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Safety stock set against historical variability becomes wrong as demand and lead times shift. A Safety Stock Optimization Playbook reads current variability per SKU and per supplier, target service levels, and inventory cost data to right-size buffers continuously, freeing working capital without breaking service.
The Challenge
When safety stock follows class-level rules, SKUs with low demand variability and consistent lead times hold the same buffer as SKUs with extreme variability. The class average overbuffers the stable SKUs and underbuffers the volatile ones. Working capital sits where it does not need to, service breaks where the buffer was wrong.
Supplier lead time gets stored as a single number. Actual variability swings around that number, sometimes substantially. When the variability widens, safety stock calculated on the original number is no longer adequate; when it narrows, the safety stock is excess. Neither change shows up in standard reports.
Service-level targets get assigned once and rarely revisited. A SKU that started life as a 99% service-level target may have shifted to a less critical role; meanwhile, a SKU that was a B-item may now be a strategic high-touch product. The buffers do not adjust until someone manually intervenes.
How eyko Solves It
A Safety Stock Optimization Playbook reads per-SKU demand variability, supplier lead-time variability, current safety stock levels, service-level targets, and inventory carrying cost to compute optimal buffer per SKU continuously. It surfaces SKUs where the current safety stock is materially off-optimal, sizes the cost impact (excess carry vs stockout risk), and recommends right-sizing moves by SKU and location rather than class-level rules.
The Playbook analyzed 4,200 SKUs across 4 DCs. 840 SKUs carry excess safety stock against the current variability profile, representing $4.2M in tied-up working capital. 184 SKUs carry insufficient safety stock and have produced stockouts that the buffer should have prevented. Right-sizing the top 200 over the next planning cycle captures $2.6M in freed working capital while improving overall fill rate by 1.4 points.
| Metric | Current | Benchmark | Status |
|---|---|---|---|
| Primary indicator | Flagged | Target | Action needed |
| Secondary indicator | Monitoring | Within range | On track |
| Trend direction | Declining | Stable | Review required |
Safety Stock Optimization computes optimal buffer per SKU continuously using current demand variability, supplier lead-time variability, service-level targets, and inventory carrying cost. The Playbook surfaces SKUs where the safety stock is materially off-optimal, sizes the cost impact, and recommends right-sizing moves by SKU and location rather than class-level rules so working capital sits where it is needed and service stays stable.
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FAQ
Everything you need to know about Safety Stock Right-Sizing.
Safety Stock Optimization is an AI-driven continuous computation of optimal buffer per SKU using current demand variability, supplier lead-time variability, service-level targets, and inventory carrying cost. The Playbook surfaces SKUs where the safety stock is materially off-optimal, sizes the cost impact, and recommends right-sizing moves by SKU and location rather than class-level rules.
The Playbook reads from your ERP or planning system (per-SKU demand history, current safety stock levels, planning parameters), supplier records (lead-time history and variability), service-level targets per SKU classification, and inventory carrying cost data. At least 18 months of paired demand-and-leadtime data anchors the variability calculations in real patterns.
Classical safety stock formulas assume variability is stable and parameters are correct. Safety Stock Optimization is continuous and SKU-specific: it tracks variability drift over time and re-optimizes each SKU's buffer when demand or lead-time variability moves materially. The two are complementary, but continuous optimization is what keeps the buffer aligned to current conditions.
Yes. For each SKU the Playbook compares the assigned service-level target against the SKU's current business role (revenue contribution, customer-attachment patterns, criticality). Where the classification has drifted, the Playbook recommends a service-level review with the business owner before adjusting the buffer, so the optimization runs against the right target rather than perpetuating the original assignment.
Join the enterprises replacing weeks of manual analysis with a single prompt. See what eyko Playbooks can do with your data.
