IoT for Process Management

Under the theme »IoT-based Process Management«, the Fraunhofer IIS works on projects for the use of IoT data (Internet of Things) for analysis and control of spatially distributed processes in production and logistics. Methods of process mapping, which are based on IoT systems, enable a comprehensive view on procedures of physical processes and provide the data basis for process mining methods as process analysis, -control and -optimization.

IoT devices as data source for Process Mining

Automated, real-time data collection during the process execution is provided by IoT technologies, resulting in large amounts of sensor data. A model-based or AI-based generation of events extracts the process-relevant information. Afterwards, process-mining techniques are used to analyze the processes, identify deviations or optimization potentials within the operational processes.  After the deduction of recommendations for action, a feedback in the process is following. Automated production planning based on objective data is enabled. IoT devices such as smart tags, RTLS or wearables offer more efficient task processing and new interaction options for process control.

Research at Fraunhofer IIS covers both the application as well as the technological development of the following topics:

Usage of IoT devices as a data source in physical processes

Process step and event detection through model-based or AI-based processes

Process Mining

Feedback in the process by IoT devices

Decentralized process control by IoT devices

Process Step Recording of Small-Scale Ceramic Production by IoT Devices

SME's usual small-scale manual production processes consist of many spatially distributed process steps and place high demands on material flow control. In the INWIPRO project, the production progress is monitored in real time by smart tags. Thereby a consistent production plan can be controlled flexibly and efficiently. The smart tags accompany the production parts through the entire, several-week production. Current position, environment and, above all, process status information are collected. This allows an optimized production planning, which adapts processes to changed conditions such as machine.