A multi-period multiple parts mixed integer linear programming model for AM adoption in the spare parts supply Chain

ABSTRACT

This research proposes a multi-period multiple parts mixed-integer linear programming optimization model for the trade-off analysis of spare parts supply through computer numerical control (CNC) manufacturing and additive manufacturing (AM). The multiple spare parts have different characteristics such as volume, shape size, and geometry complexity. The model focuses on minimizing lead times and thus reducing downtime costs. Scenario analyses are developed for some parameters to assess the robustness of the model. The analysis shows that the mix between AM-based spare parts and CNC-based spare parts is sensitive to changes in demand. For the given data, the findings demonstrate that AM is cost-effective with spare parts having high geometry complexity while CNC-based manufacturing is economically feasible for spare parts with low geometry complexity and large sizes. The proposed model can support decision-makers in selecting the optimal manufacturing method for multiple spare parts having different characteristics and attributes. The paper concludes with limitations and future directions.

KEYWORDS:

• Additive manufacturing
• supply chain
• spare parts
• optimization
• mixed integer linear programming
• conventional manufacturing

Acknowledgements

This study was made possible by the Qatar University grant# M‐QJRC‐2020‐6. The findings of this study are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contribution

Conceptualization – Shaligram Pokharel, Faris Tarlochan, and Fujio Tsumori; Methodology – Asma Mecheter, Shaligram Pokharel, and Faris Tarlochan; Writing- original draft preparation – Asma Mecheter; Writing- review and editing – all authors; Formal analysis – Asma Mecheter; Validation – Asma Mecheter; Data curation – Asma Mecheter; Investigation – Asma Mecheter; Supervision, Shaligram Pokharel, Faris Tarlochan, Fujio Tsumori; Funding acquisition – Faris Tarlochan, Shaligram Pokharel, Fujio Tsumori. All authors have read and agreed to the published version of the manuscript.

Additional information

Funding

This research was funded by Qatar University grant# M‐QJRC‐2020‐6.