A neural network approach for retailer risk assessment in the aftermarket industry

Abstract

Purpose

Given the competitive environment and complicated relationships in supply chains in the modern era, it is important to take into account internal and external risks. In addition, proper methods must be designed to evaluate these risks correctly. The purpose of this paper is to provide a suitable map based on the artificial neural network technique to assess and classify the risk levels of retailers who have interconnected rules in the downstream of the supply chain.

Design/methodology/approach

In this research, a model for risk assessment with a hexagonal grid and 2D self-organizing map was applied.

Findings

According to the results, the model used in the study can provide a basis for classification of retailers based on the specified risk levels defined by the experts and risk managers of the company. Also with the model’s visual output, managers can have a better understanding of the distribution of the risk level of retailers.

Practical implications

The proposed methodology can be adopted by managers to assess the risk of members involved in the supply chain, helping them to formulate the risk mitigation strategies based on the risk levels.

Originality/value

As a part of the risk management process, organizations can use this developed method to reduce the existing risks imposed by the members or customers on the company.

Citation

Rezaei, S., Shokouhyar, S. and Zandieh, M. (2019), "A neural network approach for retailer risk assessment in the aftermarket industry", Benchmarking: An International Journal, Vol. 26 No. 5, pp. 1631-1647. https://doi.org/10.1108/BIJ-06-2018-0162