Work

The Design and Development of a Causal Bayesian Networks Model for the Explanation of Agricultural Supply Chains

Year: 2022

Type: article

Abstract: The balancing of demand and supply in the market is a complex problem because of the dynamic supply chain and environment. It causes uncertain situations and is a limitation in decisions making systems that cannot produce reasonable descriptions to help decision makers eliminate uncertainties. This study proposes the design and development of a Causal Bayesian Networks (CBNs) model for market understanding, which encodes a human-like approach to explain demand and supply events for decision makers. A framework for generating reasonable descriptions in Agricultural Supply Chains (ASCs) management is proposed. The qualitative and quantitative design of the CBNs model is developed and proved that the CBNs model can reasonably explain events using predictive performance measurement and sensitivity analysis for producing reasonable descriptions. The results illustrate that the CBNs model is suitable for ASCs situation explanation involving uncertain situations, and is ready to apply to real-world applications to support decision making systems.

Source: IEEE Access

DOI: 10.1109/access.2022.3199353

Authors: Mallika Kliangkhlao, Somchai Limsiroratana, Bukhoree Sahoh

Institutions: Prince of Songkla University, Walailak University

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,850

Funder: Prince of Songkla University