Inventory Optimization
Safety stock recommendations driven by service-level targets, not intuition
Demand OS calculates safety stock at the SKU and location level using actual demand variance, supplier lead time variance, and your service-level commitments. Every recommendation shows its working capital impact before you commit.
Book a Demo15–30%
Reduction in inventory carrying costs from service-level-driven safety stock
Source: Gartner Supply Chain
20–30%
Safety stock reduction achievable from multi-echelon vs node-by-node optimization
Source: Gartner Supply Chain
SKU-level
Service-level target granularity — set different targets for different product tiers
Source: Perimattic product benchmark
What does inventory optimization AI software do?
Demand OS calculates safety stock at the SKU and location level using actual demand variance, supplier lead time variance, and your service-level commitments. Every recommendation shows its working capital impact before you commit.
The Problem
Why safety stock set by gut feel always costs too much
Safety stock set once and reviewed annually
Targets calculated on last year's demand patterns and lead times become wrong the moment the environment changes. Most teams update safety stock quarterly at best — meaning they carry the wrong buffers for most of the year.
Node-by-node targets that ignore the network
When each DC and branch sets safety stock independently without visibility into upstream buffers, the combined network carries far more inventory than service levels require. The math is right at each node; the system-level total is wrong.
Lead time variance excluded from the calculation
Safety stock formulas that use the average lead time produce targets that are correct on average — which means they fail systematically when actual lead times cluster at the high end. Variance, not average, drives stockout risk.
Service-level commitments with no inventory basis
Target service levels are often negotiated at contract renewal, not calibrated against actual demand patterns and current inventory economics. The result is either chronic over-commitment or safety stock set too high to be profitable.
The Capability
How Inventory Optimization sets targets the network can defend
INVENTORY CAPABILITY
Quarterly-refresh formulas that use average lead time and stale demand variance.
Demand-and-Lead-Time Variance Calculation
Safety stock is calculated using both demand variance (from the AI Forecasting Engine's accuracy output) and lead time variance (from historical supplier performance). Both inputs refresh continuously — not at the next quarterly review.
INVENTORY CAPABILITY
Each DC and branch sets safety stock independently, inflating total network inventory.
Multi-Echelon Network Optimization
Safety stock targets at each DC and branch are calculated jointly, accounting for the upstream buffer position at the central node. Total system inventory is minimized for the same service level target — not just local inventory at each node.
INVENTORY CAPABILITY
Safety stock recommendations with no visible currency impact before commitment.
Working Capital Impact Visibility
Every safety stock recommendation shows its working capital impact in currency terms before a planner commits. Tier-A SKUs get tighter service levels and larger buffers; long-tail SKUs get targets calibrated to profitability, not just fill rate.
In Practice
A wholesale distributor reduces network safety stock by 22% without a service level drop
The Situation
A specialty wholesale distributor with 1 central DC and 8 regional branches had set safety stock independently at each node using average lead times and gut-feel service targets. Total network inventory was $34M. The central DC was consistently over-stocked on the same SKUs that branches were running out of — a clear signal that the node-by-node approach was disconnecting the supply plan from the network reality.
What Demand OS Did
Inventory Optimization calculated safety stock at all 9 nodes jointly using multi-echelon logic. Lead time variance by supplier lane was incorporated from TMS export data. The system identified 340 SKUs where central DC buffer was duplicating branch safety stock and proposed consolidated targets with the working capital saving per SKU visible in the planner workbench.
The Outcome
Network safety stock was reduced by $7.5M (22%) over two planning cycles. Service levels remained flat across all branches. Replenishment recommendations from Demand OS replaced the weekly manual order review, cutting planner preparation time by 60%.
Key Metrics
What inventory teams see in the first quarter
15–30%Reduction in inventory carrying costs from service-level-driven safety stock targets20–30%Safety stock reduction from multi-echelon vs node-by-node optimization22%Network safety stock reduction achieved by a wholesale distributor in two planning cycles
Related
Explore more Demand OS capabilities
Demand OS for Distribution
Multi-echelon safety stock and lead-time-variance-aware replenishment.
Learn moreDemand OS for Pharmaceutical
GxP-compliant audit trails, FEFO inventory, and clinical supply scenario planning.
Learn moreDemand OS vs ToolsGroup
Probabilistic forecasting depth plus explainability and a planner-first workbench.
Learn moreFrequently Asked Questions
Inventory optimization, answered
How does Demand OS calculate safety stock?+
Safety stock is calculated using a service-level-driven formula that incorporates both demand variance (from the AI Forecasting Engine's accuracy output for each SKU) and lead time variance (from historical supplier performance data). The formula produces a safety stock quantity that will maintain the target service level given the observed variability in both demand and supply. Results are refreshed continuously as new demand and lead time data arrives.
What is multi-echelon inventory optimization and why does it matter?+
In a multi-echelon network — a central DC supplying regional branches supplying customers — setting safety stock independently at each node ignores the fact that upstream buffer reduces the demand variability seen by downstream nodes. Multi-echelon optimization calculates targets at every node jointly, so the total network inventory required for a given service level is lower than the sum of independently calculated node targets. For most distribution networks, this produces a 20–30% reduction in total safety stock.
Can different SKUs have different service-level targets?+
Yes. Service-level targets can be set at the SKU, category, customer segment, or location level. A-tier SKUs with high margin or strategic customer commitments can carry a 98% service level; long-tail or low-margin items can be set to 90% or lower. The system calculates the inventory cost of each tier's target and shows the working capital impact of changing a tier's service level before any change is committed.
Does Inventory Optimization connect to ERP for replenishment execution?+
Yes. Replenishment recommendations are exported in formats compatible with SAP MM, Oracle Purchasing, NetSuite, and Microsoft Dynamics 365. Recommendations are surfaced in the planner workbench for review and approval before export — the system does not generate purchase orders autonomously.
How does Inventory Optimization handle FEFO requirements for pharmaceutical or perishable inventory?+
Inventory Optimization supports lot-level inventory tracking with expiration date inputs for pharmaceutical and perishable categories. Replenishment recommendations account for the remaining shelf life of existing batches and flag lots approaching customer-configured expiry thresholds. The system provides planning-level visibility into expiration risk — it does not replace WMS-level FEFO pick logic but ensures planning decisions are made with expiration data visible.
Get Started
Ready to plan with confidence?
Talk to a demand planning specialist and see inventory optimization live on your own data. You leave with accuracy benchmarks, a scoped pilot plan, and published pricing — all in one call.