Additive Manufacturing of Slow-Moving Automotive Spare Parts: A Supply Chain Cost Assessment
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Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-02
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Mcode
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Language
en
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Journal of Manufacturing and Materials Processing, Volume 7, issue 1
Abstract
This study develops a cost model for the additive manufacturing (AM)-produced spare parts supply chain in the automotive industry. Moreover, we evaluate the economic feasibility of AM for slow-moving automotive spare parts by comparing the costs of the traditional manufacturing (TM) spare parts supply chain (SPSC) with centralized, outsourced AM SPSC. Data from a multiple case study of an OEM in the automotive industry regarding SPSC is utilized. The supply chain costs of 14 individual spare parts were analyzed, and the total SPSC cost for the AM and TM, were compared. Three of the fourteen parts showed potential for cost-savings, if they were produced with AM instead of TM. In this context, AM polymer parts showed greater potential than metal to replace TM as the more economical option of manufacturing from a total supply chain cost perspective. This study shows that the AM competitiveness to TM, from a financial perspective, increases for spare parts with low demand, high minimum order quantity, and high TM production price. The SPSC cost model included: cost of production, transport, warehousing, and service costs. This study contributes to the emerging field of part identification for AM and the existing literature regarding cost modeling in SPSCs.Description
Funding Information: This work was supported by Direct Operations Project under Grant 323831. Publisher Copyright: © 2022 by the authors.
Keywords
additive manufacturing, automotive industry, cost assessment, part identification for AM, slow-moving spare parts, spare parts supply chain
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Citation
Ahlsell, L, Jalal, D, Khajavi, S H, Jonsson, P & Holmström, J 2023, ' Additive Manufacturing of Slow-Moving Automotive Spare Parts: A Supply Chain Cost Assessment ', Journal of Manufacturing and Materials Processing, vol. 7, no. 1, 8 . https://doi.org/10.3390/jmmp7010008
