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

Abstract

The balancing of demand and supply in the market is complex 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 designing and developing 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.

Keywords

• Explainable artificial intelligence
• causal graph
• machine learning
• big data
• demand and supply analysis
• supply chain management