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EOQ formulas set order quantities at a moment in time and assume the underlying parameters stay constant. They never do. An Order Quantity Optimization Playbook reads current demand, lead time, holding cost, and ordering cost data to continuously tune order quantities per SKU to actual conditions.
The Challenge
Classical EOQ assumes demand is steady and lead times are constant. In practice both move. The order quantity that was optimal six months ago is wrong today, but the planning system keeps using the original parameter and the order behavior compounds the misfit.
Carrier rate moves change holding cost (capital tied up in transit). Supplier MOQ changes change ordering cost. Warehouse cost-per-pallet changes change holding cost. None of these flow back into the EOQ calculation in real time, so the optimization is anchored to outdated assumptions.
When EOQ runs as a class-level rule (all A-items use one formula, all B-items use another), the SKUs whose demand or supply behavior is unusual get the wrong order quantity. A few specific SKUs drive most of the inventory and ordering waste, but the class-level rule hides them.
How eyko Solves It
An Order Quantity Optimization Playbook reads per-SKU demand variability, supplier lead-time variability, current holding and ordering cost components, and supplier MOQ constraints to compute optimal order quantity per SKU continuously. It surfaces the SKUs where the current order quantity is materially off, sizes the total cost impact (inventory carrying cost + ordering cost + stockout cost), and recommends adjustments by SKU and supplier rather than by class-level rule.
The Playbook analyzed 4,200 SKUs and identified 480 where the current order quantity is materially off-optimal. Total annualized cost impact: $1.8M (split across inventory carrying cost, ordering cost, and stockout cost). The top 50 SKUs alone account for $640K. 84% of the misfit traces to demand or lead-time parameters that have drifted since the EOQ was set; only 16% reflects MOQ constraints that cannot be changed.
| Metric | Current | Benchmark | Status |
|---|---|---|---|
| Primary indicator | Flagged | Target | Action needed |
| Secondary indicator | Monitoring | Within range | On track |
| Trend direction | Declining | Stable | Review required |
Order Quantity Optimization computes optimal per-SKU order quantity continuously using demand variability, lead-time variability, current holding and ordering cost components, and supplier constraints. The Playbook surfaces SKUs where the current order quantity is materially off, sizes total cost impact across carrying cost, ordering cost, and stockout cost, and recommends adjustments by SKU and supplier rather than the class-level rules that hide individual SKU misfits.
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FAQ
Everything you need to know about Order Quantity Optimization.
Order Quantity Optimization is an AI-driven, continuous tuning of per-SKU order quantity using current demand variability, lead-time variability, holding cost, ordering cost, and supplier constraints. The Playbook surfaces SKUs where the current order quantity is materially off-optimal, sizes total cost impact across carrying cost, ordering cost, and stockout cost, and recommends adjustments by SKU and supplier rather than the class-level rules that hide individual misfits.
The Playbook reads from your ERP or planning system (per-SKU demand history, lead-time history, planning parameters), inventory system (on-hand, in-transit, days of supply), supplier records (MOQ constraints, lead-time reliability), and cost components (carrier rates, warehouse cost-per-pallet, transactional ordering cost). At least 18 months of paired ordering-and-cost data anchors the optimization.
Classical EOQ assumes stable demand and constant parameters. Order Quantity Optimization is continuous and SKU-specific: it tracks parameter drift over time and re-optimizes each SKU when its underlying demand, lead time, or cost components move materially. The two are complementary, but continuous tuning is what keeps the order behavior aligned to current conditions rather than the conditions when the formula was last set.
Yes. The Playbook distinguishes SKUs whose misfit is fixable through parameter refresh from SKUs whose order quantity is locked by a supplier MOQ constraint. For the constrained subset it surfaces the projected savings if the MOQ were reduced, so procurement can prioritize the supplier conversations most worth having and the negotiation lands on quantified business impact rather than general dissatisfaction with the constraint.
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