Production logistics and human-computer interaction—state-of-the-art, challenges and requirements for the future

Klumpp, Matthias; Hesenius, Marc; Meyer, Ole; Ruiner, Caroline; Gruhn, Volker · 2019 · OpenAlex-citations

DOI: 10.1007/s00170-019-03785-0

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Summary

This paper addresses the critical research gap in Industry 4.0 and Cyber-Physical Systems (CPS) regarding the integration of human-computer interaction (HCI) into production logistics. While current automation concepts predominantly focus on fully autonomous systems and technical optimization, they often neglect mixed environments where humans and robots collaborate. The authors argue that sustainable automation requires an interdisciplinary approach incorporating computer science, economics, and work science to ensure worker acceptance and efficient collaboration. The study aims to develop a generalized HCI efficiency description for production logistics by analyzing human intuition, algorithmic reactions to human actions, and the management of digital work settings. The research employs a three-part interdisciplinary methodology. First, it conducts a production logistics literature review and a process study of a mid-size logistics company to identify practical hurdles and benefits of automation. Second, it performs a computer science literature review and a simulation study of a decentralized autonomous traffic control algorithm, adapting it to include human actors alongside automated robots. Third, it analyzes work science perspectives on managing workers in digitalized environments. The case study involved a company with 200 employees handling production preparation and transport, examining their experiences with automated lifting devices and transportation items. Key findings from the case study reveal that successful automation implementation depends on comprehensive change management, including worker integration, extended testing phases, and top-management commitment. The study identified that while automation can enhance worker motivation and improve work environments, initial acceptance often declines if workers lack adequate competence or if solutions are overly complex. Workers frequently underestimated long-term health and productivity benefits, highlighting the need for simple, comprehensible solutions. From a computer science perspective, the authors developed a decentralized consensus algorithm for collision avoidance in autonomous traffic. This algorithm allows autonomous vehicles to exchange status information and resolve conflicts without a central controller, prioritizing communication during conflict-free times to minimize latency and network dependency. The simulation demonstrated that such decentralized systems can effectively prevent collisions by having vehicles estimate passage times and agree on braking actions based on arrival order or vehicle ID. The significance of this work lies in its contribution to a holistic HCI model for production logistics that bridges technical capabilities with human factors. The authors conclude that future automated systems must prioritize User Experience (UX) design, ensuring that robots communicate intents and maintain a mental model of human capabilities. By integrating work science insights with decentralized control algorithms, the paper provides a framework for designing mixed human-robot environments that are not only technically efficient but also socially acceptable and ergonomically supportive. This interdisciplinary approach offers practical implications for businesses aiming to implement Industry 4.0 concepts, emphasizing that the human factor is crucial for the long-term success of automated production logistics.

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• automation