This project aims to model AM within the industrial supply chain to evaluate systemic improvements and the conditions that lead to an efficient supply chain. Of course, there will be some conditions where AM performs worse, or at best and SM within an end-to-end supply chain. However, our idea is that there are also opportunities for AM to significantly improve supply chain performance compared to using traditional manufacturing methods in many cases. Here, we interpret “improve” with respect to supply chain metrics of cost, speed, responsiveness, quality, and resilience. We propose to execute this study using real data from the existing supply chains system to examine the effects of AM and SM in an A/B-like study by optimizing the underlying modeling method. Using simulation or ad hoc heuristics to compare system performance can mask the effect of the manufacturing method by conflating it with effects from differences induced by the solution method.
Team Members
Byeong-Min Roh
Timothy W. Simpson
Terry P. Harrison
Project Sponsor
GM