Demand Forecasting
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Procurement & Supply Chain
Demand Forecasting
See next quarter’s demand by SKU, so you buy and build to it.
Forecasts demand by SKU/customer using historical sales, order book, seasonality and customer signals, feeding production planning and procurement so you build and buy to real demand.
20–35%
forecast error reduction
SKU-level
demand visibility
Weekly
refreshed forecast
The impact
Better service levels with less inventory, and a procurement/production plan grounded in data rather than gut.
20–35%
forecast error reduction
SKU-level
demand visibility
Weekly
refreshed forecast
Who it's for
- Manufacturers planning purchase and production on gut feel
- Firms with 18–24 months of sales history in Tally/ERP
- Plants juggling many SKUs with uneven demand
- Sales/planning teams wanting a data baseline to adjust
What you get
- SKU- and customer-level demand forecast with confidence ranges
- Order-book-blended baseline
- Weekly refreshed forecast feeding reorder and planning
- Forecast-vs-actual (MAPE) accuracy reporting
- Override capture for human intel
The pipeline
How it works, end to end.
Every step is built, benchmarked, and wired into your stack. Here is exactly what happens.
Gather signals
It pulls 2–3 years of sales from Tally/ERP, the live order book, and seasonality/event calendars.
Forecast
The model projects demand by SKU and customer, with confidence ranges, not a single fragile number.
Blend with order book
Confirmed orders and known customer plans override the statistical baseline where available.
Feed downstream
Forecasts flow into demand-based reorder and the production planning copilot.
Track accuracy
Each cycle, forecast vs actual is measured and the model is tuned.
Human checkpoint
Sales/planning reviews and adjusts for intel the data can’t see (a big tender, a lost customer).
Gather signals
It pulls 2–3 years of sales from Tally/ERP, the live order book, and seasonality/event calendars.
Forecast
The model projects demand by SKU and customer, with confidence ranges, not a single fragile number.
Blend with order book
Confirmed orders and known customer plans override the statistical baseline where available.
Feed downstream
Forecasts flow into demand-based reorder and the production planning copilot.
Track accuracy
Each cycle, forecast vs actual is measured and the model is tuned.
Human checkpoint
Sales/planning reviews and adjusts for intel the data can’t see (a big tender, a lost customer).
Under the hood
The data flow, wired into your tools.
Reads
- 2–3 years of historical sales
- Live order book and confirmed orders
- Seasonality and event calendars
- Customer-level signals
Produces
- SKU/customer demand forecast
- Confidence ranges per SKU
- Inputs to reorder and production planning
- Accuracy (MAPE) report by SKU family
Before & after
What changes once it ships.
Buying and building to gut feel and last month
SKU-level forecast with confidence ranges, refreshed weekly
Over-stocked on slow SKUs, short on fast movers
20–35% lower forecast error balances stock and service
No way to prove or improve the plan
Forecast-vs-actual measured and tuned every cycle
Why it matters
The business case
Buying and building to gut-feel means a manufacturer is simultaneously over-stocked on slow SKUs (cash dead on the shelf) and short on the fast movers (lost sales, expedited freight). Cutting forecast error by 20–35% at SKU level lets the plant carry less inventory while serving customers better, which can swing both working capital and on-time delivery by meaningful margins. Because the forecast feeds reorder and production planning directly, the gain compounds across the whole supply chain rather than sitting in a spreadsheet.
FAQ
Demand Forecasting: your questions, answered.
How much history do we need for this to work?+
Around 18–24 months of sales gives a solid baseline, but even 12 months plus your order book is useful. Accuracy improves every cycle as more data accrues.
Can our sales team override the forecast?+
Yes, and they should. The model gives a data baseline; your team layers in real-world intel (a new tender, a customer expansion, a discontinued line) and those overrides are tracked for learning.
Does it forecast at SKU level or just totals?+
SKU and customer level, with the ability to roll up to product family or plant. That granularity is what makes it usable for actual buying and scheduling.
How does it handle seasonal and lumpy demand?+
It explicitly models seasonality and known events, and for lumpy B2B demand it leans more on your confirmed order book than on smoothing, which is more honest for project-style orders.
How do you prove it’s accurate?+
Every cycle we report forecast-vs-actual (e.g. MAPE) by SKU family, so you can see exactly where it’s strong and where human judgement still matters.
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