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In the context of Industry 5.0, socially sustainable manufacturing demands assembly systems that support diverse worker capabilities, including those of individuals with disabilities. This study investigates the impact of disability severity on system throughput and identifies which assembly configurations best promote inclusion while maintaining productivity. Using discrete event simulation, serial and parallel system flows were analysed under varying levels of product variability, buffer capacity, and worker impairment. Disability was modelled through two parameters: the increment of assembly time, representing the percentage increase in task duration for the disabled worker, and the percentage of station time executed by the disabled worker, capturing different levels of task involvement. Findings reveal that throughputs in serial systems decline sharply with higher disability severity, especially under conditions of high variability and limited buffering. In contrast, parallel flow systems exhibited greater resilience, sustaining higher performance despite severe impairments. Decision-tree analysis identified task time variability, the number of workstations, and extended task durations as critical factors in determining optimal system configurations. These effects were context-dependent and influenced by system layout. The results demonstrate that inclusive system design can be achieved without sacrificing efficiency, provided structural flexibility is embedded, especially via parallel processing strategies. This research underscores the importance of integrating flow strategy and task allocation approaches when including workers with diverse abilities, contributing to the broader vision of human-centred and inclusive manufacturing in Industry 5.0.
Finco et al. (Wed,) studied this question.