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The purpose of this paper is to develop a decision support tool to use with design for additive manufacturing (DfAM) and design for supply chain (DfSC) such that the Supply Chain (SC) configuration for a personalized product can be optimized under various demand uncertainties.

A simulation-based methodology is proposed in this industry-university cooperative research. Through identifying the company requirements with interview, an application programming interface (API) and simulation model were developed to solve the DfAM and DfSC problems of case company. Based on customer preference, the SC configuration is analyzed and suggestions are developed according to simulation results at the product design.

Results show the supplementary capacity of the additive manufacturing (AM) process improves the SC performance in terms of lead time and total cost. This work identifies the research gap between AM and SC, and gives a comprehensive investigation of different performance indicators, such as order fulfill rate and waste rate.

Metal AM technology was not in the mass production stage at the time of this study. Thus, this research mainly emphasizes a nonmetal AM process.

AM technology can improve SC performance through its supplementary capacity and help the SC to be more flexible, robust and resilient in terms of lead time and total cost. This research implements an API to assist decision making. The findings of this research provide case company a valuable reference while branching its business.

This is the first study that considers both DfAM and DfSC with the integration of an API. It also addresses the demand fluctuation level and stochastic demand of a personalized product in a unique approach.