Stockouts are corrupting our demand history
Are recorded sales hiding demand we could not fulfill?
Why This Decision Matters
Stockouts occur when inventory hits zero before the next replenishment arrives, resulting in lost sales, empty shelves, and customer dissatisfaction.
The default reaction to a stockout is to increase the average order quantity. However, stockouts are rarely caused by order size; they are driven by demand variability during the lead time and delays in supplier delivery.
Demand Analytics mathematical optimization with explicit operational constraints.
Operational Model #007
Provides a scientific decision rule to balance trade-offs and eliminate guesswork in Demand Sensing.
Decisions that Govern Execution
#1Safety Stock Level: How much buffer inventory should we maintain?
#2Reorder Point: At what exact inventory level should we trigger a replenishment order?
Execution Sequence for Operators
Track standard deviation of demand and supplier lead times separately.
Formulate safety stock using statistical service factor calculations based on target fill rates (e.g. 95% vs 99%).
Set automated reorder alerts in the ERP when stock crosses the reorder point (demand * lead time + safety stock).
Establish secondary sourcing contracts to buffer high-lead-time items.
Required Telemetry Feeds
| Field | Type | Purpose |
|---|---|---|
| Daily Sales Velocity | Quantity per day | Determines normal consumption rate. |
| Supplier Lead Time Logs | Dates: Placed vs Received | Calculates average and standard deviation of delivery time. |
Diagnostic Scoreboard & Formulas
| Metric | Mathematical Formula | Interpretation |
|---|---|---|
| Service Level (OTIF) | Delivered Orders / Total Placed Orders | Measures customer service compliance. |
| Days of Inventory Cover | On-Hand Inventory / Average Daily Demand | Indicates how long the current stock will last. |
Foundational Literature
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