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Logistics & Supply Chain · Demand planning

Your forecast is wrong before the warehouse acts on it

Most distribution operations plan demand from spreadsheets that are already a week stale when ops reviews them. The result is too much stock in the wrong depot, stockouts on fast movers, and expedited freight filling the gap.

Banao builds demand-planning AI that runs against live ERP and point-of-sale data, accounts for seasonal patterns and promotions your spreadsheet ignores, and feeds replenishment orders directly into your purchasing workflow.

Indian Oil— demand forecasting models deployed across a national fuel distribution network.

What a Banao demand-planning deployment includes

A demand-planning deployment is not a standalone forecast model. It is the model, the data pipeline, the ERP integration, and the planner workflow — all four are deliverables.

Multi-echelon demand forecasting

Models that forecast at SKU, depot, and regional level simultaneously, so the national plan and the depot replenishment order agree and stock doesn't accumulate in the wrong node.

Seasonal and promotion-aware planning

Explicit modelling of seasonal curves, promotional uplifts, and calendar events that a trailing average flattens out. The model accounts for the next Diwali or Ramadan before your planners are briefed on it.

Replenishment automation

Replenishment orders generated from the forecast and pushed into your purchasing or TMS workflow, with planner override on every line — not a closed system that locks ops out of decisions.

ERP and WMS integration

We connect to SAP, Oracle, or legacy ERP and pull live inventory positions, goods receipts, and order history. The pipeline cleans and validates the data before the model sees it.

Exception-based planning alerts

A dashboard that surfaces only what needs a planner's attention — a depot heading for a stockout, a slow-mover building dead stock, a supplier delay about to hit service levels — rather than a wall of rows to monitor.

New product introduction forecasting

Statistical priors and analogous-product history to bootstrap a forecast for products without a sales record, so a new SKU doesn't get stocked on gut feel.

Where this is already deployed

Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.

Indian Oil

Demand forecasting across a national fuel distribution network

  • ··%forecast accuracy improvement
  • ··%stockout reduction at depots
  • ··%reduction in excess inventory

Fuel demand across a national depot network is shaped by monsoon patterns, agricultural cycles, and industrial load — none of which a 13-week trailing average captures well. Banao applies forecasting models over historical demand, depot inventory positions, and seasonal signals, feeding output into fleet-routing and procurement workflows.

We run our own planning on the AI we sell

Banao operates a ~300-person engineering company on its own AI products. Vikaas runs our demand generation — pipeline forecasting, lead scoring, and outreach sequencing — so our revenue planners work from a model, not a spreadsheet.

That is the standard we hold a client's demand-planning system to: it has to survive our own planners before we put it in front of yours.

  • VikaasForecasts Banao's own pipeline and runs demand generation end to end.
  • InterviewGodScreens every Banao engineering hire before the shortlist reaches a human.

When demand-planning AI is the wrong investment

Better forecasting only pays where forecast error has a measurable cost. We will tell you when it doesn't fit:

  • Thin SKU count with stable demand: below a few hundred SKUs in a predictable category, a good planner with a spreadsheet is cheaper than a model. We'll say so.
  • No transactional history: if your ERP has less than 12 months of clean demand data, week one is data engineering, not modelling — and the honest answer may be to instrument first and forecast later.
  • Forecasts that don't connect to action: a model that feeds a report nobody acts on adds no value, however accurate. We scope the replenishment integration before we scope the model.

How we start — prove the cost before building the model

We don't scope a demand-planning build without first establishing what a percentage-point improvement in forecast accuracy is worth to your operation.

  1. AI Discovery Sprint2 weeks · fixed price

    We audit your ERP data quality, SKU velocity profile, and current planning workflow, then hand back a forecast-error baseline, an ROI model per use case, and a go/no-go — yours to keep either way. If you proceed, the Sprint cost is credited against the build.

  2. Build

    Data pipeline first — cleaning, validation, and ERP/WMS integration. Then model training against your actual SKU and depot history, with planner override on every replenishment suggestion the system produces.

  3. Production & continuous improvement

    Deployment with a planner dashboard and exception alerts, plus change management for your planning team. The model retrains on new actuals weekly, so accuracy improves after go-live rather than decaying.

Frequently asked questions

Yes — almost every engagement starts with messy data. We run a data audit in week one and build a cleaning and validation pipeline as a deliverable, not a prerequisite. The model is only as good as what goes in, so data engineering is part of the build scope.

SAP's planning modules apply rules and configuration. They struggle with lumpy, seasonal, or promotional demand because they don't learn from patterns. A machine-learning forecasting model runs on top of your SAP data, updates with each week's actuals, and doesn't require changes to your ERP configuration.

Every replenishment suggestion carries a planner override. The system surfaces exceptions — not a wall of orders — so planners confirm, adjust, or reject. Planner adoption is where demand-planning AI usually fails; we design the workflow around the planner first, the model second.

We use statistical priors and analogous-product history to bootstrap a forecast for new SKUs. The model flags the uncertainty explicitly rather than producing a confident-looking number, and it updates as first-week actuals arrive.

The AI Discovery Sprint produces exactly that — a forecast-error baseline, an inventory carrying-cost model, and per-use-case ROI estimates. Fixed price, two weeks, yours to keep regardless of whether you continue. Worst case, a free assessment; best case, the numbers your finance team needs.

Find out what your forecast error is costing

Bring your ERP data profile and your worst-performing SKU category. In 45 minutes we will map the forecast error, the carrying cost, and what it would take to close the gap.

Book a 45-min scoping call