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Solving supplier selection problem in a textile industry using an integrated grey-MABAC method

Santonab Chakraborty et al.

International Journal of Business Performance and Supply Chain Modelling2025https://doi.org/10.1504/ijbpscm.2025.147103article
AJG 1
Weight
0.37

What the paper says

Like other industries, selection of suppliers in a textile mill also plays a decisive role by providing right quality of raw materials/components/dyes and chemicals in right quantities at right time and affordable cost, thereby establishing an effective supply chain. Acknowledging supplier selection/evaluation as a multi-criteria decision-making (MCDM) problem, various mathematical tools have been proposed to resolve the issue. In this paper, a grey-based MCDM approach, i.e. grey-multi-attributive border approximation area comparison (G-MABAC) method is proposed for solving a textile supplier selection problem, expressing relative importance of the decision makers, criteria and alternatives using grey numbers. Based on the performance scores, G-MABAC partitions six alternative suppliers into upper approximation (reliable) and lower approximation (unreliable) areas, identifying the relative strengths and weaknesses of each of them. It would act as an intelligent decision support framework in effectively adopting multi-sourcing strategy for enhanced resilience with minimum disruption in the textile supply chain.

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https://doi.org/https://doi.org/10.1504/ijbpscm.2025.147103

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@article{santonab2025,
  title        = {{Solving supplier selection problem in a textile industry using an integrated grey-MABAC method}},
  author       = {Santonab Chakraborty et al.},
  journal      = {International Journal of Business Performance and Supply Chain Modelling},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijbpscm.2025.147103},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.