Open Access

Maximilian Hentsch, Christian Kuhn, Nils Künster, Frithjof Dorka, Daniel PalmORCiD

• Cycle time optimization is a fundamental skill for manufacturing planners to avoid bottlenecks and thus increase throughput of production. A learning factory, which replicates real-world manufacturing scenarios, provides an ideal environment for students to acquire this essential skill. Traditionally, cycle times in these scenarios have been manually recorded using stopwatches. This practice has become increasingly outdated with the proliferation of Industry 4.0 and Internet of Things systems that automatically take these measurements in industries, which the learning factories are designed to emulate. However, the high costs and implementation efforts associated with these systems can pose significant challenges for learning factories to adapt. To address these challenges, this paper proposes a cost-effective system for automatic cycle time measurements in learning factories. The system is composed of inexpensive and commercially available hardware such as microcontroller development boards, Radio-Frequency Identification (RFID) readers and a custom software based on open-source software that is free to use. It enables fast and economical retrofitting of existing production scenarios by equipping production stations with RFID readers and product trays with RFID tags. The solution not only enhances the realism of learning factories in terms of cycle time measurements but also introduces the students to key Industry 4.0 concepts like automation, digitalization, and real-time data tracking. By integrating this affordable system, learning factories can better align their practices with industry standards, thereby improving the training quality and preparing students more effectively for the future manufacturing environment.