Investigating the effects of learning and forgetting on the feasibility of adopting additive manufacturing in supply chains

Additive Manufacturing (AM) is an emerging technology that is inspiring manufacturers to utilize its potential in reducing risk in new product design and customizing products and services. Adopting AM in the supply chain domain, however, requires further investigation. Existing studies highlight AM's superiority at lower production rates than in traditional supply chains. However, AM requires skilled laborers to operate a machine and post process manufactured parts. As a result of learning and forgetting phenomena, the manufacturing time would vary when production discontinues for a while. This research proposes a model for a supply chain enabled with AM technology and evaluates the effects of interruptions (e.g., demand fluctuations) on the feasibility of such supply chains. The analyses are extended to quantify how variations in network infrastructures, costs, and production technology could influence investment decisions in favor of AM in supply chains. The proposed model is supported by numerical studies to minimize supply chain costs. The research highlights the influence of learning-forgetting on the capacity of AM in supply chains and suggests solutions to mitigate such effects.