Inventory Optimization
Determine optimal stock levels across locations and SKUs, balancing holding costs against stockout risk while meeting service level targets.
NP-hard (multi-echelon)Complexity
Why Inventory Optimization Is Hard
Demand uncertainty
Future demand is stochastic. Any deterministic plan is wrong by definition. The question is how wrong and what it costs.
Multi-echelon networks
Stock at warehouse A affects service levels at warehouses B and C downstream. The network effect makes each location's decision dependent on all others.
Lead time variability
Supplier lead times vary. A shipment arriving 2 days late may cause stockouts across multiple locations. Safety stock must account for this variability.
Real-World Example
What This Looks Like in Practice
Your company holds 5,000 SKUs across 12 warehouses. Holding costs run 25% of inventory value per year. Stockouts cost an estimated 3x the product margin in lost sales and reputation damage. Currently, each warehouse orders independently based on experience and fixed reorder points. Total holding costs: €2.8M per year. Service level: 96.1%, missing the 99% target. kint optimizes allocation across all 12 locations simultaneously, cutting holding costs by 34% while hitting 99.2% service level.
Approach
How kint Solves Inventory Optimization Problems
Demand forecasting with ML
Historical data trains prediction models. Seasonal patterns, trends, and anomalies are captured automatically from your sales history.
Stochastic optimization
kint doesn't optimize for average demand. It optimizes across the distribution of possible demands, accounting for uncertainty explicitly.
Multi-echelon network solve
All 12 warehouses are optimized simultaneously. Stock transfers between locations are part of the solution, not an afterthought.
Rolling re-optimization
As actual demand data comes in, forecasts update and allocation adjusts. Continuous improvement, not one-time planning.
Technical Details
Multi-echelon inventory optimization combines stochastic demand models with deterministic optimization. kint uses mixed-integer programming for network-level decisions and machine learning for demand forecasting, solving the joint problem in an integrated framework.
Impact
What Changes
Holding costs
€2.8M/year
€1.85M/year (-34%)
Service level
96.1%
99.2%
Stockouts
420/month
248/month (-41%)
Safety stock rules
Uniform across SKUs
SKU-specific, demand-driven
Planning effort
Weekly per warehouse
Daily, automated
Example
Input
Optimize stock levels for 5,000 SKUs across 12 warehouses with seasonal demand patterns and variable lead times.
Output
Optimal allocation per SKU and location. 34% lower holding costs with 99.2% service level maintained.
-34%
holding costs
99.2%
service level
-41%
stockouts
Connections
Related Problem Types
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