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Developing a fuzzy green supplier selection model using six sigma quality indices

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  • Chen, Kuen-Suan
  • Wang, Ching-Hsin
  • Tan, Kim-Hua

Abstract

Under the impact of global warming, enterprises must grow, take care of the protection of the green environment and reduce the impact on society. Therefore, 4R (Reduce, Reuse, Recycle, Recovery) is an important green concept. For sustainable production, improving product quality is one of the most crucial strategies, because enhancing product quality can reduce process scrap and the rate of Reduce, increase product availability, prolong maintenance intervals, and increase the rate of Reuse for maintenance as well as the rate of Recycle for the reproduction of all components. Meanwhile, owing to the increase of the service life, the time of Recovery will be postponed. Taiwan's electronics industry has developed from semiconductor technology to peripheral electronic components with a complete industrial ecosystem and a highly specialized division of labor. In the entire specialized and cooperative industrial chain, suppliers must be carefully selected to ensure the quality of each component and the final product quality. To sum up, this paper can fully reflect the six sigma indicators of the process yields and quality levels to construct a complete green supplier fuzzy selection model. The fuzzy evaluation model developed in this paper analyzes whether the method of collecting the required data is consistent with the traditional method of collecting data, which can increase the practical convenience. Besides, regardless of the sample sizes, more accurate judgments can be made to select suppliers, assisting the businesses and all suppliers to improve process quality (technical upgrades) and performance and thereby increasing 4R benefits as well as moving towards the goal of industry sustainability.

Suggested Citation

  • Chen, Kuen-Suan & Wang, Ching-Hsin & Tan, Kim-Hua, 2019. "Developing a fuzzy green supplier selection model using six sigma quality indices," International Journal of Production Economics, Elsevier, vol. 212(C), pages 1-7.
    • Handle: RePEc:eee:proeco:v:212:y:2019:i:c:p:1-7
    DOI: 10.1016/j.ijpe.2019.02.005
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    References listed on IDEAS

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    Cited by:

    1. Islam, Samiul & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Machine learning and optimization models for supplier selection and order allocation planning," International Journal of Production Economics, Elsevier, vol. 242(C).
    2. Mubarik, Muhammad Shujaat & Kazmi, Syed Hasnain Alam & Zaman, Syed Imran, 2021. "Application of gray DEMATEL-ANP in green-strategic sourcing," Technology in Society, Elsevier, vol. 64(C).
    3. Wang, Ching-Hsin & Chen, Kuen-Suan, 2020. "New process yield index of asymmetric tolerances for bootstrap method and six sigma approach," International Journal of Production Economics, Elsevier, vol. 219(C), pages 216-223.
    4. Hao, Jun & Li, Jianping & Wu, Dengsheng & Sun, Xiaolei, 2020. "Portfolio optimisation of material purchase considering supply risk – A multi-objective programming model," International Journal of Production Economics, Elsevier, vol. 230(C).
    5. Gligor, Dr David, 2020. "Birds of a feather: The impact of race on the supplier selection and evaluation process," International Journal of Production Economics, Elsevier, vol. 230(C).

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