Expert system and entropy‐ordered weighted average method in cold supply chain risk evaluation

Abstract

If not well identified and controlled, risks in systematically engineered cold supply chains can lead directly to food safety incidents. In current approaches to cold supply chain risk evaluation, there is a lack of systematic classification and quantitative analysis of the influences of risk factors in the chain. To accurately evaluate unknown risks that can exert a fluctuating influence and result in great losses, this study builds a knowledge base of expert systems based on expertise and extensive experience and modifies the expert weights in conjunction with entropy weights to reduce subjective error. An ordered weighted average (OWA) operator is used to evaluate risk in a cold supply chain effectively. First, in combination with a case study of a typical fresh food e‐commerce enterprise, a typical fresh food e‐commerce enterprise, this paper identifies the risks in a food refrigeration supply chain. Second, relying on an expert group with professional knowledge and rich experience, an expert system knowledge base is constructed, risks are assigned, and the expert weight is modified in combination with the entropy weight method. Finally, based on the OWA operator, the modified risk assignment is effectively evaluated, and the risk value is obtained and sorted. Specific risk control measures are put forward according to the results of the calculation. The results show that an evaluation method combining an expert system knowledge base and the entropy‐OWA method can effectively depict and process the evaluation information needed for risk rankings and solve the problems of supply chain risks more rapidly and effectively.

Exploration of risks with halal logistics: a case of emerging economy

“…On the other hand, the risk assessment frameworks/model for the cold supply chain is also purposed. For instance, Shen and Qian (2022) proposed an expert system based on entropy-ordered weighted for the risk assessment of the food refrigeration supply chain. Similar to this, Lau et al (2021) proposed a risk evaluation system by integrating federated learning and BWM to measure firm-level cold chain risks.…”

Expert systems supporting strategic decisions

“…In this situation, the strategic potential of expert systems supports decision-making processes in problem definition, information identification, potential alternative development using simulations and forecasting models, alternative analysis using machine learning systems to make more effective decisions, best alternative selection with pervasive systems, decision implementation, and establishment of a control and evaluation system with nested fuzzy models. Decision-support expert systems apply logical models for better decision-making processes (Church et al, 2016;Huber, 1981;Song et al, 2017).…”

“…Shen and Qian (2023) analyse the expert system and entropy‐OWA method in cold supply chain risk evaluation as the existing methods for evaluating cold supply chain risk lack systematic classification and quantitative analysis of the influences of risk elements in the chain. This study builds a knowledge base of expert systems based on expertise and extensive experience and modifies the expert weights in conjunction with entropy weights to reduce subjective error.…”