Predictive Stock Management

Demand-Driven Material Requirements Planning Demand Signal Analysis AI analyzes consumption patterns across multiple time horizons: daily (for fast-movers), weekly (for standard items), and monthly (for slow-movers). Identifies: seasonal cycles, trend slopes, and demand volatility (coefficient of variation). Dynamic Safety Stock Calculation Instead of static safety stock, AI calculates dynamic buffers using: demand variability × lead time variability × desired service level (95%/99%). Adjusts weekly based on actual consumption vs. forecast accuracy.

Vendor Lead-Time Intelligence Track actual vs. quoted lead times per vendor. AI calculates: average lead time, lead time variability (σ), on-time delivery rate, and quality acceptance rate. These feed directly into reorder point calculations. ABC-XYZ Classification AI classifies inventory: ABC (by spend value: A=80% of spend, B=15%, C=5%) crossed with XYZ (by demand predictability: X=stable, Y=variable, Z=erratic). Different replenishment strategies for each cell: AX items get just-in-time, CZ items get min-max. Cash Impact Modeling AI calculates the

working capital impact of every reorder decision: carrying cost (25-30% of inventory value annually), stockout cost (lost production revenue per hour), and order cost (setup, shipping, receiving). Optimizes the Economic Order Quantity (EOQ) per item. Track 'Inventory Turns' per item category — AI should flag any item with < 2 turns/year for potential obsolescence review. Implement 'Vendor Managed Inventory' for A-class items — share consumption data with suppliers and let AI auto-generate POs based on agreed min-max levels. Run a 'Dead Stock Audit'

quarterly — AI identifies items with zero movement for 180+ days and calculates the carrying cost wasted. Don't use average consumption for reorder calculations — averages hide variability. A part with 10 units average but ±50% variation needs a very different buffer than one with ±5% variation. Don't order based on 'gut feeling' or 'we've always ordered 500' — AI should provide data-driven recommended quantities every time. Don't ignore the carrying cost — every dollar of excess inventory costs $0.25-$0.30 per year in storage, insurance, obsolescence,

and opportunity cost. The 'Demand Sensing' Upgrade Beyond historical consumption, feed AI with forward-looking demand signals: open production orders, approved maintenance work orders, seasonal project schedules, and customer forecasts. This 'demand sensing' approach reduces safety stock requirements by 20-30% while improving service levels because the system anticipates demand before it hits.