This article explores how Artificial Intelligence (AI) is drastically changing the logistics industry by transforming operations and significantly improving customer satisfaction. Through meticulous analysis of real-life cases and extensive examination of numerical data, this comprehensive research thoroughly investigates the profound impact of AI on supply chain management. The findings clearly reveal the multifaceted role of AI in significantly reducing costs, greatly improving delivery accuracy, and significantly enhancing inventory control. AI is not just an incremental advancement, but rather a revolutionary leap forward for the future logistics industry, firmly paving the way for unparalleled automation, digital transformation, and operational effectiveness

Machine learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel sub-stantial portions of the data in each iteration, complicating classifier development without suffi-cient data. In critical fields like healthcare, there is a growing need to identify and analyze small yet significant subgroups within data. To address these challenges, we introduce a novel classifier based on the Patient Rule Induction Method (PRIM), a subgroup discovery algorithm. PRIM finds rules by peeling minimal data at each iteration, enabling the discovery of highly relevant regions. Unlike traditional classifiers, PRIM requires experts to select input spaces manually. Our inno-vation transforms PRIM into an interpretable classifier by starting with random input space se-lections for each class, then pruning rules using Metarules, and finally selecting definitive rules for the classifier. Tested against popular algorithms such as Random Forest, Logistic Regression, and XGBoost, our Random PRIM-based Classifier (R-PRIM-Cl) demonstrates comparable robustness, superior interpretability, and the ability to handle categorical and numeric variables. It discovers more rules in certain datasets, making it valuable especially in fields where understanding the model's decision-making process is as important as its predictive accuracy.