Li, Y., Cheng, Y., Hu, Q., Zhou, S., Ma, L. and Lim, M.K. (2019) The influence of additive manufacturing on the configuration of make-to-order spare parts supply chain under heterogeneous demand. International Journal of Production Research, 57(11), pp. 3622-3641. (doi: 10.1080/00207543.2018.1543975)
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Abstract
Additive manufacturing (AM), alongside technological developments, has been used in the production of spare parts with positive results for spare parts supply chains. In this study, we investigate spare parts supply chains serving heterogeneous demands from multiple service locations under the mode of make-to-order. We aim to compare different configurations (i.e. centralised and distributed) of spare parts supply chains in terms of their performance (e.g. sojourn time and cost) and to further propose suggestions to better configure AM-based spare parts supply chains by effectively allocating AM machines at service locations (SLs) or regional distribution centres (RDCs). In order to realise these research objectives, the simulation approach is used as the main research method. Different from the existing perception, our results illustrate that the distributed deployment of AM machines does not always guarantee a quick response, and that centralised configuration is desirable when the demand rate is relatively high due to the pooling effect. The distributed configuration, however, can still be suitable, considering the development of AM technology. Our results also indicate the possibility of a mixed configuration of AM-based supply chains with the potential for outperforming the purely centralised/distributed configuration. The criteria to design such a mixed configuration are also offered.
| Item Type: | Articles |
|---|---|
| Additional Information: | This work was supported by the Sino-Danish Center for Education and Research [Innovating your business: the Impact of AM], the National Natural Science Foundation of China [71272164], Zhejiang Federation of Humanities and Social Sciences Circles research project [2019N24], and Zhejiang Province Qianjiang Talent Plan [QJC1803001]. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Lim, Professor Ming |
| Authors: | Li, Y., Cheng, Y., Hu, Q., Zhou, S., Ma, L., and Lim, M.K. |
| College/School: | College of Social Sciences > Adam Smith Business School > Management |
| Journal Name: | International Journal of Production Research |
| Publisher: | Taylor & Francis |
| ISSN: | 0020-7543 |
| ISSN (Online): | 1366-588X |
| Published Online: | 15 November 2018 |
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