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Sustainable supply chain visibility assessment and proposals for improvements using fuzzy logic

Uje Daniel Apeji (School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK)
Funlade T. Sunmola (School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK)

Journal of Modelling in Management

ISSN: 1746-5664

Article publication date: 7 April 2022

Issue publication date: 28 April 2023

283

Abstract

Purpose

Visibility management is essential to sustainable supply chains (SSCs), allowing the ability to see the chain end-to-end, with opportunities to derive benefits, including competitive advantage. Central to visibility management is visibility assessment and identification of areas for improvement. This paper aims to propose a method of assessing visibility in SSCs and the generation of proposals for improvement.

Design/methodology/approach

A hierarchically structured assessment template is developed that comprises of dimensions, factors and attributes of visibility in SSCs. The template permits the use of linguistic variables. A fuzzy logic approach is adopted to calculate visibility levels and generate improvement areas based on linguistic data captured through the template. An industry-based case study is used to illustrate the process.

Findings

This study reveals that visibility can be measured straightforwardly using the method developed in this paper. It is found that automation and contextual factors can significantly impact visibility levels, so also is sustainability awareness and practices adopted.

Originality/value

This paper describes a visibility assessment model that incorporates linguistic variables, fuzzy logic and the use of an adaptable visibility assessment template. The assessment model can identify potential inhibitors of visibility for SSC under study.

Keywords

Citation

Apeji, U.D. and Sunmola, F.T. (2023), "Sustainable supply chain visibility assessment and proposals for improvements using fuzzy logic", Journal of Modelling in Management, Vol. 18 No. 3, pp. 701-726. https://doi.org/10.1108/JM2-08-2021-0181

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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