AI IN MANUFACTURINGAI in manufacturing: less scrap, fewer breakdowns, less cash tied up in stock

AI that reads your production, quality, maintenance and ERP data and points at the next point of margin: one factory, one number, proven before you spend more.

Get your AI roadmap Read: one factory first

Start with one factory

1One factory, one metricScrap, yield or downtime
2Baseline before the buildSame product mix, fixed period
3Build on the data you haveERP, quality and maintenance records
4Prove it, then widenAbout 90 days to a decision

The approach a Warburg Pincus portfolio manufacturer chose for its first pilot.

Where AI pays off

In short: AI pays off in manufacturing where data already exists and a result flows straight to profit: scrap and quality, unplanned downtime, production planning, stock and working capital, procurement, and the ERP data that ties them together. Start with one factory and one metric, set the baseline first and prove a gain in about 90 days. Quantana helps manufacturers do exactly that, including a Warburg Pincus portfolio manufacturer.

“If scrap is at 7 per cent, can we make it 6?”

That was a managing director’s test for his first AI pilot, in a discovery session we ran at a mid-size manufacturer in the Warburg Pincus portfolio. [1]

His wish list was long: inventory, procurement, CNC planning, maintenance, forecasting, overtime. Every item was a fair AI use case. Together they were a programme, not a pilot.

So he picked the one he could see and measure. That is the right instinct, and it is where this page starts.

Where does AI pay off in manufacturing?

Six places. Each one uses data most plants already hold in the ERP, quality records and maintenance logs, and each has a number you can measure before and after.

Where AI pays off in a factory

Quality and scrap

What the AI doesLinks scrap and rework to machine, shift, lot and settings; flags a batch that looks like past scrap before it runs

The number to watchScrap rate; material yield

Pick an area to see what the AI does and the number to watch. People decide every change on the line, in the plan and in the ERP.

Where AI pays off in a factory
Area	What the AI does	The number to watch
Quality and scrap	Links scrap and rework to machine, shift, lot and settings; flags a batch that looks like past scrap before it runs	Scrap rate; material yield
Maintenance	Finds repeat breakdowns in work orders and machine logs; ranks assets by the cost of their next failure	Unplanned downtime hours; mean time between failures
Production planning	Proposes schedules that use constrained machines better and shows the trade-off of taking a low-margin order	Machine utilisation; on-time delivery
Working capital	Spots slow-moving and excess stock; suggests reorder points from real demand rather than habit	Inventory days; cash tied up in stock
Procurement	Reads supplier quotes and contracts; compares prices against history and flags terms that drifted	Price variance; supplier lead time
ERP and SAP data	Answers plain-English questions on live ERP data and flags anomalies, within each user’s permissions	Time to answer a management question

The best-run plants show what is possible at scale. The World Economic Forum’s Global Lighthouse Network now has 238 sites. In its June 2026 cohort, Rockwell Automation’s Singapore site cut defects by 35% and raised units per person-hour by 43% using more than 50 digital and AI solutions, including AI-enabled quality control and intelligent maintenance. Hitachi Vantara’s Norman site cut inventory by 50% after joining up inventory visibility and decision-making. [6] Those are the leading sites in the world, with years of work behind them. The lesson for everyone else is the order of operations, not the headline number.

What have we done in manufacturing?

Warburg Pincus portfolio. Quantana regularly speaks at Warburg Pincus portfolio companies and helps them adopt AI. That includes discovery and pilot scoping at a portfolio manufacturer: a factory-level AI pilot on scrap reduction and material utilisation, sized to prove its value before rolling out. It also includes a workshop for CFOs and finance teams, and use-case work on supply-chain visibility and financial reconciliation. [2]
AISIN (Toyota Group). For AISIN, a Fortune Global 500 automotive parts maker, [4] we designed an edge computing solution installed in government vehicles that detects and reports road issues in real time and integrates with AISIN’s cloud. It is operational in Japan and Europe and has been adapted for the Indian market. [3]
Laboratory performance. For a global enterprise with testing and innovation laboratories across several regions, we are designing and building a platform that replaces spreadsheets with structured daily updates, downtime root-cause logging, escalations and an AI insights layer: downtime pattern analysis, a daily digest and trend forecasting. [5]

Why start with scrap and quality?

Because the value is easy to see. One point of scrap is material you no longer pay for twice. The data already exists, one factory team owns the process, and the result can be tracked week by week.

One point of scrap

The managing director’s test for a first pilot: if scrap is at 7 per cent, can we make it 6? That one point flows straight to profit. Illustrative. The 1,000 t volume shows the arithmetic; it is not the plant’s figure.

Gauge: scrap rate falls from 7 per cent to 6 per cent. Illustratively, on 1,000 tonnes of material, that one point is 10 tonnes saved.
Measure	Scrap rate	Scrap per 1,000 t of material (illustrative)
Baseline	7%	70 t
Pilot target	6%	60 t
Saving	1 point	10 t of material saved

Compare that with a forecasting pilot. Even a good forecast gets overruled by experienced buyers, so its value becomes a grey area that is easy to dispute. The manufacturer in our session was already months into a price-forecasting pilot with another vendor with nothing usable yet. [1]

What the AI does for scrap: it links each scrap and rework record to the machine, shift, material lot, settings and operator notes behind it, finds the combinations that keep showing up, and flags a batch that looks like past scrap before it runs. The plant team decides what to change. Read the full story in One factory first: an AI pilot that proves ROI in 90 days.

How does AI reduce unplanned downtime?

Start with the breakdowns you have already recorded. Work orders, failure codes and operator notes show which machines fail the same way again and what happened just before. AI reads years of that history in hours and ranks assets by the cost of their next failure.

A long-running US federal guide to operations and maintenance estimates that a properly functioning predictive maintenance program saves 8% to 12% over preventive maintenance alone, and more than that for sites that mostly fix things after they break. [7] Use those figures as a direction; your own baseline is the number that counts.

Downtime is also a safety issue. Manufacturing accounted for 14,200 serious workers’ compensation claims in 2024–25, at 9.4 per million hours worked against 6.7 across all industries. [10] Fewer emergency repairs means fewer rushed jobs around live machines.

Can AI help with planning and working capital?

Yes, once the first pilot has earned trust. Three common next steps:

Production planning. Propose schedules that make better use of constrained machines, and show the trade-off of accepting a low-margin order to fill spare capacity.
Stock and working capital. Flag slow-moving and excess stock, and suggest reorder points from real demand rather than habit. Cash tied up in the warehouse is cash not working.
Procurement. Read supplier quotes and contracts, compare prices against your own history and flag terms that drifted since the last renewal.

What about our SAP and ERP data?

That is where most of the answers live. Production orders, goods movements, scrap postings, stock and purchasing are all in the ERP. The problem is getting a straight answer out of it without waiting for a report.

QSAP connects the AI assistants your team already uses, such as Claude, ChatGPT or Microsoft Copilot, to SAP S/4HANA. It reads within each user’s SAP authorisations, condenses the data first for 80% token savings, and prepares drafts that a person approves in SAP. So a plant manager can ask “Which materials had the most scrap postings this quarter, by line?” and get an answer from live data.

For the full picture, including SAP Joule, licensing and controls, read our guide to AI in SAP.

How do you make sure the AI pays back?

Most companies have not got there yet. McKinsey’s 2026 survey found only 37% of respondents attribute any EBIT impact to AI, unchanged from 2025. [8] Gartner’s survey of 2,002 CIOs found only 13% say their enterprise has realised significant or transformational value from AI, and puts the likelihood of a use case succeeding at one in five. [9]

The same Gartner research found organisations with rules for when a pilot should not move to production are 2.3 times more likely to be among the leaders. [9] So write the stopping rule before you start:

One metric, one baseline, one owner.
An agreed way to separate the AI’s effect from everything else that changed.
A date to decide: widen, fix or stop.

How do we start?

Weeks 1 to 2: pick the factory and the metric, and set the baseline from your own data.
Weeks 3 to 8: build on the data you already hold, with the plant team reviewing every output.
Weeks 9 to 13: run it, measure against the baseline, and decide whether to widen.

Not sure which metric to start with? Our AI Compass roadmap takes 14 days and is one fixed fee, never per user: From A$15,000 + GST. Private equity owner? See how we run this across a portfolio in AI for private equity. Mining too? See AI in mining.

Figures as at 10 Nov 2026.

FAQ

AI in manufacturing, answered.

What is the best first AI project in manufacturing?

One factory and one metric that flows straight to profit, such as scrap, material yield or unplanned downtime. Set the baseline before you build, give the pilot one owner, and judge it after about 90 days. Forecasting and procurement can follow once a result is proven.

How does AI reduce scrap?

It links scrap records to the conditions behind them: machine, shift, material lot, settings and operator notes. Patterns that a person would need weeks to find show up quickly, and the AI can flag a batch that looks like past scrap before it runs. People still decide what to change on the line.

Do we need new sensors or a data lake first?

Rarely. Most manufacturers already hold years of data in their ERP, quality records and maintenance logs. Start with that. Add sensors only where the data shows a costly problem you cannot see, and only after the first pilot has paid for itself.

Can AI work with our SAP data?

Yes. AI can answer questions on live SAP data, build reports and flag anomalies such as unusual scrap postings or slow-moving stock. QSAP connects the assistants your team already uses to SAP S/4HANA within each user’s authorisations, and a person approves any change in SAP.

How do we prove an AI pilot paid off?

Agree the metric, the baseline and the attribution method before the build. For scrap, that means the scrap rate for a fixed product mix over a fixed period, compared with the same period after go-live. Gartner found organisations with rules for stopping weak pilots are more likely to be among the leaders.

Does Quantana work with manufacturers?

Yes. We regularly speak at Warburg Pincus portfolio companies and help them adopt AI, including discovery and pilot scoping at a portfolio manufacturer on scrap reduction and material utilisation. We designed edge computing for AISIN (Toyota Group) and are building a laboratory performance platform with AI insights for a global enterprise.

Sources

Accessed 10 Nov 2026.

■ YOUR NEXT STEP

Get your AI roadmap

Start with one factory and one number. We map where your scrap, downtime, stock and planning lose money, and which to put AI on first. Get your AI roadmap or talk to us.

One question for your plant team: what is your scrap rate this month, and what would one point less be worth?

Get your AI roadmap Read the AI in SAP guide