Data-Driven Ergonomic Optimization in Manufacturing Systems: Productivity, Safety, and Sustainability Impacts

Harold Chukwuemeka Godwin¹, Charles Chikwendu Okpala²

^1,2 Professor: Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.  

Abstract

Manufacturing organizations face increasing pressure to enhance productivity while concurrently improving worker safety and attaining measurable sustainability outcomes. Although ergonomics is widely recognized as critical for occupational health, it is still predominantly applied through static and reactive assessment methods, which limit its strategic value in modern manufacturing systems. This study proposes a data-driven and multidisciplinary ergonomic optimization framework that integrates human-factor sensing, production analytics, machine learning, and multi-objective optimization to jointly address productivity, safety, and environmental sustainability. Empirical data were collected over a 12-month period from a discrete manufacturing facility, that comprises wearable sensor–based ergonomic data, manufacturing execution system records, and workstation-level energy and material usage data. Ergonomic risk was quantified with the application of a hybrid approach that combines established ergonomic assessment principles with machine learning models, while productivity and sustainability performance were evaluated using throughput, cycle time variability, energy intensity, and material waste indicators. A multi-objective optimization model was then applied to identify workstation configurations that balance ergonomic risk reduction, productivity improvement, and environmental efficiency. The results reveal that data-driven ergonomic optimization can reduce musculoskeletal disorder risk by approximately 32%, increase labor productivity by 12–18%, and decrease energy intensity and material waste by 8–14%. Trade-off analysis reveals that ergonomic improvements are largely synergistic with productivity and sustainability objectives rather than conflicting. These findings provide quantitative evidence that ergonomics, when operationalized as a system-level decision variable, functions as a measurable lever for sustainable manufacturing. The proposed framework offers a scalable and replicable approach for the integration of human-centered design into Industry 4.0 manufacturing systems and contributes actionable insights for researchers, practitioners, and policy makers.     

Keywords: Ergonomics, Sustainable manufacturing, Industry 4.0, Data-driven optimization, Productivity, Occupational safety, Energy efficiency

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