積層製造技術是一種藉由堆疊不同形狀的材料薄層進行製造的技術。這項製造技術的特性和傳統加工方式相反，使得生產變得更加彈性、同時大幅降低材料消耗量，因此被認為可能對供應鏈產上質性的影響。這些特性使得許多企業開始考慮是否能在其生產系統中導入積層製造技術，但現今在生產系統中導入積層製造技術的企業仍為少數，故其在工業中的應用將帶來何種影響仍不確定。甚者，機器的投資對企業也是一門重要的決策，該如何有效運用這些設備亦是一項重要決策，這也是為何許多企業仍保持著觀望的態度。透過先前文獻回顧，客戶對於如何透過電腦輔助設計軟體設計自己想要的產品有技術上的障礙，這個障礙使得積層製造技術無法更加普及。因此，本篇研究提出一個互動式方法，其目的為建立一個決策系統。這個決策系統將能夠支援基於積層製造面向設計和基於供應鏈面向設計，在客戶設計屬於自己的個人化產品時，同時考慮需求的不確定性、並從企業角度將供應鏈優化。本研究為第一篇發展考慮供應鏈面向的應用程式介面之研究。在產品的設計階段分析供應鏈配置的績效，基於客戶的喜好造成的需求波動進行模擬後，建議企業該如何配置其供應鏈。根據研究結果，積層製造技術在需求波動的情況下，對傳統製造程序的產能替代性能從前置時間和總成本的角度改善供應鏈的績效。

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.

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