Operational process monitoring: An object-centric approach

In business processes, an operational problem refers to a deviation and an inefficiency that prohibits an organization from reaching its goals, e.g., a delay in approving a purchase order in a Procure-To-Pay (P2P) process. Operational process monitoring aims to assess the occurrence of such operational problems by analyzing event data that record the execution of business processes. Once the problems are detected, organizations can act upon the corresponding problems with viable actions, e.g., adding more resources, bypassing problematic activities, etc. A plethora of approaches have been proposed to implement operational process monitoring. The lion’s share of existing approaches assumes that a single case notion (e.g., a purchase order in a P2P process) exists in a business process and analyzes operational problems defined over the single case notion. However, most real-life business processes manifest the interplay of multiple interrelated objects. For instance, an execution of an omnipresent P2P process involves multiple objects of different types, e.g., purchase orders, goods receipts, invoices, etc. Applying the existing approaches in these object-centric business processes results in inaccurate or misleading results. In this study, we propose a novel approach to assessing operational problems within object-centric business processes. Our approach not only ensures an accurate assessment of existing problems but also facilitates the analysis of object-centric problems that consider the interaction among different objects. We evaluate this approach by applying it to both simulated business processes and real-life business processes. • A novel approach for monitoring operational problems in object-centric processes. • Design of object-centric problem graphs to formally represent object-centric operational problems. • Design of method to evaluate the problems by analyzing object-centric event logs. • Evaluation of the approach with use cases and experiments on a real-life process.