A Fuzzy Bayesian Network Model for Quality Control in O2O e-Commerce

Abstract

With the popularization of the online to offline (O2O) e-commerce on fresh food products, how to control the quality is becoming increasingly important. To adequately address this problem, this paper presents a fuzzy Bayesian network model for effectively controlling the quality in O2O ecommerce. Reasoning about uncertain events and incomplete data through an intelligent simulation with Bayesian networks provides a convenient and fast method of evaluation and analysis for e-commerce platforms to quickly select fresh food suppliers. Such a model is capable of appropriately modelling the uncertainty inherent in the fresh food product distribution process. It focuses on the identification of the critical factors that affect the food product quality along the supply chain. This leads to the development of a complete selection and evaluation system for the quality in O2O e-commerce. A simulation study is conducted that shows the proposed model is applicable for effectively controlling the quality in O2O e-commerce. Ultimately, the unloading level, warehouse inspection and warehouse monitoring are determined as the entry points for quality control, with corresponding degrees of influence of 44%, 37%, and 34%. The main points to protect the quality of food are introduced, which provides a theoretical basis for solving fresh food safety problems for business platforms.