlenecks, inefficiencies, and areas for improvement. Thus, basing informed decisions on real-time data, to streamline the process.

The research results confirm that process mining is a valuable tool to support developments and process improvements, already displaying a return on investment. However, it is important to create professional dashboards to present findings to management, and it is advised to have a proactive process mining representative to enhance the success of the endeavour. Though process mining has extensive potential, technology alone is not adequate, the role of process experts is significant in data transformation, validation and in drawing conclusions. Further collaboration with other teams and focusing on end-to-end continuous improvement could be a positive next step.