The Fairness Analysis of the Supply Chain in the Saipa Automotive Group: Examining Deviations and Supplier Performance Using a Neural Network Approach

Abstract

The fairness of the supply chain refers to the ways in which members of the supply chain interact or intersect with one another. Due to imperfections in competitive markets, some members may exploit their position or circumstances, allowing them to gain excessive advantages over others. Within the Saipa Automotive Group, two suppliers, Sazehgostar and Megamotor, play a crucial role in the supply chain for Saipa, Pars Khodro, Saipa Citroën, Benro, and Zamyad. The objective of this research is to examine the deviations and production stoppages, as well as the impact of supplier performance on the fairness of parts distribution within the Saipa Group companies, and to provide solutions aimed at improving supply chain performance. To achieve this, statistical analysis of production stoppage reports from the Saipa Automotive Group during the first six months of 2024 has been conducted to investigate the behavior of automotive parts suppliers within the group’s manufacturers. The results of the statistical analyses indicate that the suppliers’ goal is to meet weekly and monthly production targets; however, they did not exhibit consistent performance in achieving daily production plans across the automotive companies in the group. Ultimately, a decision-making framework based on neural networks is proposed to enhance supply chain performance.