Overview

Automated Inventory Policies by Logility revolutionizes inventory planning by using an AI-driven probabilistic model that intelligently analyzes demand patterns. The system automatically distinguishes between normally distributed demand and intermittent (lumpy) demand, which traditional methods struggle to handle effectively. By applying advanced statistical techniques, it calculates optimal safety stock levels tailored to each product’s unique demand variability. This approach helps businesses maintain high service levels even for challenging, irregular demand, minimizing costly stockouts and excess inventory. It is particularly valuable for managing service parts and long lifecycle products, ensuring availability while controlling working capital. The solution integrates smoothly into Logility’s broader inventory optimization suite, enabling comprehensive, multi-echelon inventory planning.

Features and Capabilities

• AI-driven demand pattern recognition: Automatically classifies items as either normal or intermittent demand, enabling precise inventory policy adjustment.
• Probabilistic safety stock calculation: Uses statistical models to dynamically set safety stock levels based on actual demand uncertainty, not fixed heuristics.
• Inventory optimization: Balances inventory investment with service goals to reduce excess stock and prevent shortages, particularly for skewed or lumpy demand patterns.
• Data-driven decision support: Provides actionable insights and replaces guesswork with rigorous analytics to improve inventory planning accuracy.
• Service parts availability: Ensures readiness of critical spare parts and slow-moving items, supporting typical product lifecycles of around seven years.
• Integration with Logility InventoryAI+ and MEIO: Enhances multi-echelon inventory optimization across warehouses, distribution centers, and stores for holistic supply chain management.