Abstract
PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.
Highlights
Additive manufacturing (AM) research is a rapidly evolving field that pioneers, develops and matures new materials, processes and technology
As this paper aims to study the impact of intermittent demand properties on AM management within a supply network, we refer the interested reader to Basten and van Houtum (2014) for a detailed survey on spare parts inventory control for single location and multi-echelon models; for earlier reviews, see Kennedy et al (2002) or Muckstadt (2005)
Within the framework of the demand classification scheme proposed by Syntetos et al (2005), this paper provides a demand-focused modeling approach to study the impact of AM addition to a supply chain and, to the best of our knowledge, is the first paper to map AMconfiguration performance tradeoffs to intermittent demand properties common to spare parts supply chains
Summary
Additive manufacturing (AM) research is a rapidly evolving field that pioneers, develops and matures new materials, processes and technology. The rest of this paper is organized as follows: Section 2 reviews applicable literature and identifies gaps; Section 3 provides a high level overview of the approach and presents our methods; Section 4 details the supply chain network and assumptions; Section 5 explores the tradeoffs of various AM configurations; Section 6 compares the performance of the centralized and distributed configurations; Section 7 explores the relationship between total demand volume and relative AM production capacity, and Section 8 provides closing remarks and extensions for future work. The supply chain can benefit from a pooling effect by aggregating demand from downstream service locations (SLs) and maximize the AM machine utilization One disadvantage from this configuration is AM-produced parts still require transportation and distribution to downstream locations, which limits savings in logistics costs and lead time reductions (Holmstrom et al, 2010). As noted by den Boer et al (2020), strategic and centralized placement of AM at key air- or seaports could exploit existing energy and infrastructure to benefit airline and shipping companies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
