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Assessment of supplier performance based on product-development strategy by applying multi-granularity linguistic term sets

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  • Wang, Shih-Yuan
  • Chang, Sheng-Lin
  • Wang, Reay-Chen

Abstract

Measuring supplier performance possesses the properties of multi-dimension and multi-scale, it increases the complexity of the aggregation and assessment process. A focal company must develop an effective procedure for overcoming the complexity of performance measurement. Furthermore, the assessment of supplier performance must conform to the product-developed strategy of the focal company. This study is a further research deeper into the concept already introduced in Chang et al. [1] [Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. International Journal of Production Economics 2006; 100(2):348-59]; using multi-granularity linguistic variable and numerical ration scale to represent the overall supply performance. By unifying the derived information, the measurement complexity could be resolved. The fuzzy preference was constructed to adjust the consistent direction and transform information into fuzzy relationship. Finally, fuzzy linguistic quantifier guided ordered weighted aggregation (FLQG-OWA) operator with maximal entropy was computed and aggregated with all indicators to meet the current policy of the focal company.

Suggested Citation

  • Wang, Shih-Yuan & Chang, Sheng-Lin & Wang, Reay-Chen, 2009. "Assessment of supplier performance based on product-development strategy by applying multi-granularity linguistic term sets," Omega, Elsevier, vol. 37(1), pages 215-226, February.
    • Handle: RePEc:eee:jomega:v:37:y:2009:i:1:p:215-226
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    References listed on IDEAS

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    Cited by:

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    2. Sun, Bingzhen & Ma, Weimin, 2015. "An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application," Omega, Elsevier, vol. 51(C), pages 83-92.
    3. Mukherjee, Krishnendu, 2014. "Supplier selection criteria and methods: past, present and future," MPRA Paper 60079, University Library of Munich, Germany.
    4. Amirmahdi Malek & Sadoullah Ebrahimnejad & Reza Tavakkoli-Moghaddam, 2017. "An Improved Hybrid Grey Relational Analysis Approach for Green Resilient Supply Chain Network Assessment," Sustainability, MDPI, vol. 9(8), pages 1-28, August.
    5. Hua, Zhongsheng & Zhang, Xuemei & Xu, Xiaoyan, 2011. "Product design strategies in a manufacturer-retailer distribution channel," Omega, Elsevier, vol. 39(1), pages 23-32, January.
    6. Yan, Hong-Bin & Ma, Tieju & Huynh, Van-Nam, 2017. "On qualitative multi-attribute group decision making and its consensus measure: A probability based perspective," Omega, Elsevier, vol. 70(C), pages 94-117.
    7. Yoo, Seung Ho & Shin, Hojung & Park, Myung-Sub, 2015. "New product development and the effect of supplier involvement," Omega, Elsevier, vol. 51(C), pages 107-120.
    8. Zhou, Wei & Xu, Zeshui, 2016. "Generalized asymmetric linguistic term set and its application to qualitative decision making involving risk appetites," European Journal of Operational Research, Elsevier, vol. 254(2), pages 610-621.
    9. Abdurrezzak Sener & Mehmet Barut & Ali Dag & Mehmet Bayram Yildirim, 2021. "Impact of commitment, information sharing, and information usage on supplier performance: a Bayesian belief network approach," Annals of Operations Research, Springer, vol. 303(1), pages 125-158, August.

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