Transforming Logistics Operations with Intelligent Automation Using a Naive Bayes Perspective on Warehouse Management

Intelligent automation has revolutionized logistics operations, especially warehouse management. This study provides an in-depth investigation into improving inventory management through the use of artificial intelligence using Bayesian inference. Errors and inefficiencies define the current problems with traditional inventory control systems. About naive bayes algorithms, particularly in the field of product management, research knowledge is lacking. For this study, data were gathered from performance evaluation of conventional systems, the Naive Bayes algorithm, and previously existing inventory control systems. The revelations and outcomes are: The negative Bayesian approach applied in product management has the potential to raise performance, accuracy, and cost economy according to research findings. The findings of this work show evidence that the Naive Bayes algorithm is a valuable instrument for handling inventory control issues and greatly add to the increasing corpus of knowledge on supply chain automation.