Retail & E-commerce · Demand forecasting
Banao builds demand forecasting models at the SKU and store level, wired into your replenishment workflow — so purchasing decisions reflect what will sell next week, not what sold last quarter.
We do not hand over a forecast spreadsheet. We ship a system: feature pipeline, model, and an API your buying and planning teams can act on, with visibility into why each number moved.
A forecasting system lives or dies by its data pipeline and its integration into decisions. We build both.
Gradient-boosted and deep-learning models trained on your sales history, seasonality, promotions, and external signals — calibrated to your lead times and minimum order quantities, not generic retail parameters.
Holidays, weather, regional events, price changes, and competitor promotions encoded as model inputs, so the forecast reflects the calendar your customers actually shop on.
Forecast output wired into your purchase orders, warehouse management system, or ERP — so the model output becomes a buying decision rather than a number in a dashboard nobody opens.
Separate treatment for hero SKUs with high volume and frequent orders, and long-tail items with sparse or intermittent demand — because a single model architecture fails at both ends of the catalog.
MAPE, bias, and coverage tracked per SKU cluster and product category, surfaced to the planning team so forecast drift is caught before it becomes a purchasing error.
Buyers can override any forecast line with a reason code — new supplier, catalogue drop, delayed shipment — and those overrides feed back into the model so planning knowledge improves accuracy over time.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once the number is verified.
Myntra
Banao engineers worked inside Myntra pods on systems that sit at the intersection of demand, merchandising, and supply — where a one-point improvement in forecast accuracy at scale directly moves working capital.
Banao runs its own pipeline demand through Vikaas, our AI demand-gen system, before any client sees it. That means we track lead conversion, forecast pipeline volume, and tune our own buying of engineering capacity — the same discipline we apply to your inventory problem.
A forecasting system we are willing to run our own business on is the only one we will put in front of yours.
Forecasting AI earns its keep only under specific conditions. We will tell you before you build if yours do not exist yet:
Most retailers already know stockouts are costing them. The question is which SKU clusters and which lead-time windows are driving it — and whether a model will actually close the gap.
We run your historical sales, returns, and purchase order data through a baseline forecast, quantify the accuracy gap against your current method, and map it to a working capital and margin number. You keep the analysis whether or not you continue. If you do, the Sprint cost is credited against the build.
Feature engineering, model training, accuracy benchmarking per SKU cluster, and integration with your replenishment workflow or ERP. We ship a live system with an accuracy monitoring dashboard, not a model file.
Forecast retrained on rolling actuals, accuracy reviewed monthly against business targets, and a buyer override loop that feeds planning knowledge back into the model each season.
Our spreadsheet forecast works fine for most SKUs. Why build a model?
Spreadsheets work well for stable, high-volume lines where a buyer has years of intuition. They fail on the long tail — seasonal items, new launches, and SKUs with intermittent demand — which is exactly where stockouts or overstock tend to accumulate. A model handles the tail; the buyer keeps control of the hero lines.
How much sales history do we need?
Generally two or more years of weekly sales, ideally at the SKU and store level. Less is workable if you have consistent category-level data we can use for hierarchical modelling. The Discovery Sprint establishes the baseline with what you actually have.
Can this connect to our existing ERP or warehouse system?
Yes. We integrate forecast output with SAP, Oracle, Unicommerce, and custom warehouse systems via API. Integration is part of the build deliverable — a forecast that does not reach a purchase order has no business value.
How do buyers stay in control of the model's recommendations?
Buyers can override any forecast line with a reason code — promotional cover, delayed shipment, new supplier, catalogue drop. Overrides feed back into the model, so planning knowledge improves accuracy over time rather than competing with it.
What accuracy improvement can we expect?
That depends on your current baseline and catalog complexity, which is exactly what the Discovery Sprint quantifies. We will not promise a number before we see your data — but we will give you the lift maths after two weeks, yours to keep either way.
Bring your stockout rate, your tail inventory cost, and your current forecast method. In 45 minutes we will tell you whether a forecasting model will close the gap — and what the accuracy improvement would be worth.
Book a 45-min scoping call
