A Perspective on Supplier Selection and Order Allocation: Literature Review

Purchasing and procurement managers should make informed decisions in selecting materials at the right time, in sufficient quantities, and at affordable prices. Supplier selection and order allocation (SSOA) is a vital aspect of purchasing and procurement processes. In this research, the techniques and decision-making methods used in SSOA from peer-reviewed journals published from 2021 to 2023 are examined. This research explores the publications through three major categories, including literature reviews (LR), deterministic optimization (DO) models, and uncertain optimization (UO) models. The related operations research techniques are also discussed. Furthermore, observations, conclusions, and suggestions for future studies are provided with details.

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Publication Details

Journal
Administrative Sciences
Published
2024-09-05
DOI
https://doi.org/10.3390/admsci14090206
Citations
1
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
0.35

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article

A Perspective on Supplier Selection and Order Allocation: Literature Review

Saman Hassanzadeh Amin, T.T.V. Nguyen, Bharat Shah
1 citations
Administrative Sciences
Advanced Manufacturing and Logistics Optimization
0.35
article

A Perspective on Supplier Selection and Order Allocation: Literature Review

Saman Hassanzadeh Amin, T.T.V. Nguyen, Bharat Shah
article en
1 citations

Abstract

Purchasing and procurement managers should make informed decisions in selecting materials at the right time, in sufficient quantities, and at affordable prices. Supplier selection and order allocation (SSOA) is a vital aspect of purchasing and procurement processes. In this research, the techniques and decision-making methods used in SSOA from peer-reviewed journals published from 2021 to 2023 are examined. This research explores the publications through three major categories, including literature reviews (LR), deterministic optimization (DO) models, and uncertain optimization (UO) models. The related operations research techniques are also discussed. Furthermore, observations, conclusions, and suggestions for future studies are provided with details.

Administrative SciencesVol. 14(9)
Toronto Metropolitan University (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 37%
Advanced Manufacturing and Logistics Optimization
0.35
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