Manufacturing · Scrap reduction
Your scrap rate is a process problem, not an inspection problem
End-of-line inspection tells you what to discard. It does not tell you why the part was bad, which shift produced the pattern, or what to adjust before the next hundred parts come off the line the same way.
Banao builds AI systems that watch process parameters — temperature, pressure, feed rate, humidity, material batch — and flag drift before it converts raw material into reject. The goal is fewer defects reaching inspection, not faster sorting of the ones that do.
The first call is free · 45 minutes · no obligation
What we build
What a Banao scrap-reduction deployment covers
Scrap-reduction AI sits upstream of the inspection gate. It monitors the process variables that drive defects and acts before material is already wasted.
Process parameter monitoring
Continuous ingestion of temperature, pressure, humidity, feed rate, and machine state — with anomaly detection tuned to your product's defect-generating thresholds, not generic control limits.
Drift alerts before the batch fails
When a parameter combination historically precedes a scrap spike, the system alerts operators in time to adjust — before the run converts raw material into reject, not after.
Root-cause attribution
The model attributes scrap patterns to the specific machine, shift, material batch, or parameter combination that drove them. Attribution replaces guesswork in root-cause investigation.
Material batch correlation
Incoming material variation is one of the most under-reported causes of scrap. We link raw-material batch records to downstream reject rates so you can quantify supplier contribution.
Yield forecasting mid-run
Given current process state, the model estimates end-of-batch yield before the run finishes — giving production planners early warning to adjust targets or pull maintenance.
Shift-by-shift scrap dashboard
Scrap rates broken down by machine, product family, shift, and material batch — so quality engineers see trend and cause, not just a daily total.
Receipts
Where this pattern has run
Metrics shown dotted (··) are being finalised in our case-study pack — published only once verified.
Process monitoring surfaced batch reject patterns before end-of-line inspection
Manual process checks ran on shift change, meaning parameter drift had hours to accumulate before anyone saw it. Banao deployed continuous sensor ingestion and drift alerting, attributing scrap patterns back to kiln temperature variation and raw-material batch differences.
Dogfooding
We operate our own AI before you run yours
Banao manages a ~300-person engineering organisation on its own AI products. InterviewGod screens every Banao hire; Vikaas runs our own demand pipeline. A system that has to hold up under our internal operation is already stress-tested before it runs on your process line.
We know from operating AI daily that the gap between a promising model and a production system is integration, data quality, and floor-team adoption — not the algorithm. That is what we build.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When scrap-reduction AI is not the right investment
Not every scrap problem warrants an AI system. We will tell you if yours doesn't:
- Low-volume, high-mix lines: if you run dozens of products in small batches, the data volume needed to train meaningful drift models may take years to accumulate. A simpler SPC approach is often the right answer.
- No sensor infrastructure: if process data is not being logged digitally — or logged but not time-stamped to the part — the first project is instrumentation, not modelling. We scope that honestly.
- Single dominant cause already identified: if your scrap is driven by one known issue with a mechanical fix, a model adds overhead without reducing the reject rate. Fix the machine first.
How we start
How we engage — start with the data, not a proposal
We audit your process data before we quote a system. What we find in the first two weeks shapes everything that follows.
- 01
AI Discovery Sprint
2 weeks · fixed price
We ingest a sample of your historical process data and reject logs, identify which parameters correlate with your scrap rate, and return a root-cause analysis and feasibility assessment — yours to keep. If you proceed, the Sprint fee is credited against the build.
- 02
Build
Drift detection models trained on your process history, integrated with your sensors, PLCs, and MES. Alerting, root-cause dashboards, and material batch linkage are all part of the deliverable.
- 03
Production and continuous improvement
System in production with operator alerting, shift dashboards, and a quarterly review of which parameter thresholds have drifted as your process evolves.
FAQ
Frequently asked questions
What process data do you need to start?
Historical sensor logs — temperature, pressure, feed rate, machine state — time-stamped to the part or batch, paired with your reject or scrap records for the same period. Twelve months of history is a useful starting point; less is workable if the Discovery Sprint confirms sufficient signal.
Does our line need new sensors or equipment?
Not necessarily. We start with whatever is already being logged — PLC data, SCADA exports, manual inspection records. The Discovery Sprint tells you whether existing data is sufficient or whether specific gaps need filling before modelling is viable.
How is this different from the statistical process control we already run?
Traditional SPC monitors one variable at a time against fixed limits. The AI layer models interactions between multiple parameters — combinations that individually look in-spec but together predict a reject. It also adapts as your process evolves instead of requiring manual limit recalculation.
How quickly do operators receive alerts?
Alert latency depends on your sensor polling frequency and MES integration. In most deployments we achieve sub-minute alerting from parameter breach to operator notification — fast enough to adjust before the batch completes.
Can the system account for planned product changeovers?
Yes. The model is configured per product family, so a changeover does not trigger false alerts from the new product's normal parameter range. Changeover events are logged and excluded from drift attribution.
Get started
Show us your scrap data — we'll show you where it's coming from
In 45 minutes we can tell you whether your reject pattern has a detectable upstream cause and what it would take to build a system that catches it earlier.
Book a Discovery Sprint