Analyzing interconnected processes: using object-centric process mining to analyze procurement processes

International Journal of Data Science and Analytics, vol. 20, pp. 475–497

Abstract

Abstract The purchase-to-pay (P2P) process is one of the core business processes in any organization. It ensures the correct and efficient provisioning of materials and services. An efficient P2P process reduces operational costs by ensuring discounts, avoiding late payments, and choosing the optimal supplier for the goods. Process mining techniques help practitioners optimize the execution of P2P processes by analyzing the execution data and providing useful insights. However, existing techniques may result in misleading insights due to many-to-many relationships between business objects, e.g., between orders and invoices in the P2P process. Recently, object-centric process mining techniques have been proposed to avoid the limitations of traditional process mining techniques. In this paper, we present a case study on a real-life P2P process using object-centric process mining techniques. To that end, we adopt the well-known PM $$^{2}$$ 2 process mining project methodology to the object-centric setting and analyze the performance and compliance.

Authors 5

1. Alessandro Berti corresponding Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science Group, RWTH Aachen University, Aachen, Germany

   Process and Data Science Group, RWTH Aachen University, Ahornstrasse 55, 52074, Aachen, NRW, Germany

2. Urszula Jessen corresponding
   Affiliation as printed

   Process Insights, ECE Group Services, Hamburg, Germany

   Process Insights, ECE Group Services, Heegbarg 30, Hamburg, Hamburg, 22391, Germany

3. Gyunam Park corresponding Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science Group, RWTH Aachen University, Aachen, Germany

   Process and Data Science Group, RWTH Aachen University, Ahornstrasse 55, 52074, Aachen, NRW, Germany

4. Majid Rafiei corresponding Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science Group, RWTH Aachen University, Aachen, Germany

   Process and Data Science Group, RWTH Aachen University, Ahornstrasse 55, 52074, Aachen, NRW, Germany

5. Wil M. P. van der Aalst corresponding Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science Group, RWTH Aachen University, Aachen, Germany

   Process and Data Science Group, RWTH Aachen University, Ahornstrasse 55, 52074, Aachen, NRW, Germany