Process mining-based business process management architecture: A case study in smart factories

Some Business Process Management Systems (BPMSs) have been developed in the field of smart factories. These systems are typically based on technical or production areas and technical processes. However, many existing systems, with respect to technologies used in smart factories and also the dynamic nature of the processes in these environments, are not able meet requirements of smart factories in the business process execution. The present study presents a new prototype of BPMS architecture based on smart factories' characteristics. This prototype has several components. Tn the monitoring component, process management can take place through process mining techniques inside a defined data analysis system for collecting event logs from big data. This component could operate based on control and optimization modules. The control module is applied to discover process models and their conformity with models extracted from business process analysis using Non-dominated Sorting Genetic Algorithm-TT (NSGA-TT) and Adaptive Boosting (AdaBoost) algorithms. Also, the optimization module can improve the processes model based on Business Process Tntelligence (BPT) technique and Key Performance Tndicators (KPTs). The results of the new prototype execution on a case study indicate that the proposed architecture is highly accurate, complete, and optimal in process management for smart factories.