Opportunities for process improvement: A cross-clientele analysis of event data using process mining

Abstract

Services organizations are always under pressure to operate under tight costs and to improve their operational efficiency. Transaction processing is one of the major operations in a services organization. An organization is typically trained to serve a standard set of processes within different domains across several clients. Although each client has their own specifics with respect to a process, there is a lot of commonality within similar processes across clients. An organization’s operational KPIs (i.e., Key Performance Indicators like processing time) across these clients when dealing with such related processes might not be similar; an organization might perform well for some clients and perform below par on others. There is a need to gain insights for such variance in performance and seek opportunities to learn from well performing client engagements (e.g., establish best practices) and leverage these learnings/insights on non-performing clients. We present a framework for analysing operational event data of related processes across different clients to gain insights on process executions. We present results of analyzing real-world transaction processing operations of a large services organization using the proposed framework. Our analysis shows that resource workload, clarity of process definitions, experience, and skill proficiency are key factors that influence the average processing time of transactions.