KI

Change is necessary for companies to maintain their competitive edge—both digital and ecological change. But while external support is at hand, the drive for change must come from companies themselves. In this issue of Industry 4.0 Science, experts of the Academic Society for Work and Industrial Organization discuss how the real-world application of innovative technologies lead to resource-efficient manufacturing.

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The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.

Correctly recycling obsolete refrigeration devices plays an important role in environmental and climate protection efforts. Recycling plants are subject to regular audits to ensure their compliance with strict environmental regulations. However, the collection of audit-related data is a challenging and time-consuming task, as it is usually done manually and is prone to errors. One solution for more sustainable and efficient monitoring is to automate digital data collection using sensors and artificial intelligence. This enables a direct estimate of the expected level of pollutants. This paves the way for continuous performance monitoring and efficient management of refrigeration appliance recycling plants.

Assistance systems are being used increasingly in the context of digital transformation. They can support employees in industrial production processes both in the learning phase and in the active work phase. In this way, competencies can be built up in a way that is close to the workplace and the process as well as demand-oriented. This paper discusses the current state of research on the possible applications of these assistance systems and illustrates them with examples. Among other things, the current challenges are also highlighted. At the end of the paper, focal points for the future development of AI in industrial learning processes and research on this are identified.