What changed on the floor.
Anonymised accounts of real MachineWise deployments — the plant as it was, what was connected, and what the measurement showed. Write-ups name the sector and cluster, never the customer.
Further down this page are some of the plants that run MachineWise. The write-ups here are kept separate and stay anonymised: we describe the sector, the cluster and the machine count, never the customer, and no write-up is matched to a logo. What matters to a reader evaluating monitoring is what the floor looked like and what changed.
Figures are only quoted where they come from measured platform data and the customer has cleared their use. Where they have not, the outcome is described qualitatively rather than given a number we cannot stand behind.
What actually changed on the floor.
Hidden idle time found across the floor
Monthly spreadsheet OEE, two weeks late, on a floor everyone believed ran at 75%. Live cycle tracking exposed unlogged idle across “fine” machines — late starts, material waits, break creep.
Downtime was logged at shift end from memory, so the monthly OEE figure was disputed in every review.
Software-first connection across the CNC fleet, gateways on the older machines, and reason capture at the machine in two taps.
A repeating start-of-shift pattern and two machines carrying most of the unlogged loss, both addressed by scheduling rather than capital.
Rejections down sharply after root-cause
Mid-week rejection spikes nobody could explain. Machine-state correlation tied them to spindle temperature drift on three machines; a coolant-flow correction ended the argument — and the scrap.
Rejections clustered mid-week with no pattern anyone could name, and the cause was argued about rather than investigated.
Rejections correlated against machine state at the moment each part was produced, across the full fleet.
Spindle temperature drift identified on three machines; a coolant-flow correction removed the cause rather than the symptom.
Live OEE on every CNC by day 3
Defence-customer audit pressure meant evidence, fast. 45 minutes per machine, zero stoppage, no OEM involvement — and an audit team impressed enough to request a supplier-summit presentation.
A defence customer audit required machine-level evidence on a timescale that ruled out any conventional rollout.
Software-first connection machine by machine, on-premises throughout, with no OEM involvement and no production stoppage.
Full-floor coverage by day three, and an audit team that asked for a supplier-summit presentation.
CNC Machine Monitoring Reveals Hidden Production Losses
An auto component manufacturer in Pune wanted better visibility into CNC machine utilization, downtime and production activity across its machining operations. MachineWise connected machine data with shop-floor monitoring to provide supervisors with a real-time view of machine status and production losses. The system helped separate productive machining time from waiting, setup and other non-productive periods.
Production teams were dependent on manual shift reporting to understand machine utilization and downtime. Short stoppages, waiting periods and variations in setup time were difficult to capture consistently, making it challenging to identify where productive machine hours were being lost.
MachineWise integrated CNC machine data and continuously monitored machine states, production activity, cycle information and downtime. Supervisors could view machine-wise and shift-wise performance through live dashboards and analyse historical trends to identify recurring losses.
The manufacturer gained a single source of shop-floor information for daily production reviews. Hidden idle periods and recurring downtime patterns became visible, allowing teams to focus improvement efforts on specific machines and production activities.
The plants behind the numbers.
A few of the 150+ plants running MachineWise today, from Ludhiana to Chennai. Hover a logo to see what they run, or click it for more.
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Three rules every write-up follows.
Anonymised by default
Sector, cluster and machine count. No customer names, no identifying detail, unless a customer has explicitly asked to be named.
Figures trace to platform data
Every number comes from the deployment's own record. Where a figure cannot be sourced or cleared, the outcome is described qualitatively.
The honest version
Including what did not work, where a rollout was slower than planned, and what we would do differently. A case study with no friction in it is marketing.