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AI-Powered Inventory Optimization

Inventory Optimization

Find the perfect balance between cost and service. AI-driven multi-echelon optimization.

Reduce inventory by 20-35% while achieving 99% service levels. Free up working capital and eliminate stockouts.

20-35%
Less Inventory
99%
Service Level
3x
Turnover Rate

Inventory Optimization Configuration

1. Optimization Goals (Multi-select)
Holding Cost
Stockout Cost
Service Level
Working Capital
Fill Rate
Ordering Cost
Obsolescence
Space Usage
Turnover
Carbon Impact
2. Analysis Methods (Multi-select)
ABC Analysis
XYZ Analysis
Multi-Echelon
Safety Stock
EOQ Model
3. Service Level Target
95%
99%
99.9%
4. Parameters (Multi-select)
Demand:
Historical
Forecast
Seasonality
Constraints:
Storage Cap
Budget
MOQ
5. Review Frequency
Daily
Weekly
Monthly
Real-time
SKU-location recommendations ready

Too Much or Too Little. Never Just Right.

Traditional inventory management is a constant struggle. AI optimization finds the perfect balance automatically.

Traditional Approach

  • ×Static safety stock calculations
  • ×Spreadsheet-based planning
  • ×Single-location optimization
  • ×Reactive to stockouts
  • ×Manual ABC classification
  • ×Ignores demand variability

AI-Powered Optimization

  • ✓Dynamic optimization algorithms
  • ✓Automated multi-echelon planning
  • ✓Network-wide optimization
  • ✓Predictive stockout prevention
  • ✓AI-driven segmentation
  • ✓Accounts for all uncertainties

Your Path to Inventory Excellence

Five steps to optimal inventory levels across your supply chain

1

Data Integration

Connect your ERP, WMS, and integrate demand forecasting results

Historical data, demand forecasts from forecasting module, lead times, costs

2

SKU Classification

Automatically classify products using ABC-XYZ analysis

Segment by value, variability, and criticality

3

Model Configuration

Set service level targets and cost parameters

Balance service, cost, and working capital goals

4

AI Optimization

Calculate optimal inventory levels for every SKU-location

Multi-echelon optimization with demand uncertainty

5

Implementation & Monitor

Deploy recommendations and track performance

Real-time KPIs and continuous improvement

Complete Control Over Your Inventory

Customize every aspect of your inventory optimization strategy

Multi-Objective Optimization

Balance competing objectives to match your business priorities

Holding Cost

Stockout Cost

Ordering Cost

Working Capital

Service Level

Fill Rate

Obsolescence Risk

Space Utilization

Inventory Turns

Carbon Footprint

Advanced Analysis Methods

Leverage proven methodologies enhanced with AI

ABC Analysis

Classify items by value contribution

XYZ Analysis

Classify by demand variability

Multi-Echelon

Optimize across supply chain tiers

Safety Stock

Calculate optimal buffer inventory

Economic Order Quantity

Find optimal order sizes

Input Parameters

Comprehensive data inputs for accurate optimization

Demand Input Parameters

  • Historical Demand Data
  • Forecasts from Demand Module
  • Demand Variability Metrics
  • Seasonality Patterns

Supply Parameters

  • Lead Time
  • Lead Time Variability
  • Supplier Reliability
  • MOQ Constraints

Costs Parameters

  • Unit Cost
  • Holding Cost Rate
  • Ordering Cost
  • Stockout Penalty

Constraints Parameters

  • Storage Capacity
  • Budget Limits
  • Service Targets
  • Shelf Life

Planning Systems & Policies

Support for comprehensive inventory planning systems and replenishment policies

MRP (Material Requirements Planning)

Time-phased planning for dependent demand items based on BOM, master schedule, and inventory data

DRP (Distribution Requirements Planning)

Multi-echelon planning that coordinates inventory deployment across the distribution network

MRP II (Manufacturing Resource Planning)

Integrated planning including capacity, finance, and materials with closed-loop feedback

JIT/Kanban Systems

Pull-based replenishment triggered by consumption signals and visual controls

DDMRP (Demand Driven MRP)

Combines MRP with pull-based execution and strategic inventory buffers

Vendor Managed Inventory (VMI)

Supplier manages inventory levels based on agreed service levels and constraints

CPFR (Collaborative Planning)

Joint forecasting and replenishment planning with supply chain partners

Statistical Inventory Optimization

Probabilistic models optimizing safety stock based on demand and supply variability

AI Integration: Our optimization engine works seamlessly with your existing planning systems (MRP, DRP, etc.) and enhances them with AI-driven safety stock optimization and dynamic parameter tuning

Intelligent SKU Segmentation

Not all products are equal. Optimize each based on its unique characteristics.

ABC-XYZ Analysis

9-Box Classification Matrix

Automatically classify thousands of SKUs based on value contribution and demand variability

  • A-Items

    High value, tight control, frequent review

  • X-Items

    Low variability, predictable demand patterns

  • Custom Policies

    Tailored strategies for each segment

Classification Matrix

AX
AY
AZ
BX
BY
BZ
CX
CY
CZ
High priority (AX)
Medium-high priority
Standard priority

Transform Your Inventory Performance

Achieve breakthrough improvements in cost and service

20-35% Inventory Reduction

Less inventory

Optimize stock levels while maintaining or improving service levels

Free Up Working Capital

30% reduction

Release cash tied in excess inventory for strategic investments

99% Service Level

99% OTIF

Achieve near-perfect availability without excessive safety stock

80% Fewer Stockouts

80% reduction

Predict and prevent stockouts before they impact customers

Proven Success Across Industries

See how companies optimize inventory in different scenarios

Multi-Echelon Optimization

Scenario

Optimizing inventory across manufacturing, DCs, and retail locations

Challenges

Complex network, varying lead times, different service requirements

Our Solution

AI-powered multi-echelon optimization considering all interdependencies

Result

30% total inventory reduction, 99% service level achievement

New Product Launch

Scenario

Setting initial inventory levels for products with no sales history

Challenges

No historical data, uncertain demand, risk of stockouts or excess

Our Solution

Use similar product profiles and market intelligence for optimization

Result

85% forecast accuracy, minimal obsolescence in first year

Seasonal Planning

Scenario

Managing inventory for highly seasonal demand patterns

Challenges

Demand spikes, long lead times, storage constraints

Our Solution

Dynamic safety stock adjustments based on seasonal patterns

Result

40% reduction in peak season stockouts, 25% less off-season inventory

SKU Rationalization

Scenario

Identifying and eliminating underperforming SKUs

Challenges

Too many SKUs, low-velocity items, high complexity costs

Our Solution

Data-driven SKU performance analysis and optimization

Result

20% SKU reduction, 15% margin improvement

Measurable Business Impact

Real results from real implementations

35%
Inventory reduction
While improving service
$5M+
Working capital freed
Average per $100M revenue
50%
Faster planning cycles
From weeks to days
3x
Inventory turnover
Improvement in 12 months

Why Choose Lagrange.AI

Advanced capabilities that deliver superior results

Integrated with Demand Forecasting

Seamlessly uses output from our demand forecasting module for accurate inventory optimization

True Multi-Echelon

Optimize inventory across your entire supply chain network simultaneously

Real-Time Optimization

Continuous recalculation as conditions change, not just periodic reviews

Scenario Planning

Test different strategies and see impact before implementation

Ready to Optimize Your Inventory?

Join industry leaders who've reduced inventory by 20-35% while achieving 99% service levels. Get your personalized optimization roadmap.

Free inventory assessment included • Results in 2-4 weeks