Human-Centric and Inclusive Emerging Approaches for Sustainable and Resilient Industry 5.0 Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 812

Editors


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Guest Editor
Department of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: human-centric systems; inclusive systems; industry 5.0; sustainable industrial systems; socio-technical systems design; human–robot collaboration; data-driven methods; circular economy; manufacturing systems; logistics systems; closed-loop supply chains

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Guest Editor
Department of Engineering, University of Campania “Luigi Vanvitelli”, 81031 Aversa, Italy
Interests: production planning; operations management; sustainable manufacturing; human–robot interaction

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Guest Editor
Department of Management, Information and Production Engineering, University of Bergamo, 24044 Dalmine, BG, Italy
Interests: human factors; healthcare systems; human–robot collaboration; industrial systems; human–computer interaction; inclusive systems; accessible systems; universal design; design for all; cognitive workload assessment; usability; safety; interactive systems; human–machine interfaces; accessibility

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Guest Editor
Department of Mechanics, Mathematics and Management, Politecnico di Bari, 70125 Bari, Italy
Interests: human factors; ergonomics; industrial systems; human-centric manufacturing; Industry 5.0; cognitive workload; human performance; cognitive abilities; motor skills; industrial tasks; learning processes; training systems; assembly systems; logistics systems; cognitive assistive technologies; human–machine interaction; occupational safety; worker well-being; production systems; adaptive systems

Special Issue Information

Dear Colleagues,

The transition toward Industry 5.0 marks a shift from technology-driven paradigms to human-centric, sustainable, and resilient systems, where value emerges from the interaction between humans and advanced technologies. The increasing complexity of manufacturing and logistics systems, driven by digitalization and circular economy principles, calls for emerging approaches to system design and management. In this context, attention is moving beyond a generic view of “the human” toward the individual as a key element of complex systems, characterized by diverse capabilities, cognition, and behavior. These aspects play a crucial role in shaping system adaptability and long-term sustainability.

This Special Issue aims to explore how emerging human-centric and inclusive approaches, sustainability, resilience, and circularity co-evolve in Industry 5.0 systems. By showcasing recent experimental evidence, it will contribute to overall sustainability across social, economic, and environmental dimensions. The topic aligns with Systems by addressing the modeling and analysis of complex socio-technical systems, integrating engineering, behavioral, and organizational perspectives.

We welcome contributions addressing, but not limited to, the following topics: human-centric, inclusive, and person-centered system design; human–technology interaction; human factors and ergonomics modeling in industrial contexts; and adaptive, context-aware socio-technical systems supported by data-driven approaches.

Dr. Francesco Mancusi
Dr. Valentina De Simone
Dr. Claudia Piffari
Dr. Andrea Lucchese
Guest Editors

Manuscript Submission Information

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Keywords

  • human-centric systems
  • socio-technical systems
  • person-centered approaches
  • inclusion
  • industry 5.0
  • sustainability
  • resilience
  • data-driven methods
  • human–technology interaction

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Published Papers (1 paper)

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Research

29 pages, 5937 KB  
Article
From Quantity-Based to Capacity-Aware Planning: Building Workload Control Readiness in a High-Variety Engineer-to-Order Manufacturer
by Alireza Ahmadi, Alessandra Cantini, Stefano Frecassetti, Federica Costa and Alberto Portioli-Staudacher
Systems 2026, 14(7), 864; https://doi.org/10.3390/systems14070864 - 20 Jul 2026
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Abstract
High-variety engineer-to-order (ETO) manufacturers often rely on quantity-based planning logic, in which planned output is weakly connected to finite machine and labor capacity. This disconnect can create workload peaks, congested queues, and delivery unreliability. Workload Control (WLC) offers a capacity-aware planning logic for [...] Read more.
High-variety engineer-to-order (ETO) manufacturers often rely on quantity-based planning logic, in which planned output is weakly connected to finite machine and labor capacity. This disconnect can create workload peaks, congested queues, and delivery unreliability. Workload Control (WLC) offers a capacity-aware planning logic for ETO environments, but its implementation depends on informational conditions that many firms do not initially possess, including order traceability, production time data, capacity visibility, and data-quality control. Although WLC research has demonstrated its potential through analytical and simulation-based studies, empirical and longitudinal evidence on how firms build these preconditions remains limited. This paper investigates how WLC informational readiness is progressively developed in practice. Based on an action-learning-informed longitudinal case study in the shaft department of a European manufacturer of customized complex electrical machines, the study identifies four cumulative readiness stages: diagnosing the planning problem, establishing order traceability and performance visibility, building capacity visibility, and addressing the data-quality layer. The findings show how technical data infrastructure and organizational routines jointly support the transition from quantity-based planning toward capacity-aware planning. The paper contributes a practice-grounded process model of WLC informational readiness by shifting attention from the design of WLC mechanisms to the informational and organizational conditions required before such mechanisms can operate reliably. For practice, it offers a case-derived staged roadmap for manufacturers that cannot move directly from quantity-based planning to full WLC implementation. Full article
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