This document provides a review of data mining methods used in manufacturing and how warehousing impacts manufacturing. It discusses how data mining techniques can be applied to extract patterns from manufacturing data to improve quality, optimize processes, and predict demands. Common data mining methods used include decision trees, neural networks, clustering, and regression. The document also examines the role of warehousing in managing inventory and supporting efficient manufacturing operations. Issues like demand variability, labor costs, and inventory inaccuracies are discussed. Automating warehouse processes through technologies like RFID is presented as a way to improve performance.