Warehouse efficiency is a critical component in supply chain management, as it directly affects operational costs, service levels, and customer satisfaction. In modern warehouses, data analytics plays a pivotal role in optimizing operations, particularly through techniques such as slotting algorithms and route optimization with heuristics. These techniques leverage large datasets, historical order patterns, and advanced computational methods to improve the speed and accuracy of warehouse processes, ultimately impacting key performance indicators (KPIs) like On-Time In-Full (OTIF) rates. Slotting refers to the strategic arrangement of inventory within a warehouse to minimize handling time and maximize picking efficiency. The core principle of slotting is to position items in locations that reduce travel time, minimize congestion, and accommodate product characteristics such as size, weight, demand frequency, and replenishment cycles. Data analytics enhances slotting decisions by analyzing historical order data, identifying high-velocity items (frequently ordered), and grouping them in easily accessible zones, often near packing or shipping areas. Low-velocity items may be stored in less accessible locations, as their retrieval frequency is lower. Modern slotting algorithms can also consider product compatibility, grouping items often ordered together to reduce multi-item order picking time. Slotting algorithms employ several approaches, including rule-based, heuristic, and optimization-based methods. Rule-based algorithms follow predefined rules, such as storing the fastest-moving items closest to dispatch points. Heuristic methods use practical rules derived from past performance or experiential insights to propose near-optimal arrangements quickly. For instance, a heuristic might group fast-moving items in the front aisles and slow movers in the back, while considering weight and volume constraints. Optimization-based algorithms, often leveraging integer programming or linear programming techniques, aim to minimize total travel distance or maximize throughput, though these require more computational resources. The use of predictive analytics can further refine slotting by anticipating seasonal demand spikes, allowing dynamic adjustments in item locations. Route optimization with heuristics focuses on the path