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A fuzzy-based decision support model for monitoring on-time delivery performance: A textile industry case study

Author

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  • Nakandala, Dilupa
  • Samaranayake, Premaratne
  • Lau, H.C.W.

Abstract

This paper investigates uncertainties in complex supply chain situations and proposes a fuzzy-based decision support model for determining the chance of meeting on-time delivery in a complex supply chain environment. It integrates fuzzy logic principles and unitary structure-based supply chain model and enables addressing uncertainties associated with key inputs of on-time delivery performance for effective decision making process. The proposed pragmatic model deals with the fuzziness of the key inputs including, variations in demand forecasting, materials shortages and distribution lead time, and combines a fuzzy reasoning approach for monitoring on-time delivery of finished products. In systematically dealing with the uncertainties of complex supply chains, this model supports the minimizing of business losses that result from penalties and customer dissatisfaction, and the consequent reduced market share. Application of the proposed model is illustrated using a textile industry case study.

Suggested Citation

  • Nakandala, Dilupa & Samaranayake, Premaratne & Lau, H.C.W., 2013. "A fuzzy-based decision support model for monitoring on-time delivery performance: A textile industry case study," European Journal of Operational Research, Elsevier, vol. 225(3), pages 507-517.
  • Handle: RePEc:eee:ejores:v:225:y:2013:i:3:p:507-517
    DOI: 10.1016/j.ejor.2012.10.010
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    References listed on IDEAS

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    1. Lohman, Clemens & Fortuin, Leonard & Wouters, Marc, 2004. "Designing a performance measurement system: A case study," European Journal of Operational Research, Elsevier, vol. 156(2), pages 267-286, July.
    2. Chuu, Shian-Jong, 2011. "Interactive group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a supply chain," European Journal of Operational Research, Elsevier, vol. 213(1), pages 279-289, August.
    3. Schmitz, J. & Platts, K. W., 2004. "Supplier logistics performance measurement: Indications from a study in the automotive industry," International Journal of Production Economics, Elsevier, vol. 89(2), pages 231-243, May.
    4. Peidro, David & Mula, Josefa & Jiménez, Mariano & del Mar Botella, Ma, 2010. "A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment," European Journal of Operational Research, Elsevier, vol. 205(1), pages 65-80, August.
    5. Li, Haitao & Womer, Keith, 2012. "Optimizing the supply chain configuration for make-to-order manufacturing," European Journal of Operational Research, Elsevier, vol. 221(1), pages 118-128.
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    Citations

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    Cited by:

    1. Alessio Ishizaka, 2014. "Comparison of fuzzy logic, AHP, FAHP and hybrid fuzzy AHP for new supplier selection and its performance analysis," International Journal of Integrated Supply Management, Inderscience Enterprises Ltd, vol. 9(1/2), pages 1-22.
    2. Al-Ebbini, Lina & Oztekin, Asil & Chen, Yao, 2016. "FLAS: Fuzzy lung allocation system for US-based transplantations," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1051-1065.
    3. Derhami, Shahab & Smith, Alice E., 2017. "An integer programming approach for fuzzy rule-based classification systems," European Journal of Operational Research, Elsevier, vol. 256(3), pages 924-934.
    4. Zhang, Junlong & Lam, William H.K. & Chen, Bi Yu, 2016. "On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows," European Journal of Operational Research, Elsevier, vol. 249(1), pages 144-154.
    5. Nunes, L.J.R. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Analysis of the use of biomass as an energy alternative for the Portuguese textile dyeing industry," Energy, Elsevier, vol. 84(C), pages 503-508.
    6. Xu, Xun & Munson, Charles L. & Zeng, Shuo, 2017. "The impact of e-service offerings on the demand of online customers," International Journal of Production Economics, Elsevier, vol. 184(C), pages 231-244.

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