Optimizing Distribution Efficiency and Reducing Operational Waste Using Bayesian Network Models
Abstract
This study addresses the complexity of managing supply chain inefficiencies and product waste in decentralized distribution systems by leveraging Bayesian Network Models. Focusing on a North American CPG manufacturer, the research model’s operational data involves distribution partners to identify latent drivers of obsolete products, delivery delays, and potential fraud. Traditional analytics often fall short in such environments due to sparse ground-truth data and the interdependent nature of logistical, behavioral, and environmental factors.
Using BayesiaLab, we adopt a multi-phase methodology beginning with exploratory analysis, followed by the construction of an unsupervised Bayesian network to uncover hidden structures in the data. The resulting network identifies conditional dependencies among distribution performance indicators, geography, delivery windows, and observed stale rates. This baseline model is refined with business knowledge and expert elicitation, enabling a semi-supervised probabilistic structure that can serve as a diagnostic tool and an early warning system. Additionally, we explore using Generative AI to translate complex probabilistic outputs into interpretable business narratives, enhancing communication and adoption among operational stakeholders. Our findings demonstrate the model’s ability to quantify causal relationships and simulate interventions, offering actionable strategies to improve freshness, reduce internal wastage, and detect anomalies linked to distribution inefficiencies.
About the Presenter
Anand has over 12 years of experience in applied artificial intelligence and data sciences. He is known for creating market solutions based on Bayesian Network theory, which can quantify causality in observational studies. His work and research areas include Knowledge Modelling and Machine Learning with BayesiaLab. Anand has a background in applied statistics and a keen interest in machine reasoning, causal inference, and experimental design.