Food & Beverage · Waste reduction analytics
You know waste is costing you — you just can't say exactly where
Spoilage, over-prep, and trim loss accumulate across every shift, but they rarely show up as a named line on the P&L. Banao builds waste analytics that traces each source — by product, line, station, and day — so the number becomes visible and the fix becomes specific.
The system pulls from your production data, sales, POS, and manual count logs into a single model that ranks waste sources by cost. Kitchen and operations managers get a view they can act on, not a data dump to interpret.
The first call is free · 45 minutes · no obligation
What we build
What the Banao waste analytics build includes
Waste surfaces at different points in a food operation. We model each one rather than treating waste as a single bucket.
Waste source attribution by line and shift
We break total waste down into named sources — over-prep by station, trim loss by cut, expiry by SKU, production off-spec by batch — so the operations team knows which source to address first, not just how much was lost.
Spoilage and expiry prediction
Time-series models over stock age, temperature logs, and sales velocity predict which items will expire unsold and by when — so the kitchen can sell down or reroute before the bin receives it.
Over-prep modeling against demand forecasts
Daily and daypart prep quantities driven by forecast demand rather than last week's habit. The model accounts for day of week, local events, and outlet-level sales patterns — not a single average across all sites.
Yield tracking against production targets
Actual yield versus standard yield by recipe and batch, flagged when a variance crosses the cost threshold. Plant and kitchen managers see the gap as a cost figure, not a percentage on a static report.
Procurement alignment with waste data
Purchase quantities adjusted by waste-pattern feedback, so over-ordering of high-waste items shrinks over time without requiring a manual audit cycle to trigger each change.
Manager alerts and corrective action tracking
Threshold-based alerts when a source is running above its cost target, with a corrective-action log that records what was done and whether waste came down. Closes the feedback loop between the data and the floor.
Receipts
Where waste analytics is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Waste and over-prep modelled across thousands of kitchens
Swiggy's cloud-kitchen and delivery operation spans thousands of kitchens and shifts. Banao built demand and waste models that give each kitchen a daily prep target and flag which categories are running above their waste budget.
Dogfooding
We run on data-driven operations before you do
Banao manages a ~300-person engineering company on its own AI products. Vikaas runs our demand-gen pipeline — every outbound campaign, lead scoring, and follow-up cadence is driven by the same kind of source-attribution model we build for food operations.
A system that has to perform inside our own business is already stress-tested before it reaches your kitchen or production line. We know what breaks when data is inconsistent, and we build for that.
Runs Banao's demand-gen pipeline — source attribution and campaign timing from live data.
Screens Banao's own engineering hires on structured data, not gut feel.
The honest version
When waste analytics is the wrong starting point
Waste analytics requires data that is already being collected. If the foundation is missing, we will say so before you build:
- No production or sales data: if prep quantities, sales counts, and waste weights are not logged digitally, the first month is data infrastructure, not modelling. We will scope that clearly.
- Single-site, low volume: below a few hundred covers or batches a day, the pattern is too thin to model reliably. A manual prep-count system often returns more per hour spent.
- Unstable menus or recipes: if the menu changes weekly, the model's yield baselines expire with it. We will tell you whether the change rate makes analytics viable before you commit.
How we start
How we start — see your waste data before you build on it
We do not quote a waste analytics system without looking at your actual data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your existing production, sales, and waste logs, identify the largest attributable waste sources, and hand back a ranked cost estimate and a build scope — yours to keep. If you proceed, the Sprint credits against the build.
- 02
Build
Model development, POS and production data integration, yield baseline calibration, and a manager-facing dashboard. Data pipeline and alert configuration are part of the deliverable.
- 03
Production & continuous improvement
Live deployment with weekly waste reviews, threshold refinement as operations change, and a corrective-action loop that closes the gap between the data and what the floor actually does.
FAQ
Frequently asked questions
What data do you need to start a waste analytics build?
Production records, sales or POS data, and some form of waste or spoilage log — even spreadsheet-level counts. The Discovery Sprint establishes whether what you have is sufficient or whether a short data-collection phase comes first.
Can the system handle multiple sites or outlets?
Yes. Multi-site is the common case. The model breaks waste down by outlet, line, and shift, which is where the variation between sites becomes actionable rather than averaged away.
How does it integrate with our POS or ERP?
We connect directly to your POS, ERP, and production logs via API or file export. Integration is part of the build deliverable — the analytics layer sits on top of the data you already have rather than requiring a new system of record.
How long before the waste models produce reliable outputs?
Typically four to eight weeks of live data after deployment, depending on how much historical data is available at build time. The Discovery Sprint sets a more precise timeline once we have reviewed your actual records.
Will kitchen managers actually use the dashboard?
Only if the outputs are actionable and the alerts are specific. We design the manager view around one question per shift — what is over its waste budget today and by how much — and keep the corrective-action log inside the same screen. Adoption is a deliverable, not an assumption.
Get started
Find out where your waste is actually coming from
Bring your production and sales data to a 45-minute call. We will tell you whether waste analytics is worth building, which source to address first, and what the build would take.
Book a Discovery Sprint