Practice area
Demand & Sales Forecasting
DataWise builds demand and sales forecasting models on your real order data, grades them against actuals monthly, and wires them into planning.
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DataWise builds demand and sales forecasting systems for manufacturers, importers, and distributors, trained on your real order history and graded openly against actuals every month. Our longest engagement of this kind ran for three years as an embedded monthly service for a US-market industrial manufacturer, feeding directly into leadership planning meetings. We deliver forecasts leadership can plan on, not model demos.
What does DataWise actually build in a forecasting engagement?
DataWise builds multi-horizon forecasting systems: monthly and annual sales forecasts, quarterly product-family demand forecasts, or item-level projections, depending on the decision they need to feed. Under the hood we use ensemble time-series stacks that combine statistical models, gradient-boosted models, and neural architectures, tuned per horizon, because no single model family wins at every horizon on real business data. Around the core forecast we have shipped, in real engagements, customer clustering, dedicated forecasting for mega-customers, stockpiling detection, churn analysis, an explainability layer, and geographic sales analytics. Every engagement starts with a characterization phase that defines the decision the forecast serves, the data available, and the success metric, before any model is trained.
How do you know the forecast is actually working?
This is the question that separates a forecasting service from a slide deck, and DataWise answers it with a ledger. In our flagship engagement, forecast performance was tracked openly against actuals every month for three years, with 3-month and 12-month error tracking in a yearly performance ledger. Predictions were graded, not just made. The same discipline applies to every forecasting engagement: the accuracy metric is defined during characterization, measured on real data before rollout, and reported against actuals on an agreed cycle after it. If the model stops earning its place in the planning meeting, the ledger shows it first.
Planning on experience vs. planning on a graded forecast
| Question | Experience and gut feel | Graded forecasting with DataWise |
|---|---|---|
| Monthly and yearly targets | Set by negotiation and memory | Anchored to a quantitative forecast, reviewed monthly |
| Shifts in customer behavior | Detected late, explained after the fact | Surfaced as they develop, with customer-behavior analytics |
| Customer attrition | Discovered after the customer is gone | Flagged by churn analysis, matched against field intelligence |
| Forecast accuracy | Nobody measures it | Tracked against actuals in an open ledger, 3-month and 12-month horizons |
| Ownership | Knowledge lives in one person’s head | Models, code, and pipelines belong to you |
Which industries and data situations does this fit?
DataWise has built and validated forecasting on real data across manufacturing and distribution contexts: multi-region quarterly demand forecasts for a global water-technology manufacturer across five sales regions (EU, UK, US, Australia, China), unifying direct sales with inter-company stock transfers; a GPU-accelerated commodity-price forecasting engine for feed procurement, validated against real feed-mill purchasing history; and business-cashflow forecasting built directly from raw bank-account transaction history. We have also designed and specified forecasting systems for construction-supply procurement and for sea-freight and currency exposure. The common requirement is not big data, it is real transaction history: order lines, invoices, or bank movements over enough time to learn seasonality.
Proof
Flagship
a three-year data-science engagement for a Mueller-group industrial manufacturer (US market, ~$70M annual revenue); monthly forecasts graded against actuals for three years; CEO recommendation on file.
Showcase cards
multi-region demand forecasting (water technology, five regions), commodity-price forecasting engine for feed procurement, cashflow prediction from raw banking data, three-tier procurement forecasting (designed), freight and currency forecasting (designed and specified).
See selected workIndustry evidence
Danone cut forecast error by about 20% and lost sales by about 30% after moving to ML demand planning, per a ToolsGroup vendor case study, and Walmart reports a single AI-driven inventory system has saved it more than $55 million by aligning store orders to demand, per its 2025 supply-chain briefing. The pattern we take from both: forecasting pays when it is wired into the actual ordering and planning decision.
FAQ
Do we need "big data" for this to work?
No. The engagements above ran on ordinary business data: order lines, ERP exports, bank transactions. What matters is enough history to capture seasonality and a decision the forecast will actually feed. If your data cannot support the decision you want, the characterization phase will say so before you commit to a build.
Can the forecast plug into our ERP?
Yes, that is the normal shape of it. DataWise has designed forecasting as a cloud API integrating into Priority ERP, and builds against the systems you already run rather than asking you to replace them.
What if our demand is dominated by a few large customers?
That is common in Israeli mid-market manufacturing and distribution, and it is handled explicitly: in our flagship engagement we shipped dedicated forecasting for mega-customers alongside the aggregate models, plus stockpiling detection for when large customers buy ahead.