Applying MAPE-K control loops for adaptive workflow management in smart factories

Abstract Monitoring the state of currently running processes and reacting to ad-hoc situations during runtime is a key challenge in Business Process Management (BPM) . This especially case cyber-physical environments that are characterized by high context sensitivity. MAPE-K control loops widely used for self-management these describe four phases approaching this challenge: Monitor, Analyze, Plan, Execute. In paper, we present an architectural solution as well implementation proposals using adaptive workflow management smart factories. We use Complex Event Processing (CEP) techniques process execution states Workflow System (WfMS) monitoring phase. addition, apply automated planning resolve detected exceptional continue execution. The experimental evaluation with physical factory shows potential developed approach able detect failures IoT sensor data them autonomously near real time considerable results.