See the inventory decisions your ERP can’t prioritize.
We work from your sales, inventory, purchase-order and supplier history to find where cash is stuck, where stockout risk is building, and which replenishment rules deserve a second look.
When the numbers and the warehouse stop agreeing.
Most inventory problems are not “no data” problems. They are rule, uncertainty and attention problems buried inside years of transactions.
Inventory keeps climbing
Purchasing stays cautious, reorder settings age quietly, and cash accumulates in items that no longer move the way they used to.
The same SKUs still go short
Total inventory looks healthy, but the items customers actually ask for keep landing on the exception list.
Reorder points became folklore
“That’s what we’ve always used” is common. It is also a good reason to compare the rule with current demand and lead-time behavior.
Supplier averages hide bad weeks
An average 12-day lead time is not very helpful if the real range is 6 to 31 days and those long tails drive the stockouts.
Buyers are triaging spreadsheets
Thousands of SKU-location combinations make it hard to know which five decisions deserve a human’s attention this morning.
Cash gets committed too early
The useful question is not “can we buy more?” It is “where does the next inventory dollar protect the most revenue or service?”
Forecasting is only useful when it changes the order decision.
A practical replenishment model combines demand, lead-time uncertainty and the cost of being wrong. The output should be something a buyer can act on.
Illustrative only. Real policies are fitted to your data, costs, constraints and service goals.
Data in. Decisions out.
The analysis can be sophisticated underneath. The operating output should be simple enough to use before the first cup of coffee is gone.
Less inventory noise. More useful operating focus.
The target is not a prettier dashboard or a blanket inventory cut. It is a better allocation of cash, service and buyer attention.
Find slow and excess stock without assuming every SKU should be cut by the same percentage.
Spot items where current stock is thin relative to demand and actual supplier behavior.
Rank exceptions by consequence so people spend time where judgment is worth the most.
Replay a candidate policy against history or simulated conditions before changing the operation.
Start small enough to prove the opportunity.
An assessment is meant to answer a simple commercial question first: is there enough operational value in the data to justify deeper work?
Inventory Optimization Assessment
- SKU segmentation and variability
- Excess / slow inventory review
- Lead-time and reorder-point review
- Prioritized action plan
Forecasting & Optimization
- SKU-level forecasting
- Safety stock / reorder policies
- Scenario simulation and backtesting
- Custom decision-support models
Inventory Intelligence
- Forecast refreshes
- High-value exceptions
- Risk and working-capital tracking
- Periodic model recalibration
Inventory-heavy businesses where complexity outruns the spreadsheet.
Especially industrial distributors, wholesalers and manufacturers with meaningful inventory, uneven demand and buyers making dozens of judgment calls every week.
Industrial distribution
Large catalogs, supplier variation, branch inventory and constant replenishment decisions.
See where it fits →Manufacturing
Raw materials, components and finished goods competing for cash under uncertain demand.
See where it fits →Multi-location supply
One branch is long, another is short, and a transfer may beat another purchase order.
See where it fits →15,000 item-location combinations. Maybe 12 decisions worth your morning.
A useful decision layer does not generate another wall of alerts. It ranks the situations where money, service or supplier risk has actually moved enough to deserve a person.
If the inventory feels wrong, we can usually test why.
CSV, Excel, ERP exports or database extracts are enough to begin a scoped assessment.