This study responded to the need for practical and transparent assessment frameworks for Industry 5.0 human-centric manufacturing maturity. Current maturity models show two recurring limitations: arithmetic mean aggregation may mask critical weaknesses when high scores compensate for low scores across criteria, and equal weighting does not reflect expert judgments about the relative importance of assessment criteria. To address these issues, we have proposed the human-centric assessment model (HCAM), which integrates the best–worst method (BWM) weighting with min–max goal programming to generate expert-weighted and non-compensatory maturity levels. The HCAM operationalizes five dimensions, which are human–machine collaboration, workforce well-being, adaptive learning, ethical use of technology, and resilience, through 30 sub-dimensions derived from a systematic synthesis of the literature. In an empirical application to twenty firms, the optimization-based evaluation changed 42% of the maturity assignments compared to a simple average, indicating that the offsetting effects were substantial in the specific sample analyzed. The BWM comparisons showed acceptable consistency across the five dimensions (maximum consistency ratio = 0.038), and the instrument as a whole demonstrated moderate to acceptable internal consistency for an exploratory validation study (Cronbach's $ \alpha $ = 0.693). Overall, the HCAM provides a rigorous and interpretable approach for assessing human-centric maturity.
Citation: Saloua Mihoubi, Badr Eddine Goumih, Touria Benazzouz. A goal programming-based human-centric assessment model for Industry 5.0 supply chains: development and validation[J]. Journal of Industrial and Management Optimization, 2026, 22(5): 2242-2271. doi: 10.3934/jimo.2026082
This study responded to the need for practical and transparent assessment frameworks for Industry 5.0 human-centric manufacturing maturity. Current maturity models show two recurring limitations: arithmetic mean aggregation may mask critical weaknesses when high scores compensate for low scores across criteria, and equal weighting does not reflect expert judgments about the relative importance of assessment criteria. To address these issues, we have proposed the human-centric assessment model (HCAM), which integrates the best–worst method (BWM) weighting with min–max goal programming to generate expert-weighted and non-compensatory maturity levels. The HCAM operationalizes five dimensions, which are human–machine collaboration, workforce well-being, adaptive learning, ethical use of technology, and resilience, through 30 sub-dimensions derived from a systematic synthesis of the literature. In an empirical application to twenty firms, the optimization-based evaluation changed 42% of the maturity assignments compared to a simple average, indicating that the offsetting effects were substantial in the specific sample analyzed. The BWM comparisons showed acceptable consistency across the five dimensions (maximum consistency ratio = 0.038), and the instrument as a whole demonstrated moderate to acceptable internal consistency for an exploratory validation study (Cronbach's $ \alpha $ = 0.693). Overall, the HCAM provides a rigorous and interpretable approach for assessing human-centric maturity.
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Saloua Mihoubi, Badr Eddine Goumih, Touria Benazzouz. A goal programming-based human-centric assessment model for Industry 5.0 supply chains: development and validation[J]. Journal of Industrial and Management Optimization, 2026, 22(5): 2242-2271. doi: 10.3934/jimo.2026082
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