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Fixed reorder points calibrated months ago drift out of fit with current demand and lead time. A Replenishment Timing Playbook reads per-SKU demand variability, lead-time variability, and service-level targets to continuously tune reorder triggers so each SKU gets replenished at the right moment, not the calendar moment.
The Challenge
A reorder point set at SKU launch reflects the demand and lead time at that moment. Eighteen months later, both have moved. The reorder point still fires on the original assumption and either triggers too early (excess inventory) or too late (stockout risk) for the current reality.
When lead times become more variable, the effective service level at a fixed reorder point degrades silently. Inventory does not change, but stockouts increase because the buffer no longer matches the variability it was sized against. The team sees the symptom without seeing the underlying drift.
Operations sets reorder triggers at the class level because per-SKU tuning is operationally expensive. Within each class, a small number of SKUs have unusual demand or supply patterns and pay a meaningful cost for being tuned to the class average rather than their own pattern.
How eyko Solves It
A Replenishment Timing Playbook reads per-SKU demand variability, supplier lead-time variability, service-level targets, and current inventory position to compute optimal reorder triggers continuously. It surfaces the SKUs where the current trigger is materially off, attributes the gap to demand drift, lead-time drift, or service-level mismatch, and recommends specific reorder-point updates by SKU and location rather than class-level rules.
The Playbook analyzed 4,200 SKUs across 4 DCs and identified 680 SKUs where the current reorder trigger is materially off-optimal. Total cost impact: $1.2M annualized (split across stockout cost, expediting cost, and excess inventory carrying cost). 320 of the 680 are demand-drift cases, 240 are lead-time-drift cases, and 120 are service-level mismatches. Re-tuning the top 200 SKUs captures 70% of the opportunity.
| Metric | Current | Benchmark | Status |
|---|---|---|---|
| Primary indicator | Flagged | Target | Action needed |
| Secondary indicator | Monitoring | Within range | On track |
| Trend direction | Declining | Stable | Review required |
Replenishment Timing computes optimal reorder triggers continuously per SKU and location using demand variability, lead-time variability, service-level targets, and current inventory positions. The Playbook surfaces SKUs where the current trigger is materially off-optimal, attributes the gap to specific drivers (demand drift, lead-time drift, service-level mismatch), and recommends specific reorder-point updates rather than relying on class-level rules that hide individual SKU misfits.
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FAQ
Everything you need to know about Replenishment Timing Optimization.
Replenishment Timing is an AI-driven, continuous tuning of per-SKU reorder triggers using current demand variability, lead-time variability, and service-level targets. The Playbook surfaces SKUs where the current trigger is materially off-optimal, attributes the gap to specific drivers, and recommends specific reorder-point updates rather than relying on class-level rules that hide individual SKU misfits.
The Playbook reads from your ERP or planning system (per-SKU demand history, planning parameters, current reorder points), inventory system (on-hand, in-transit, days of supply), supplier records (lead-time history and reliability), and service-level targets per SKU class. At least 18 months of paired demand-and-leadtime data anchors the trigger optimization.
Classical reorder-point formulas assume stable demand and lead times. Replenishment Timing is continuous and SKU-specific: it tracks parameter drift over time and re-optimizes each SKU's trigger when its underlying demand or lead-time variability moves materially. The two are complementary, but continuous tuning is what keeps the trigger aligned to current conditions rather than the conditions when the formula was last set.
Yes. The Playbook attributes each misfit to its dominant driver: demand drift, lead-time drift, or service-level mismatch. For drift-driven cases the fix is parameter refresh. For service-level mismatches the fix is a conversation with the business owner about whether the target is right for the SKU's actual demand profile. Each case routes to the appropriate owner with the dominant driver named.
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