Optimized skill configuration for the seru production system under an uncertain demand

Abstract

Owing to its high efficiency and flexibility, the seru production system (SPS), which originated in Japan, has attracted greater attention in management and academic studies. This research focuses on optimizing the configuration for implementing the SPS under an uncertain demand. The study is aimed at formulating a robust production system capable of effectively responding to stochastic demands. The primary issues are determining the amount of skill training required and matching workers with their corresponding skills. A stochastic optimization model is developed to minimize the total expected cost of the system, while considering the costs associated with training, staff shortage, and staff surplus. A heuristic algorithm is developed to solve this problem. Experimental results indicate that, compared to the full-skilled training strategy, appropriate partial skill training (such as the long chain skill training strategy) can yield greater benefits. The total cost and amount of skill training increase with growing differences in the product mix compositions, demand fluctuations, and number of product types. Moreover, the skill level of workers increases with a decrease in training cost and an increase in staff shortage and surplus costs.