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In search for classification and selection of spare parts suitable for additive manufacturing: a literature review

Author

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  • Casper Selmer Frandsen
  • Martin Mathias Nielsen
  • Atanu Chaudhuri
  • Jayanth Jayaram
  • Kannan Govindan

Abstract

This paper reviews the literature on additive manufacturing (AM) technologies and equipment, and spare parts classification criteria to propose a systematic process for selecting spare parts which are suitable for AM. This systematic process identifies criteria that can be used to select spare parts that are suitable for AM. The review found that there is limited research that addresses identifying processes for spare parts selection for AM, even though companies have identified this to be a key challenge in adopting AM. Seven areas for future research are identified relating to the methodology of spare parts selection for AM, processes for cross-functional integration in selecting spare parts for AM, broadening the spare parts portfolio that is suitable for AM (by considering usage of AM in conjunction with conventional technologies), and potential impact of AM on product modularity and integrality.

Suggested Citation

  • Casper Selmer Frandsen & Martin Mathias Nielsen & Atanu Chaudhuri & Jayanth Jayaram & Kannan Govindan, 2020. "In search for classification and selection of spare parts suitable for additive manufacturing: a literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 970-996, February.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:4:p:970-996
    DOI: 10.1080/00207543.2019.1605226
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    Cited by:

    1. Shuai Zhang & Kai Huang & Yufei Yuan, 2021. "Spare Parts Inventory Management: A Literature Review," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    2. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    3. Foshammer, Jeppe & Søberg, Peder Veng & Helo, Petri & Ituarte, Iñigo Flores, 2022. "Identification of aftermarket and legacy parts suitable for additive manufacturing: A knowledge management-based approach," International Journal of Production Economics, Elsevier, vol. 253(C).

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