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Purpose -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. Design/methodology/approach -A simulation-based methodology is proposed in this industryuniversity 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. Findings -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. Research limitations/implications -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. Practical implications -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. Originality/value -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.

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Supplier selection is one of the key decisions in supply chain management. Companies need not only to make the “make” or “buy” decisions but also differentiate across potential suppliers in order to improve operational performance. Product design is an engineering based activity that realizes the customer requirements into functions of a new product. Many studies have pointed out that the integration of product and supply chain is a key factor for profitability and efficiency. However, most studies address supply chain performance after freezing the design of the product; only a few studies discuss when and how to incorporate supply chain decisions during product design. This paper presents a graph theory based optimization methodology to tackle this problem. The supplier selection issue is considered by evaluating its impact on both internal (e.g., ease of assembly) and external (e.g., transportation time) enterprise performances, which are aggregated as supply chain performance at the conceptual design stage. A case study in the bicycle industry demonstrates the advantages of this methodology. The presented mathematical programming formulation enables simultaneous optimization of both product design and supply chain design during the early design stages.