Automate does not always mean Optimize – Case Study at Logistics Company

Jan Suchy, Milan Suchy, Michal Rosik, Agnes Valkova

Situation faced: Dynamic growth of digitized information creates space for the systematic collection of data related to business processes. Extraction of this data is an enormous challenge because of the existence of many systems, which store data in many formats. The logistics company examined here has fully automated its Purchase Order and Invoice Approval processes, driven by a BPM system. Logistics always deals with optimization and cost reduction, and the company asked us whether it was possible to optimize its processes further.

Action taken: In our work, we focus on the extraction, pre-processing, and analysis of data that is stored in BPM systems. We presented the methodology with which to extract business-related events from processes of the logistics company, analyzed the BPM system, deployed processes to develop a connector for extracting event data, and used process mining techniques to reconstruct processes from event logs. Advanced analytics techniques make it possible to present collected data in an “as-is” view of processes and to find bottlenecks, loops, delays, and deadlocks.

Results achieved: We identified the structure for stored data and the attributes attached to the metadata of the processes. Then we imported newly created process logs into a process mining tool. Next, we introduced a process model and its statistics based on the extracted processes. Finally, we pointed out characteristics and points for improvement in individual human activity. As a result, we identified bottlenecks, loops, suppliers’ characteristics, and found in the Purchase Order process over-allocated employees to dedicated tasks via the social network.

Lessons learned: Today’s businesses are process-driven; everything done in a business is a process. A process-driven application is a software that provides automatic execution of business processes and logs the executed activities. Most systems have design-time data that defines the processes and runtime data that includes information on executed activities. One can use connectors to extract the data in the desired process log structure. Process mining techniques allow us to reconstruct the process from logs, analyze it, and find optimization points. Processes can be analyzed from several perspectives: as human to human processes, human to system processes, and system to system processes.

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