Search results

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).

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.

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.

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

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.

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

Supply chain resilience (SCR) has recently become an important issue for organizations all around the world. In order to decrease the risk of failures, managers and decision makers are constantly looking for practices to enhance SCR. The purpose of this paper is to propose a research model that mainly focuses on the effect of information sharing (IS) to enhance SCR.

Survey method is used in this research. Data were collected from the supply chain, procurement, executive and IT managers of 82 of Tehran’s stock exchange production companies. Structural equation modeling with SmartPLS3 software is used to analyze the collected data.

Research results show that IS significantly enhances SCR.

Data were collected at one point in time, and therefore a longitudinal study can further investigate the results of this study. The research was carried out in production companies in Iran. Future research can examine the research model in other industry sectors, e.g. services, in other countries to validate the accuracy of the findings.

The findings of this research should help managers, and decision makers create more resilient supply chains for their own and partners’ organizations.

The research model provides insightful results regarding the significance of IS in the SCR context.