Analyzing Order-to-Cash Using Process Mining A Case Study in Collaboration with Paulig

Abstract

Organizations increasingly rely on digital data to understand and improve their business processes. Process Mining is a data-driven approach that uses event logs from information systems to visualize actual process behavior and identify inefficiencies. This thesis investigates how Process Mining can be applied in practice to analyze the Order-to-Cash process, with a particular focus on the use of pre-defined reference process models and backward-looking analytical techniques. The study is conducted as a case study in collaboration with Paulig, using Infor’s Process Mining solution integrated with the ERP system M3. Through a combination of Process Mining analysis, interviews, workshops and shadowing sessions, the thesis evaluates how well a pre-defined industry-specific process model reflects an organization’s actual Order-to-Cash process and how inefficiencies and bottlenecks can be identified. The reference process model proved to be a strong baseline for understanding the overall process structure, while the analysis revealed bottlenecks related to master data issues that cause unnecessary manual interventions and longer cycle times. The results demonstrate that Process Mining can support improvements in both administrative processes and physical logistics flows by revealing systematic issues that are difficult to detect through traditional qualitative methods alone. The study also highlights the importance of combining Process Mining insights with domain knowledge and stakeholder involvement to correctly interpret results.