Case Studies

See how manufacturing problems became measurable business outcomes

Drawn from real engagements. Client names are withheld; the situations, the work and the arithmetic behind every number are genuine.

Better decisions

Around $39,000 a year recovered from field testing and warranty

Indian subsidiary of a €3 billion global automotive components and systems supplier

An engineering director was accountable for how the company's climate control systems performed in real vehicles, on real roads, in real Indian conditions — and for the warranty exposure when they did not.

What was going wrong

The only way to know how a system behaved in the field was to put a senior engineer in the vehicle for a week-long cross-country trial, writing down temperatures, filter condition and refrigerant pressure by hand as conditions changed around him. Roughly twenty trips a year.

Design reviews turned into arguments about whose recollection of the trial was right. He was signing off design changes and warranty positions on evidence he privately knew was thin, and his best engineers were spending weeks in a car instead of at a drawing board.

What it was costing

Expensive trials, inconsistent data, senior engineering time lost, and warranty claims that could not be traced back to a cause.

The plan we gave them

01Agreed with the leadership team exactly what they needed to know about a system in the field, and what decision each answer would drive.
02Established one compact on-vehicle monitoring approach capturing twenty temperature points, filter clogging and refrigerant pressure continuously.
03Made the results available as proper time-series data so engineering could analyse behaviour instead of recalling it.

How the gain arises

The engineer no longer travels on the trial — data comes back on its own: 1 engineer removed from ~20 week-long trips = ~100 days of salary and trip expenses saved: ~$5,000 a year
Those 100 days go back into design work: Opportunity cost recovered: ~$4,000 a year
Failure analysis finally runs on real continuous field data, so root causes get designed out: ~10% reduction in warranty claims: ~$30,000 a year
Cost of obtaining field data falls; quality of the relationships found in it rises: ~40% lower monitoring cost, ~50% better ability to identify meaningful relationships

How they lead now: Design reviews now open with field evidence rather than opinion, and the team decides which trials are genuinely necessary instead of running them by habit.

Reduced losses

Blast design set on measured data, not estimate

Large public-sector mining and minerals enterprise

A mine's operations head was judged on tonnes moved and cost per tonne, both of which begin at the blast face — the one step in the chain nobody could actually measure.

What was going wrong

There was no reliable way to measure the fragment size distribution of a blast pile. Blast parameters were set on experience and the result was assessed by eye after the event.

Every downstream problem — slow loading, a struggling crusher, a dust complaint — could be blamed on the blast, and nobody could prove otherwise. So the team over-blasted, because more explosive was the only defence against being wrong.

What it was costing

Avoidable explosive consumption, avoidable dust and compliance exposure, and productivity that swung from blast to blast for reasons nobody could name.

The plan we gave them

01Defined, with the blasting engineers, what a good blast looks like in measurable terms rather than in judgement.
02Established a cloud-based image processing approach that turns photographs of a blast pile into precise fragment size distribution.
03Put that distribution in front of leadership and blasting engineers together, blast after blast, so design became a reviewed decision.

How the gain arises

Fragment size is measured after every blast instead of estimated: Blast parameters are tuned to a target distribution rather than padded for safety
Over-blasting stops being the default defence: Explosive consumption and dust generation both reduce
Better-sized muckpile feeds loading, haulage and crushing: Downstream processing cost falls and throughput stops fluctuating blast to blast

This engagement was measured in operational improvement rather than a single certified figure; we have not attached a rupee value the client did not publish.

How they lead now: Blasting decisions are now reviewed against measured fragmentation data each cycle, so parameters are tuned deliberately instead of being carried forward by habit.

Recovered capacity

Around $66,000 a year recovered and 600 engineering hours released

Global automotive fuel management systems manufacturer supplying leading two- and four-wheeler OEMs

An R&D head had new products waiting to launch and one test rig standing between them and the market.

What was going wrong

Testing depended on a complex manual rig — ten runtime measurements, multiple solenoids and positioning sensors, every reading taken and written down by a person.

He was being asked to go faster while knowing the results themselves contained human error. The obvious answer was commercial test automation, which meant a capital request he could not easily justify.

What it was costing

Testing throughput capped time-to-market, manual recording put errors into the data engineering relied on, and the alternative required significant capital.

The plan we gave them

01Started with the testing outcome the business needed, not the equipment it assumed it had to buy.
02Designed and proved an automated approach to valve control and data capture built around a low-cost controller.
03Handed the plant a rig whose throughput and error rate could be reviewed as a business number.

How the gain arises

Automated valve control and capture removes manual pauses in the test cycle: ~15% faster testing rate: ~$55,000 a year
Readings are captured by the instrument, not transcribed by hand: 100% reduction in recording errors: ~$5,000 a year
Engineers stop babysitting the rig: 600 engineering hours a year returned to product work: ~$6,000 a year
The low-cost controller replaces commercial test automation: Planned capital spend reduced ~18%; time-to-market improved ~30%

How they lead now: Engineering leadership now treats test capacity as a manageable business variable, reviewing throughput and error data routinely instead of escalating only when a launch is at risk.

Reduced losses

Around $3.1 million a year in identified cost reduction

One of the largest commercial vehicle manufacturers in India

A quality head owned two numbers that would not move: line stoppages caused by circuit testing, and warranty cost caused by the faults that testing missed.

What was going wrong

Locating faults in vehicle electrical circuits took significant time at the assembly line and again in service centres, and defects that escaped detection surfaced later as field failures.

Design blamed the supplier, sourcing blamed the process, the plant blamed the harness. There was no granular record of what actually failed, so every meeting was assertion against assertion.

What it was costing

Undetected circuit defects were a major driver of warranty and service cost, slow diagnosis held up the line, and no one could see where faults originated across designs, processes and suppliers.

The plan we gave them

01Established a rapid, data-driven circuit test that runs in four simple steps at the line and in service.
02Reported every result to a dashboard that made fault patterns visible at a granular level to engineering, quality and sourcing.
03Used the accumulated fault record to drive design, supplier and process changes on evidence.

How the gain arises

Testing cost — down 80%: ~$860,000 a year
Rework and retest cost — down 60%: ~$400,000 a year
Warranty servicing cost — down 70%: ~$600,000 a year
Warranty cost — down 50%: ~$1,070,000 a year
Data capture and recording cost — down 50%: ~$100,000 a year
Data analytics cost — down 50%: ~$100,000 a year

Total identified: around $3.13 million a year. Scrap, interest cost, lost sales, inventory holding and vendor evaluation were not counted, so the real figure is higher.

How they lead now: Quality, design and sourcing teams now work from one shared record of what actually fails and why, so supplier conversations and design changes are based on evidence rather than assertion.

Operational visibility

One trusted performance picture across dispersed sites

Large open-cast mining enterprise

A corporate operations director was answerable for the output of haul fleets across sites hundreds of kilometres apart, and for the dust those fleets raised over the communities beside them.

What was going wrong

Leadership had limited visibility of how the haul truck fleet was actually performing across dispersed operations, and no reliable measure of dust and air quality on haul roads.

Every site reported its own numbers in its own way, so the monthly review became a reconciliation exercise. Capital was being allocated between sites on figures that could not be compared.

What it was costing

Fleet availability and payload determine output in mining, and dust suppression determines safety, compliance and community standing. Both were being managed on estimates.

The plan we gave them

01Agreed one definition of good fleet performance that every site would be measured against.
02Connected on-board vehicle systems and environmental sensing so vehicle health, payload and air quality flowed continuously into a single view.
03Put that single view at the centre of the leadership review.

How the gain arises

Vehicle health and payload are read from the machine, not reported by the site: Dumper decisions become faster and comparable across sites
Air quality is measured continuously along haul roads: Water sprinkler logistics are planned against data; corrective action moves from reactive to same-shift
Every site reports on the same basis: Capital and operating decisions rest on evidence rather than on the strongest advocate

How they lead now: Site and corporate leadership now hold a single monthly performance conversation using the same numbers, rather than reconciling competing site reports.

Recovered capacity

Supervisor effectiveness up around 30%

Large domestic appliances manufacturer

A plant head wanted his supervisors to run their shifts. Instead they were spending them collecting numbers about the shift.

What was going wrong

Production and productivity data was collected by hand and reviewed well after the shift had ended — by which time the chance to intervene had gone.

Because the figures were assembled by the people being judged on them, they were disputed as often as they were used. Management reviews debated the accuracy of the data instead of the performance behind it.

What it was costing

Supervisor hours consumed by clerical work, lost output that could have been recovered within the shift, and reviews that produced argument rather than action.

The plan we gave them

01Automated collection of production data directly from the machines.
02Presented output and productivity as hourly insight the operators and supervisors could see themselves.
03Made that same hourly record the only version used in the management review.

How the gain arises

Data comes off the machine, so supervisors stop recording it: Reporting effort drops substantially and those hours go back to the line
Performance is visible hour by hour, not the next morning: Problems are corrected inside the shift they occur — the loss is prevented, not explained
One trusted version of production performance: Operator and supervisor effectiveness up around 30%

How they lead now: Supervisors now correct problems within the shift they occur, and management reviews discuss causes and priorities instead of arguing about the numbers.

The cost of waiting

In each of these businesses, the losses had been running for years before anyone measured them. Nothing forced the issue. The measurement did.

Could your business be the next success story?

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