Stock-outs despite high inventory
Aggregate stock looks sufficient, but the wrong items, locations or timing create poor availability.
Inventory and stock optimization
Inventory optimization that balances availability and cash
Inventory optimization is the process of setting stock, reorder and replenishment rules that balance service availability with cash, storage and obsolescence risk. Yoptis makes those trade-offs explicit and tests policies against real demand and lead-time behaviour.
This service is for supply-chain, purchasing, production and commercial teams dealing with stock-outs, excess inventory, unstable forecasts or replenishment parameters that are difficult to explain.
Discuss your constraintInventory problems we help diagnose
Excess and obsolete stock
Buffers, order quantities or forecasts do not reflect current demand, shelf life or substitution.
Planner overrides everywhere
Teams do not trust system parameters and compensate with spreadsheets or informal rules.
Unstable supplier lead times
Replenishment settings use a single average that hides meaningful delay risk.
How inventory optimization works
The objective is not the lowest possible stock or a perfect forecast. It is a policy that produces an acceptable service and risk outcome, can be explained to planners and can be reviewed when conditions change.
| Step | What we do | Output |
|---|---|---|
| 1. Define | Agree the service promise, shortage cost, working-capital constraints and item scope. | Decision criteria and item population |
| 2. Diagnose | Review demand history, stock-out periods, lead times, order rules and data quality. | Data-quality and policy baseline |
| 3. Segment | Group items by demand pattern, criticality, margin, shelf life and supply risk. | Practical policy segments |
| 4. Test | Backtest or simulate reorder points, safety stock and order quantities. | Policy comparison and trade-off table |
| 5. Operate | Pilot selected rules and define ownership, exceptions and review triggers. | Operating policy and monitoring plan |
Typical deliverables
- Inventory and replenishment diagnostic
- SKU segmentation logic
- Safety-stock and reorder-point rules
- Backtest or simulation results
- Exception and planner-override policy
- Parameter review schedule and dashboard specification
Is inventory optimization the right starting point?
Good fit
Transaction and lead-time records are available, the item scope can be defined, and the team can pilot rules on a controlled group.
Not the first fix
Inventory records are materially inaccurate, basic purchasing transactions are missing, or a critical supply interruption requires immediate contingency action.
Inventory optimization questions
What is the difference between inventory optimization and forecasting?
Forecasting estimates future demand. Inventory optimization uses demand, lead time, service targets, costs and constraints to choose replenishment rules. A better forecast can help, but it does not by itself define the correct stock policy.
How do you calculate safety stock?
The method depends on demand and lead-time behaviour. Stable items may use a transparent statistical rule; intermittent or highly variable items often need segmentation and simulation. We match units, remove data artefacts carefully and backtest the result.
Do all SKUs need the same service level?
Usually not. Criticality, margin, substitution, shortage cost and shelf life differ by item. Segmented service policies are easier to explain and prevent low-value items from consuming disproportionate working capital.
Can inventory be optimized with dirty data?
Often, but not by hiding the gaps. We flag stock-out periods, duplicates, promotions and missing lead times, then test how sensitive the policy is to those assumptions. Material record inaccuracies may need correction before optimization.
Related optimization services
Let’s reduce friction
Tell us what you want to improve. We will use your message to assess fit and agree the next step.
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