Additive Manufacturing in an End-to-End Supply Chain Setting

Abstract Additive manufacturing (AM) is a rapidly developing group of technologies that has the potential to disrupt traditional manufacturing. AM is fundamentally different because it creates product by successive deposits of material instead of using removal or forming processes. Because of this difference, the cost and operational characteristics are distinct from traditional manufacturing. In this study, we place AM in an end-to-end supply chain setting. We propose a strategic optimization model that helps a manufacturer decide whether AM is a better option for their supply chain than traditional manufacturing. We run a scenario regimen over a wide variety of stochastic elements to determine the most important factors for AM adoption. We observe that, in line with others' observations, the magnitude of demand is the most critical factor for the decision of which method to adopt. We argue that a decrease in the cost of materials has the most potential to increase AM adoption. We place AM in an end-to-end supply chain context and propose a stochastic optimization model to help a manufacturer decide when AM is best for them. Finally, we characterize the most important factors for AM adoption on a wider scale and estimate the magnitude of that impact.