Exploring the interrelations between additive manufacturing adoption barriers and supply chain vulnerabilities: the case of an original equipment manufacturer

Abstract

Purpose

This study aims to explore how certain adoption barriers of additive manufacturing (AM) technology may lead to supply chain (SC) vulnerabilities, which in turn would deteriorate supply chain resilience (SCR).

Design/methodology/approach

A case study of a leading original equipment manufacturer (OEM) that uses AM technology to directly produce end-use metal parts for different industries was performed. Primary data were collected using the in-depth interview method, which was complemented by secondary data from internal and publicly available sources. The findings were compared with the existing literature to triangulate the results.

Findings

The findings indicate that certain AM adoption barriers make the SC vulnerable to reliance on specialty sources, supplier capacity, production capacity, utilization of restricted materials, importance of product purity, raw material availability, unpredictability in customer demand, reliability of equipment, unforeseen technology failures, reliance on information flow, industrial espionage, and utilities availability.

Research limitations/implications

The SCR outcomes of the identified SC vulnerabilities and their interrelated AM adoption barriers are proposed in this study.

Practical implications

Drawing on the case study findings and the existing literature, several practices are put forward in a framework that supply chain management (SCM) may use to mitigate the identified SC vulnerabilities caused by the AM adoption barriers.

Originality/value

This is the first study to empirically examine and identify the SC vulnerabilities that are caused by the adoption barriers of AM technology.

Citation

Naghshineh, B. and Carvalho, H. (2022), "Exploring the interrelations between additive manufacturing adoption barriers and supply chain vulnerabilities: the case of an original equipment manufacturer", Journal of Manufacturing Technology Management, Vol. 33 No. 8, pp. 1473-1489. https://doi.org/10.1108/JMTM-04-2022-0148