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.

A Knowledge Graph Construction Approach for Legal Domain

“…Zhang et al [11] regarded relationship extraction as a clustering problem on shallow parse trees and proposed a tree-similarity-based approach to extract relationships among name instances from a large raw corpus. Zhang et al [12] present a fuzzy Bayesian network model to analyze the relationships between unloading level, warehouse inspection, warehouse monitoring and the quality in O2O ecommerce. Wang et al [13] introduced the coclustering theory on the basis of k-means, not only clustering entity pairs but also relationship characteristics to make good use of the duality of datasets.…”

Quality-controlled logistics with internet of things: a conceptual framework

“…Both the logistic-curve and logistic-regression models are so-call parametric models in which all the parameters of interest are in the finite-dimensional parameter spaces. With the development of big data technologies, new non-parametric machine-learning models with higher degrees of freedom, for example, Bayesian Network (e.g., Zhang et al, 2020) and Neural Networks (Chen et al, 2001;Singh et al, 2009;Soltani et al, 2015;Wang et al, 2017) are thriving in the domain of food quality prediction. However, the big data models have not yet been able to replace the traditional parametric quality-decay models governed by pre-specified mathematical functions due to the reasons such as intensive data requirement, and longer computational time.…”

Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review

“…The most used technique is FL (5 articles). Articles [27], [29], [31]- [33] apply the FL technique to set the level of importance or weight of the criteria for supplier evaluation and selection. This allows them to incorporate uncertainty in the criteria to improve efficiency in the selection of suppliers.…”

“…BN and PSO have been used in two articles. Studies using BN techniques [30], [33] organize the criteria for evaluating and selecting suppliers into a set of variables and the inter-dependency relationships between them. This technique allows us to establish the probability of unknown variables based on known ones.…”

“…Considering the approaches to model risk and uncertainty for supplier selection, seven papers employ fuzzy set theory to incorporate inherent uncertainty into the decision model [26], [27], [29]- [33]. Within these papers, techniques such as fuzzy analytical hierarchy process, fuzzy multi-objective mathematical programming [27] or fuzzy DEMATEL [32] are used.…”