Optimal investment strategies for flexible workers under uncertain demand

Abstract

Skill chaining (SC) has proven to be an effective way to balance the workload and elevate the capacity of a serial production line. However, SC training is costly. Therefore, the costs and benefits of investment in cross-training must be weighed. To save costs, we propose a partial skill chaining (PSC) strategy to save cross-training investment costs in an assemble-to-order (ATO) system. First, we build a chain segment model to describe the PSC. Then, we provide a heuristic algorithm with which to determine the optimal path for cross-training. We also examine the efficiency of several training schemes from the perspective of flexibility structure. Second, we find that the costs of both overproduction and delivery delay in an ATO system with uncertain demand can be reduced after obtaining the capacity. The problem is then transformed into a trade-off between investments and benefits. Finally, we identify the relevant decision-making criteria for both flexibility capacity maximization and profit maximization.