CVaR-based risk assessment and control of the agricultural supply chain

Abstract

Purpose The purpose of this paper is to establish an effective risk assessment approach based on the conditional value-at-risk (CVaR) in the agricultural supply chain. Design/methodology/approach This study analyzes and assesses the risks of breeding, processing, transportation and warehousing in the agricultural supply chain. The ordered weighted averaging operator is used to sort risk control factors according to their importance and determine the main risk indicators of an enterprise. The CVaR model is utilized to establish the risk loss function, and an improved genetic algorithm is employed to identify the optimal risk control portfolios in the case of the smallest risk loss. Findings Based on the approach, the optimal combination of risk control to minimize risk losses is determined. Results show that the proportion of capital investment in risk control differs at three confidence levels, and a large amount of money needs to be invested in the production process at the source. Thus, any attempt to control the risks inherent in the agricultural supply chain must begin with the production process at the source. Originality/value Supply chain risk management has become increasingly important and significant to the operation and production of enterprises in recent years. The proposed method to assess the risk in the agricultural supply chain can benefit managers in making smart decisions to control total risk.