Decision-support model to select spare parts suitable for additive manufacturing

Abstract

The opportunities to shorten supply chains, open supplier markets, improve response times, and ultimately optimize stocks, generate interest in adopting additive manufacturing (AM) in spare parts’ supply chains. However, large-scale implementation in complex industries remains a challenge, in big part due to the generalized lack of data, necessary to identify spare parts suitable for AM. The common practice is to identify parts using a bottom-up approach, heavily reliant on the knowledge of people directly involved in maintenance activities. Although interesting for demonstration proposes, to fully integrate additive manufacturing technologies in spare parts management, practitioners need comprehensive tools, based on top-down approaches, capable of quickly analysing large samples of spare parts. This paper’s objective is to contribute to the large-scale adoption of additive manufacturing, by proposing, explaining, and demonstrating, a decision-support model, designed to identify spare parts suitable for AM, and support the definition of new and optimized warehouse management strategies. The model is structured in 4 phases: Characterization; preselection from the operational perspective; technological preselection; strategy definition. It includes a multi-criteria classification system aimed at quickly preselecting suitable candidates; and environmental/cost models to facilitate strategy definition. Its application is demonstrated using a case from the paper and pulp industry. Results show that there are significant economic gains to be had by properly identifying spare parts, and consequently optimizing management strategies. However, contrary to popular belief, there are still environmental difficulties to be overcome, to enable large scale adoption.