Additive manufacturing and supply chain configuration: Modelling and performance evaluation

Abstract

Purpose: the aim of the study is to compare the performance of different supply chain configurations adopting Additive Manufacturing. Five input factors have been varied with the aim of testing the response of the supply chain to different starting conditions. In order to evaluate the supply chain performance, a set of key performance indicators have been identified considering both manufacturing and logistic processes. Design/methodology/approach: A discrete event simulation model has been developed in order to reproduce the behavior of the players according to their role in the supply chain. Different supply chain configurations have been modelled to assess the performance of the solution combined with different input factors. Many scenarios have been tested with the aim of identifying suitable applications of the additive technology. Findings: in general, the decentralized configuration has better logistic performance than the centralized supply chain. In fact, it is more flexible, suitable for high service levels, and less affected by the variability of the demand. However, when the distances among players are very short and the average demand is low, the benefits in adopting a decentralized configuration are very limited. Concerning the performance of the production phase, the centralized structure allows providing a better capacity utilization, exploiting the potential of a High-cost machine with higher production camera volume and speed. Practical implications: the outcomes obtained allow deriving some useful guidelines, which could help practitioners to identify a suitable application of the additive technology. Originality/value: first, the model provides a quantitative evaluation. Moreover, the study analyzes the performance of the additive technology combined with different supply chain configurations. This is a strong point since it is well known that emerging manufacturing technologies can affect the structure and the performance of the whole supply chain.

Keywords

• additive manufacturing, supply chain modelling, discrete event simulation, supply chain structure