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Article
Publication date: 13 July 2020

Anirudh Tusnial, Satyendra Kumar Sharma, Parth Dhingra and Srikanta Routroy

The paper develops a decision-making model for supplier selection combining quality function deployment (QFD), analytic hierarchy process (AHP) and technique for order preference…

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Abstract

Purpose

The paper develops a decision-making model for supplier selection combining quality function deployment (QFD), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The efficacy of the model was demonstrated by applying it for supplier selection of lithium ion batteries.

Design/methodology/approach

The proposed methodology involved identifying customer requirements for lithium ion batteries and translating them to requisite technical characteristics using QFD. Further, separate sourcing, safety and sustainability-related supplier parameters were proposed taking into account the manufacturer's point of view. The relative weight of each parameter was then calculated using AHP, and finally, TOPSIS was used to select the best supplier.

Findings

The proposed methodology was applied to six suppliers of lithium ion batteries, and the obtained results were used to select the most and least preferred suppliers.

Practical implications

The obtained results cannot be generalized and are valid to the case environment. However, the proposed approach can be used for any environment related to supplier selection after capturing the corresponding parameters. The proposed approach does not restrict the number of parameters to be considered.

Originality/value

Many researches related to supplier evaluation are reported in literature, but few studies are available related to supplier performance evaluation for lithium ion batteries using QFD, AHP and TOPSIS. The study will provide a guideline for comparing and selecting supplier on the basis of performance in general and its application to lithium ion batteries in specific.

Details

International Journal of Productivity and Performance Management, vol. 70 no. 6
Type: Research Article
ISSN: 1741-0401

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