Industry
Distribution, Import & Retail
Item-level demand forecasting into Priority ERP, safety-stock and purchasing recommendations, and receivables anomaly alerts, all measured by purchasing error.
If you run a distribution or import operation, your money is parked in two places: inventory you bought too much of, and receivables nobody chased in time. AI attacks both, and it only counts if it lands inside the systems you already run. DataWise designs forecasting and anomaly systems that live inside Priority-class ERP workflows: item-level demand forecasts with dynamic safety stock and purchasing recommendations for importers in the $40M to $115M range, with the success KPI defined up front as at least a 10% reduction in purchasing error. Not a dashboard on the side. A number your CFO can audit.
What does AI actually deliver in distribution and retail?
Across retail and CPG, 89% of companies now use or are evaluating AI, up from 82% two years earlier, per NVIDIA’s 2025 survey; the question has shifted from whether to where it pays. The published evidence, with sources labeled honestly:
- Forecasting wired into ordering, not reporting. More Retail, an Indian grocery chain, connected ML demand forecasts directly to automated ordering: forecast accuracy rose from 24% to 76%, fresh wastage fell 30%, and in-stock availability climbed from 80% to 90%, per the AWS case study with the customer named. The lesson is the wiring: the forecast pays when it places the order.
- Volume is not the obstacle. Walgreens forecasts across more than 20,000 SKUs per location and cut over-forecasting from 15% to 1%, per the vendor’s case study. Item-level resolution at real catalog sizes is a solved engineering problem.
- Receivables run on the same discipline. BlueLinx, a US wholesale distributor, cut past-due receivables by 30% and reached 91% automated cash application, per the vendor’s case study with the customer named. Collections is a prediction problem: who is drifting, before the drift becomes a write-off.
None of these are DataWise clients, and we never present them as such. They are the evidence base for what this technology does when it is wired into operations.
What has DataWise actually built for distributors and retail operations?
- Item-level inventory intelligence for ERP environments. Designed and architected item-level demand forecasting with dynamic safety stock and purchasing recommendations, built as a cloud API that integrates into Priority ERP, for importers and distributors in the $40M to $115M revenue range. Success KPI defined in the scope itself: at least 10% reduction in purchasing error.
- Three-tier procurement forecasting for construction supply. Designed a 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.
- Receivables anomaly detection for perishable-goods distribution. Designed for a produce wholesaler serving 1,000+ customers: automatic detection of overdue-payment anomalies with weekly alerting, plus daily “who didn’t order today” monitoring, integrated with the company’s ERP and ordering systems.
- The whole operation, in one system. For an SMB production and events company we scoped every workflow and centralized operations that previously ran across 13 WhatsApp groups and more than a dozen fragmented tools into a monday.com-based work OS: boards and dashboards covering roughly ten operational domains, some 30 automations with their own failure-monitoring board, and two years of spreadsheet history migrated into structured boards. Live and in daily production use for months.
Verb tiers are exact: designed and architected means the build did not proceed past specification; the operations system is live and running today.
Everything we have lives in Priority and Excel. Is that a problem?
It is the normal starting point, and it is precisely why our designs integrate into Priority-class ERP rather than replacing it. Your order history, item master, supplier lead times, and aging report are the raw material. Characterization starts by testing what that data can actually support, and it is allowed to conclude that some of it cannot, yet. A leading, repeatedly-cited barrier for Israeli AI buyers is tools that do not integrate with core systems (Calcalist/CTech); we treat ERP integration as a day-one requirement, not a phase two.
How would we measure this, honestly?
One number per system, defined before any build. For inventory intelligence: reduction in purchasing error, target set at a minimum of 10% against your current baseline. For receivables: past-due volume and time-to-alert. For forecasting: tracked error at defined horizons, graded against actuals on a fixed cycle. If a vendor cannot tell you the single number their system will be judged by, you have learned something important about the system.
FAQ
Our catalog has thousands of SKUs with messy item data. Can forecasting work at item level?
Usually yes, with honest caveats: item-level forecasting works best on items with real sales history, and new or sparse items ride on family-level patterns. Walgreens runs it at 20,000+ SKUs per location. Characterization tells you which slice of your catalog is ready.
We already get a forecast from our ERP module. Why is this different?
Because of what surrounds the model: dynamic safety stock per item, purchasing recommendations in the buyer's workflow, and a defined KPI graded against actuals. A forecast column nobody grades is an opinion.
Can you also cover collections and customer risk?
Yes, it is the same data discipline pointed at receivables: anomaly detection on payment behavior, weekly alerts on drifting customers, and daily monitoring of customers who stopped ordering. Designed exactly this way for a produce wholesaler with over 1,000 active customers.