Human-centric production and logistics system design and management: transitioning from Industry 4.0 to Industry 5.0

Figures & data

Figure 1. Conceptual model of system design, human factors, and outcomes. Poor administration of HF has negative consequences for system operators, compromising operators’ performances and leading to poor system results.

Image of a conceptual model explaining how system performance is affected by the HF quality determined by the system designers. The system design leads to a determined state of human factors in terms of perceptual, cognitive, physical and psychosocial demands on the system operator. These human factors demands on the operator then lead to effects on operators, influencing their performance at work, in turn influencing overall system performance.

Table 1. Classification of papers included in the special issue according to topic area and the type of HF addressed.

					Human Factors
Authors	Application Areas	Technology	Humancentricity Perspective	Study Type	Perceptual	Cognitive	Physical	Psychosocial
Ulmer et al. (2023)	Manufacturing	Augmented Reality (AR)/Personalised Assistant Systems	Worker Diversity and Skills	Empirical, Technology Implementation Trial	X	X
De Lombaert et al. (2023)	Logistics/Order Picking	Automation and Robotisation	Worker Diversity and Skills	Literature Review and Semi-structured Interviews		X		X
Pasparakis, De Vries, and De Koster (2023)	Logistics/Order Picking	Mobile Robot	Job Satisfaction	Empirical and Experimental study		X		X
Vijayakumar and Sobhani (2023)	Logistics/Order Picking	Pick and Transport Robot	Worker Wellbeing	Multi-objective Optimisation and Case Study		X	X	X
Thylén, Wänström, and Hanson (2023)	Manufacturing	Automated Guided Vehicles (AGVs)	Human-Technology – Organisation Interaction	Empirical and Case Study				X
Abdous et al. (2022)	Assembly	Collaborative Robot, Mobile Robot, Exoskeletons	Worker Diversity	Multi-objective Optimisation			X
van Oudenhoven et al. (2022)	Maintenance	Data-driven Decision-making Aids	Worker Diversity and Skills	Empirical	X	X
Enang, Bashiri, and Jarvis (2023)	Manufacturing/Logistics	Digital Technologies	Human-Technology-Organisation Interaction	Literature Review	X	X	X	X
Peltokorpi et al. (2023)	Assembly	Assistive Technologies	Worker Diversity and Skills	Empirical, Technology Implementation Trial	X	X
De Assis Dornelles, Ayala, and Frank (2023)	Manufacturing	Collaborative Robot	Worker Diversity and Skills	Empirical and Case study		X		X

Ulmer, J., S. Braun, C. T. Cheng, S. Dowey, and J. Wollert. 2023. “A Human Factors-Aware Assistance System in Manufacturing Based on Gamification and Hardware Modularisation.” International Journal of Production Research, 1–16. https://doi.org/10.1080/00207543.2023.2166140

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De Lombaert, T., K. Braekers, R. De Koster, and K. Ramaekers. 2023. “In Pursuit of Humanised Order Picking Planning: Methodological Review, Literature Classification and Input from Practice.” International Journal of Production Research 61 (10): 3300–3330. https://doi.org/10.1080/00207543.2022.2079437

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Pasparakis, A., J. De Vries, and R. De Koster. 2023. “Assessing the Impact of Human–Robot Collaborative Order Picking Systems on Warehouse Workers.” International Journal of Production Research, 1–15. https://doi.org/10.1080/00207543.2023.2183343

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Vijayakumar, V., and A. Sobhani. 2023. “Performance Optimisation of Pick and Transport Robot in a Picker to Parts Order Picking System: A Human-Centric Approach.” International Journal of Production Research, 1–18. https://doi.org/10.1080/00207543.2023.2232469

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Thylén, N., C. Wänström, and R. Hanson. 2023. “Challenges in Introducing Automated Guided Vehicles in a Production Facility–Interactions Between Human, Technology, and Organisation.” International Journal of Production Research, 1–21. https://doi.org/10.1080/00207543.2023.2175310

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Abdous, M. A., X. Delorme, D. Battini, and S. Berger-Douce. 2022. “Multi-objective Collaborative Assembly Line Design Problem with the Optimisation of Ergonomics and Economics.” International Journal of Production Research, 1–16. https://doi.org/10.1080/00207543.2022.2153185

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van Oudenhoven, B., P. Van de Calseyde, R. Basten, and E. Demerouti. 2022. “Predictive Maintenance for Industry 5.0: Behavioural Inquiries from a Work System Perspective.” International Journal of Production Research, 1–20. https://doi.org/10.1080/00207543.2022.2154403

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Enang, E., M. Bashiri, and D. Jarvis. 2023. “Exploring the Transition from Techno Centric Industry 4.0 Towards Value Centric Industry 5.0: A Systematic Literature Review.” International Journal of Production Research, 1–37. https://doi.org/10.1080/00207543.2023.2221344

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Peltokorpi, J., S. Hoedt, T. Colman, K. Rutten, E. H. Aghezzaf, and J. Cottyn. 2023. “Manual Assembly Learning, Disability, and Instructions: An Industrial Experiment.” International Journal of Production Research, 1–19. https://doi.org/10.1080/00207543.2023.2195957

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De Assis Dornelles, J., N. F. Ayala, and A. G. Frank. 2023. “Collaborative or Substitutive Robots? Effects on Workers’ Skills in Manufacturing Activities.” International Journal of Production Research, https://doi.org/10.1080/00207543.2023.2240912.

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