Abstract
Additive manufacturing (AM) technology is a technology that fabricates parts by laying material layers one on another. This opposite characteristic of AM is considered to have influence on the supply chain (SC). Many enterprises have considered to apply AM to their manufacturing process but still hesitant because the influence of AM is not certain. Thus, the purpose of this study is to develop a decision support tool to use with design for additive manufacturing (DfAM) and design for supply chain (DfSC) such that the SC configuration for a personalized product can be optimized under various demand uncertainties. An interactive 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 study to it with the investigation of different performance indicators, such as order fulfill rate and waste rate. 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.