Practice area
Per-Unit Intelligence
DataWise designs item-level demand forecasting, dynamic safety stock, and purchasing recommendations that integrate with Priority and your ERP.
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DataWise brings forecasting down to the level where purchasing actually happens: the individual item. For importers, distributors, and manufacturers we design item-level demand forecasting with dynamic safety stock and purchasing recommendations, built to integrate with the ERP you already run, Priority included. The goal is a measurable reduction in over-purchasing and dead stock, defined as a KPI before the work starts.
What is item-level intelligence and why does aggregate forecasting miss it?
A company-level sales forecast can be right while the warehouse is still wrong: the aggregate holds, but item A is out of stock and item B fills a shelf for two years. Purchasing decisions are made per item, per order date, per supplier lead time, so the intelligence has to live at that resolution too. DataWise designs systems that forecast demand at item resolution, compute dynamic safety stock per item instead of a flat months-of-cover rule, and turn both into concrete purchasing recommendations: what to order, how much, and when. In our reference design for importers and distributors in the $40M to $115M revenue range, the success KPI was defined as at least a 10% reduction in “purchasing error”, the gap between what was bought and what was actually needed.
How does this connect to Priority or our ERP?
As an API, not as a replacement. DataWise architected its item-level system as a cloud API integrating into Priority ERP: the ERP keeps its role as the system of record, and the forecasting layer reads sales and stock history from it and returns recommendations into the purchasing workflow your team already uses. The same shape applies to other ERPs. We have also designed a three-tier progressive-accuracy system for a US construction-procurement SaaS: sales forecast, then supplier lead-time forecast, then dynamic reservation of safety stock, with ERP-integrated automation. Integration with the systems you already run is a design requirement from day one, because a recommendation that lives outside the buyer’s screen does not change any order.
What per-unit decisions can be improved?
- Order quantity and timing per item, from item-level demand forecasts instead of last year plus a percentage.
- Safety stock per item, dynamic and demand-driven, instead of one blanket coverage rule for the whole catalog.
- Buy-timing for commodity inputs: DataWise built a GPU-accelerated forecasting engine for agricultural commodity prices (wheat, corn, oils), validated against real feed-mill purchasing history, aimed at optimal buy-timing and stockpiling decisions.
- Import-cost exposure: designed and specified forecasting of sea-freight prices and exchange rates as procurement decision support for a plastics manufacturer.
- Supplier lead-time risk, forecast as its own layer so reservations reflect reality rather than the catalog’s promised dates.
Proof
Flagship connection
in our three-year forecasting engagement for a Mueller-group industrial manufacturer, forecasts fed inventory commitments and leadership planning, graded monthly against actuals for three years.
Showcase cards
item-level inventory intelligence for ERP environments (designed and specified, Priority integration, ≥10% purchasing-error KPI), three-tier procurement forecasting for construction supply (designed), commodity-price forecasting engine (built and validated), freight and currency forecasting (designed and specified).
See selected workIndustry evidence
More Retail (India, grocery) connected ML demand forecasting to automated ordering and moved forecast accuracy from 24% to 76%, cut fresh wastage by 30%, and lifted in-stock rates from 80% to 90%, per an AWS vendor case study. The lesson: the payoff arrives when the forecast drives the order, not a report.
FAQ
Our catalog has thousands of items. Does this scale?
Item-level forecasting is designed exactly for that: the system forecasts every item programmatically and ranks where the money is, so buyers spend attention on the items where the model flags risk or opportunity, not on scrolling reports.
What is "purchasing error" and why make it the KPI?
It is the gap between what you bought and what you actually needed, visible later as dead stock on one side and stock-outs on the other. We use it because it is measured in your own ERP data, before and after, with no room for storytelling. Our reference design set the bar at a reduction of at least 10% from the current state.
We already have min/max rules in the ERP. Isn't that enough?
Static rules assume demand is stable per item. Where it is, they are fine and we will say so. Where demand moves with seasonality, customer concentration, or price, dynamic per-item forecasting is what keeps safety stock honest. The characterization phase quantifies which of your items are which.