01 · STOCHASTIC SIMULATION
Simulate the uncertainty that stock must absorb.
Use Monte Carlo-style demand and replenishment samples to propagate inventory over time and compare candidate safety-stock or coverage policies under realistic variability.
Inventory Optimization
Put safety stock where its economic value is highest.
Use stochastic simulation to balance service, lost EBITDA, working capital, storage and writeoff risk for every product-location combination.
Replace blanket service targets with an economic policy.
The same service level should not be imposed on every A, B or C item when variability, replenishment and financial exposure differ by product and distribution center.
02 · FINANCIAL OBJECTIVE
Price both excess and shortage.
Evaluate lost sales in contribution margin or EBITDA together with cost of capital, storage, writeoff, loss and obsolescence. The financially optimal point reflects the real asymmetry between carrying one more unit and missing one sale.
03 · PRODUCT × LOCATION DETAIL
Respect every local demand and supply profile.
Set policy at the material-location level using its sales variability, replenishment distribution, lead time, cost and economic exposure instead of inheriting a single target from an ABC class.
04 · DECISION DIALOGUE
Move from defending a number to presenting a trade-off.
Show Finance the working-capital and writeoff consequence, and Commercial the service and lost-margin consequence. The policy becomes an explicit business choice supported by sensitivity curves.
Policy connected to execution.
The selected coverage or safety-stock target feeds the same material-location structures consumed by supply and distribution planning.
- Candidate policies compared through forward inventory propagation.
- Service, shortage, capital and aging outcomes evaluated in the same simulation.
- Local policy differentiated by demand, supply and economic behavior.
- Scenario curves that expose the cost of moving toward more or less service.
Operational stock policy in Community. Financial optimization in Pro/Enterprise.
The numbered rows show how advanced editions move from executing a stock target to choosing it through stochastic, product-location and economic analysis.
| Capability | Community | Pro / Enterprise |
|---|---|---|
| Material-location stock policies in quantity or coverage | Included | Included, with managed governance and scale. |
| Safety stock consumed by heuristic supply planning | Included | Included. |
| Point 09AStochastic / Monte Carlo policy simulation | Not included | Pro / Enterprise Generate demand and replenishment samples, propagate inventory through time and compare candidate safety-stock or coverage policies. |
| Point 09BFinancially optimal safety stock | Not included | Pro / Enterprise Balance lost sales measured in contribution margin or EBITDA against cost of capital, storage, writeoff, loss and obsolescence. |
| Point 09CProduct-location policy instead of blanket ABC targets | Policies can be stored by material and location, using planner-defined targets. | Pro / Enterprise Optimize each product-location combination from its own sales variability, replenishment behavior, lead time, cost and economic exposure. |
| Financial sensitivity curves for service decisions | Not included | Pro / Enterprise Present Finance and Commercial with the economic consequence of moving toward more or less service. |
| Advanced aging and obsolescence scenarios | Not included | Enterprise For relevant product configurations. |