Spare part segmentation for additive manufacturing – A framework

Abstract

Organizations strive to find new ways to manage the production of strategic spare parts with unanticipated demand and high delivery time. Additive Manufacturing (AM) is a revolutionary technology that effectively serves such needs by producing compatible spare parts in a shorter period without holding inventories. Although the promising benefits of spare parts production through AM, research is scarce in this domain. This study is developed to demonstrate the applicability of AM in spare part manufacturing. We have proposed a novel generic multi-criteria framework using Delphi, analytical hierarchy process (AHP), and segmentation approaches to identify the most compatible spares producible through AM. The applicability of the proposed framework is illustrated through a real-life case study. Delphi is used to determine and validate relevant criteria considered for spare part classification. This results in determining 11 criteria belonging to two group criteria, namely – Business Impact (BI) and Technical Compatibility (TC). AHP is used to compute the relative weights of criteria for prioritizing them. A normalization (objective criteria) and rating (subjective criteria) approach is used to evaluate the total score of each spare part for BI and TC. Spare parts are clustered into four quadrants (Type A, B, C, and D) using the segmentation approach according to the total scores of BI and TC. This generic framework benefits practitioners in identifying technically compatible spares for AM that improve business competitiveness.