Carry less inventory and stock out less — at the same time.
Most manufacturers solve stockouts by adding more safety stock, and solve excess inventory by running promotions. Neither addresses the root cause. Analytics-driven inventory optimization calculates the right stock levels for every SKU at every location based on actual demand variability and service level targets.
Inventory Levels Set by Gut Feel and Fear of Stockouts
- →Safety stock set by a blanket formula or "whatever the buyer thinks is enough" — not by demand variability analysis
- →Reorder points that haven’t been updated since the items were set up in the ERP
- →ABC classification done once and never maintained — C items getting the same attention as A items
- →Excess inventory and stockouts happening simultaneously because different SKUs have different demand patterns
Inventory Optimization Analytics
Safety Stock Optimization
Calculate optimal safety stock for every SKU based on demand variability, lead time variability, and target service level. Replace blanket formulas with item-specific calculations that balance cost and availability.
Reorder Point Calculation
Dynamic reorder points that update as demand patterns and lead times change. No more static reorder points set during ERP implementation that nobody has reviewed since.
ABC/XYZ Classification
Multi-dimensional inventory classification by revenue impact (ABC) and demand predictability (XYZ). Different inventory policies for different segments — high-value/predictable items managed differently than low-value/erratic ones.
Excess & Obsolete Analysis
Identify slow-moving, excess, and obsolete inventory with aging analysis, usage trend tracking, and disposition recommendations. Quantify the carrying cost of dead stock.
Multi-Location Optimization
Optimize inventory placement across warehouses and distribution points. Balance stock where it’s needed based on demand geography, not just where it’s convenient to store.
Service Level Modeling
Model the trade-off between inventory investment and service level. Show leadership exactly what it costs to go from 95% to 98% fill rate — and where the diminishing returns start.
The Engagement Process
Inventory Data Analysis
Analyze current inventory levels, demand patterns, lead times, and service level performance across all SKUs and locations. Identify where investment is misallocated.
Policy Design
Design inventory policies by segment — safety stock formulas, reorder points, review frequencies, and replenishment methods. Align with operations on service level targets.
Optimization Modeling
Run optimization models to calculate target inventory levels. Compare current vs. optimized inventory investment and projected service level impact.
Implementation & Monitoring
Update ERP planning parameters with optimized values. Deploy monitoring dashboards tracking inventory turns, service levels, and excess stock. Monthly reviews to maintain optimization.
Frequently Asked Questions
Data Analytics · Inventory
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