On the Composition of the Long Tail of Business Processes: Implications from a Process Mining Study

TL;DR

This paper investigates the distribution of business process variants across companies, showing that most processes form a long tail suitable for decentralized management, which can enhance digital transformation efforts.

Contribution

It introduces a system of indicators for process prioritization and analyzes process mining data to validate the long tail hypothesis in business processes.

Findings

Long tail distribution of processes across companies.

Long tail variants have fewer contacts and lower frequency.

Distributed improvement is suitable for long tail processes.

Abstract

Digital transformation forces companies to rethink their processes to meet current customer needs. Business Process Management (BPM) can provide the means to structure and tackle this change. However, most approaches to BPM face restrictions on the number of processes they can optimize at a time due to complexity and resource restrictions. Investigating this shortcoming, the concept of the long tail of business processes suggests a hybrid approach that entails managing important processes centrally, while incrementally improving the majority of processes at their place of execution. This study scrutinizes this observation as well as corresponding implications. First, we define a system of indicators to automatically prioritize processes based on execution data. Second, we use process mining to analyze processes from multiple companies to investigate the distribution of process value in terms of their process variants. Third, we examine the characteristics of the process variants contained in the short head and the long tail to derive and justify recommendations for their management. Our results suggest that the assumption of a long-tailed distribution holds across companies and indicators and also applies to the overall improvement potential of processes and their variants. Across all cases, process variants in the long tail were characterized by fewer customer contacts, lower execution frequencies, and a larger number of involved stakeholders, making them suitable candidates for distributed improvement.