Optimising the configuration of green supply chains under mass personalisation

Abstract

To achieve sustainable development, manufacturing firms should consider both environmental protection and customers’ growing personalised demands in supply chain management. Although research on sustainable manufacturing with focus on green supply chain management is increasing, only a few studies have emphasised the significance of mass personalisation. This study proposes a novel supply chain configuration approach that effectively combines the two aspects. A fuzzy analytic hierarchy process evaluation method is developed to rank suppliers into different green levels. Based on this, a supply chain scheduling optimisation model is established to match supply with demand. Simulation results show that the optimal solution for a scheduling scheme can not only satisfy customers’ personalised requirements on products, services functions, and completion time, but also improve the green management performance of the entire supply chain by selecting suppliers with high green levels and enabling them to achieve economies of scale, thereby verifying the reliability and validity of the model. The corresponding algorithm also shows good calculation efficiency. This study contributes to the research on sustainable manufacturing by integrating firms’ demands on green supply chain management and customers’ demands on personalisation into one research framework and provides an effective decision-making tool for managers.

Keywords:

• sustainable manufacturing
• green supply chain management
• mass personalisation
• scheduling
• fuzzy AHP
• optimisation model

Acknowledgements

The author would like to thank the anonymous reviewers and editors whose valuable comments and corrections substantially improved this paper.

Disclosure statement

No potential conflicts of interest are reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed https://doi.org/10.1080/00207543.2020.1723814.

ORCID

Jianming Yao http://orcid.org/0000-0001-9618-2723

Notes

1 Qingdao Kutesmart Co., Ltd. (n.d.). Retrieved June 2, 2019, from: http://www.kutesmart.com/web/main/index.html.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under [grant number 71872174].