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Cited by 974

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Vendor Selection Using Interpretive Structural Modelling (ISM)

Mandal, Anukul, Deshmukh, S.G.·1994·International Journal of Operations & Production Management

Abstract

Vendor selection is one of the most important activities of a purchasing department. Traditionally, vendors are selected for their ability to meet the quality requirement, delivery performance and the price offered. However, as they are selected not only to meet the immediate requirement but also future needs, one needs to consider many other factors when selecting a reliable vendor. Analyses some of the most important criteria which have been classified into four categories: autonomous, dependent, linkage and driver depending on their driver power and dependence. Develops an interpretive structural model (ISM) to show the inter‐relationship of different criteria and their levels of importance in the vendor selection process. Reveals that “attitude and willingness for business” and “after sales service” are as important factors as quality, delivery and practice. These criteria are dependent on all the others. This analysis could provide a meaningful analytical base in the vendor selection process.

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Metadata

Authors
Mandal, A., & Deshmukh, S. G.
Venue
International Journal of Operations & Production Management
Publisher
Emerald
Published
1994-06-01
Volume
14
Issue
6
Pages
52-59
ISSN
0144-3577 (ISSN-L)
OpenAlex
W2093996214
Cited by
974
References
0
FWCI
2.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Vendor Selection Using Interpretive Structural Modelling (ISM). https://app.cochat.ai/papers/10.1108/01443579410062086
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Mandal, A., & Deshmukh, S. G. (1994). Vendor Selection Using Interpretive Structural Modelling (ISM). International Journal of Operations & Production Management. https://doi.org/10.1108/01443579410062086

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.