Evaluation of critical success determinants to the implementation of additive manufacturing technology in the spare parts supply chain: a grey causal modelling approach

Abstract

PurposeAdditive Manufacturing technology (AMT) is swiftly gaining prominence to induce automation and innovation in manufacturing systems. It holds immense potential to change supply chain dynamics by providing the possibility of printing objects on demand. This study thus formulates and analyzes the framework to incorporate AMT to handle the spare parts supply chain management (SPSCM) in capital-intensive industries by identifying and assessing the critical success factors (CSFs).Design/methodology/approachAssessment of the CSFs is performed using the novel Grey Causal Modeling method (GCM) with the objective of making SPSCM resilient and efficient. GCM conducts causal analysis by taking into consideration cause, effects, the objectives, and the situations.FindingsFindings indicate that; Logistics Lead Time (SD4), Time to manufacture (SD3), Management Support (SD11), and Risk Management (SD20) are the most prominent causal factor having a maximum impact when incorporating AMT in SPSCM. The results also reveal that the performance of manufacturing organizations that adopt AMT is substantially influenced by internal and external factors such as Management Support (SD11) and Government Regulations (SD16).Research limitations/implicationsThis research provides valuable information for getting the global spare parts supply chain equipped for the post-COVID age, where digital technologies such as AMT will be fundamental for bolstering supply chain resilience and efficiency.Originality/valueThis research proposes a framework for performance assessment when incorporating AMT in SPSCM. Study also demonstrates methodological application of novel Grey Causal Modelling technique using a real case in a spare parts manufacturing industry in India.