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Replenishment driven by demand, not by recurring requisitions.
Forecasting models per item or item-class, safety-stock policy by service level, reorder-point and economic-order-quantity logic, ABC and XYZ classification — driving replenishment without a buyer reading a stock-out report on Monday morning.
Forecast models
Moving average, exponential smoothing, seasonal decomposition — per item or item-class. Operational override available for the cases the model misses.
Safety-stock policy
Service-level driven safety stock, recomputed as demand variability changes. Per-category service-level targets reflect the commercial promise.
Replenishment rules
Reorder point, periodic review, min-max, lot-for-lot — composed by the operations team per item, not hard-coded.
ABC / XYZ classification
Value-driven (ABC) and variability-driven (XYZ) classification, recomputed on the policy cadence, driving review frequency.
What the planning surface covers
Forecast engine
Time-series models per item or item-class. Forecast errors tracked; model performance compared against actuals; model-selection policy auditable.
Safety stock
Service-level driven (cycle service or fill rate) with demand-variability and lead-time-variability factored in. Per-category service targets configured by operations.
Replenishment policies
Reorder-point (continuous review), periodic review, lot-for-lot, min-max — selected per item based on the demand pattern and the supply lead time.
ABC and XYZ
ABC by annual usage value, XYZ by coefficient of variation. Cross-classified 9-cell matrix drives review frequency, safety-stock posture and forecast-method choice.
Planning calendar
Forecast generation cadence, safety-stock recalculation cadence, replenishment-rule review cadence — automated on the operations team's policy.
Exception management
Forecast deviations, stock-out predictions, expiry-near events, over-stock alerts — surfaced on a single planner dashboard with named ownership.
Why planning is an operations decision, not a software decision
A planning module that imposes a single forecast model or a single safety-stock policy on every item is treating planning as a software problem. It is not. Different items have different demand patterns, different supply lead times, different service-level promises. The right model for a fast-moving low-margin item is wrong for a slow-moving high-value spare.
Axional's planning engine is a library of methods configured by the operations team per item or item-class. The engine runs the methods; the operations team owns the policy. The trade-off between stock-out risk and inventory cost is a business decision, not a vendor default.