Analyzing the quality of digitalization in supply chain collaboration models using an integrated fuzzy BWM-TOPSIS approach
Volume 14, Issue 3, Autumn 2024, Pages 224-243
https://doi.org/10.48313/jqem.2025.516446.1513
Shahab Bayatzadeh, Hamidreza Talaie, Ali Sorourkhah
Abstract Purpose: This study aims to evaluate and rank collaboration models in the Iranian rubber industry supply chain from the perspective of digitalization quality. Digitalization quality refers to the effective use of Industry 4.0 technologies to improve transparency, integration, agility, resilience, and sustainability. The rubber industry was selected due to its operational complexity and urgent need for digital transformation.
Methodology: A multi-criteria decision-making approach was adopted, combining the Fuzzy Best-Worst Method (BWM) for weighting the evaluation criteria and TOPSIS for ranking the collaboration models. A sensitivity analysis was also conducted to assess the robustness of the results across varying criterion weights.
Findings: The digital supply chain model ranks highest in digitalization quality, with "technology integration" as the most critical criterion. The sensitivity analysis confirms the rankings' robustness and stability across different weight scenarios.
Originality/Value: This research uniquely addresses the comparative assessment of collaboration models in the rubber industry based on digitalization quality. The use of a Fuzzy BWM-TOPSIS hybrid method and comprehensive sensitivity analysis provides a novel, practical framework for strategic decision-making in digital supply chain transformation.
Supply Chain Quality Management in Transportation Routing Using Genetic Algorithm
Volume 10, Issue 3, Autumn 2020, Pages 227-234
https://doi.org/10.48313/jqem.2020.131077
Mohammad Mehdi Movahedi, Alireza Azizi, Seyed Ahmad Shayannia
Abstract
The purpose of this article is to examine the quality of goods in the process of transfer between members of the supply chain. For this purpose, a suitable mathematical model has been designed to manage the supply channel route of the supply chain problem and the problem has been solved using a genetic algorithm. In the present study, real-world conditions such as vehicle traffic constraints as well as product quality are considered by considering returned items, and also the Markov chain is used to investigate the possibility of transfer between members of the supply chain. The innovation of this research is the introduction of the channel selection system for transportation planning in the supply chain. The results show that the method used in this study has a good performance and the optimal way of product flow in a distribution network using genetic algorithm is presented.