Automotive · Dealer inventory optimization
Fast variants go on backorder. Slow ones sit for ninety days. Both are inventory problems.
Banao builds demand models that predict what each dealer will sell — by trim, colour, and body style — before the order needs to be placed. Reorder triggers, minimum stock levels, and lot-to-lot transfer recommendations surface inside your dealer management system, where your sales teams already work.
The model trains on each dealer's own transaction history plus regional patterns and registration-cycle signals. Ninety days from now, the right variant will be in the right lot.
The first call is free · 45 minutes · no obligation
What we build
What a dealer inventory AI deployment includes
Inventory turns and aged-stock days are the metrics. Every capability below connects directly to one of them.
Per-dealer demand forecasting
A model trained on each dealer's own transaction history — trim, colour, option pack, finance type — predicts sell-through at that specific location rather than applying a national average to every site.
DMS-native reorder triggers
Reorder recommendations surface inside your dealer management system, not in a separate tool your team has to remember to open. Minimum stock levels and order timing come from the model.
Lot-to-lot transfer recommendations
When an ageing colour sits at one site while the same line is sold out two dealerships over, the model surfaces the transfer before both sites have already re-ordered from the OEM.
Aged-stock early warning
A unit trending toward ninety days on the lot is flagged at thirty, giving sales managers time to reprice, retarget, or transfer before the full discount has to come off the margin.
Seasonal and registration-cycle adjustment
Demand forecasts that account for plate-change cycles, end-of-quarter fleet buys, and local market events — the signals that make a national model systematically wrong for specific regions and dealer profiles.
Receipts
Where this is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Demand models matched trim orders to real sell-through by site
The network ordered on monthly national reports, so slow variants accumulated at high-volume sites while fast trims sold out at smaller ones. Banao built per-dealer demand models wired into the DMS, with automated transfer alerts and reorder triggers. Stock allocation shifted from the national plan to each site's own sell-through pattern.
Dogfooding
We operate a demand-planning system before you do
Banao's own growth team runs on Vikaas, our AI demand-generation product, managing lead flow and pipeline for a ~300-person engineering business. Before we put demand AI in front of a client, it has already had to predict what our own market wants and adjust when it was wrong.
That is not a brochure claim — it is what we mean when we say we dogfood. A model that has to survive our own commercial operation is already hardened before it reaches your dealer network.
Runs Banao's own demand-gen pipeline end to end.
Screens Banao's own engineering hires every week.
The honest version
When dealer inventory AI doesn't pay
Not every network gets the same return from demand modelling. We will tell you the honest case before you spend.
- Thin transaction history: a dealer open less than two years, or one that changed ownership and lost the records, gives the model too little signal for reliable trim-level forecasting. We can still help with regional benchmarking, but the per-dealer model needs data.
- Centralised allocation with no DMS autonomy: if the OEM controls every order and the dealer has no discretion over trim mix, a demand model tells you what to ask for but cannot trigger orders. Integration is only as good as the authority behind it.
- Mid-changeover product lines: a brand that is mid-way through a full model changeover sees demand patterns no historical model can follow. We will say so at the Discovery Sprint rather than after six months of inaccurate forecasts.
How we start
How we start — see the gap before you commit to closing it
Bring your aged-stock report and your last twelve months of sales by trim. We will show you where the model would have called it differently.
- 01
AI Discovery Sprint
2 weeks · fixed price
We analyse your DMS transaction history, map the aged-stock and stockout patterns by site, and hand back a demand-gap report with ROI maths — yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Per-dealer model training, DMS integration, and the aged-stock and transfer alert layer. We connect to your existing dealer management system — older platforms included.
- 03
Production & continuous learning
Deployment with a sales-manager dashboard, reorder workflow, and monthly model retraining on new transaction data. The forecast improves as it sees more of your network's real sell-through.
FAQ
Frequently asked questions
Which dealer management systems do you integrate with?
Banao has integrated with CDK Global, Reynolds & Reynolds, Kerridge, and several regional DMS platforms. If yours is not on the list, week one of any engagement is an integration assessment — we have not yet found a DMS with no export path.
How much transaction history do you need to build a per-dealer model?
Eighteen months of daily transaction data at trim and colour level is enough to get a working model for most dealer profiles. Dealers with fewer sales per month need a longer window; we assess this in the Discovery Sprint before committing to forecast accuracy targets.
Can the model handle dealers that sell multiple brands?
Yes — the model trains per brand per dealer. A multi-brand dealer gets separate demand forecasts for each franchise, and the transfer recommendations stay within each brand's inventory pool.
How do we handle aged stock that has already accumulated?
The Discovery Sprint surfaces your current aged-stock position and the transfer or repricing opportunities that exist today. Clearing the backlog is a separate exercise from building the forward-looking model, and we address both.
What does the sales team actually see?
A dashboard in or beside the DMS showing each site's forecast versus current stock, aged-stock flags at thirty and sixty days, and open transfer recommendations. Sales-team onboarding and change management are non-negotiable parts of the production deliverable — adoption is what makes the model pay.
Get started
Bring your aged-stock report. We will show you where the model would have been different.
In 45 minutes we'll map the demand gaps across your network, show you which sites are over-stocked on the wrong trims, and give you a first estimate of what that costs per quarter.
Book a Discovery Sprint