Moving to additive manufacturing for spare parts supply

N. Knofius*, M. C. van der Heijden, W. H.M. Zijm

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

This study seeks to investigate when and how a transition to additive manufacturing (AM) becomes profitable for the low-volume spare parts business. As a starting point, we conducted a case study at an OEM of radar systems which foresaw various opportunities that become available with the transition to AM. In particular, it is the case company that can perceive the prospects of shortened lead times and the promise of tool-less manufacturing. However, scepticism regarding whether a transition will pay off amid high AM piece prices and uncertain AM technology advancements remains. We employed stochastic dynamic programming to assess the situation encountered at the company. Therefore, we regarded particularities such as a decreasing AM piece price over the course of the service horizon and determined if (and when) AM should be prepared or tooling be discarded. It turned out that an immediate investment in AM technology is the most effective strategy and leads to more than 12% cost savings. Numerical experiments further substantiate the results of the case study and indicate that long lead times, high inventories, and severe backorder costs in the classical situation are all arguments in favor of an early investment in AM technology; this occurs despite an (initially) higher AM piece price and additional setup costs. Moreover, we observed that postponing the investment in AM is often not advisable. Instead, conventional manufacturing and AM are recommended to be used in parallel before making a complete transition to AM.

Original languageEnglish
Article number103134
Number of pages12
JournalComputers in industry
Volume113
Early online date7 Oct 2019
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • 3D printing
  • Defense industry
  • Digital manufacturing
  • Maintenance
  • Stochastic dynamic programming

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