A case study of Bayesian belief networks in industrial work process design based on utility expectation and operational performance

Purpose – The primary purpose of this study is to illustrate a practical approach for industrial work process design that, in an integrative manner, captures essential concerns from different parties associated with manufacturing. It aims explicitly to incorporate utility expectation from the perspectives of operational managers, floor workers, and financial planners into the decision making process. Design/methodology/approach – Through a real industrial scenario, the case study illustrates the use of a Bayesian belief network (BBN)‐based expert system and influence diagram in work process design. What‐if analysis is performed. Statistical tests are then used to benchmark and validate the experimental results and actual data. Findings – The results suggest that the proposed BBN framework is effective in modeling and solving the work design problem. The findings can draw meaningful insights into the adoption and capacity of BBN in the fields of ergonomics, worker health management, and performance improvement. Practical implications – Practically, the industrial problem is to compare the new stand‐up sewing cells against the traditional sit‐down sewing layout while taking into consideration of ergonomic effect (repetitive motion injury (RMI) likelihood), floor space (SF), yield (%), and cost ($). The study illustrates the use of an expert system and influence diagram to evaluate different alternatives for ergonomic work design in production process. Social implications – The results of this study can potentially improve health safety management and worker ergonomics. Originality/value – The paper is among the few systematic studies that have applied BBN and influence diagram to production ergonomics and worker health management. A methodological framework utilizing these probabilistic reasoning techniques are developed. This new framework can capture essential concerns from different parties in manufacturing.