The plant already produces the data. Almost nobody uses it.
Machines emit far more than anyone reads. The gap between what a plant records and what it acts on is where most of the available improvement in this sector still sits.
Data that reaches a screen nobody watches is not data.
Most plants we visit already have telemetry. It arrives in a SCADA system, a machine vendor's portal and a spreadsheet a supervisor keeps, and it is used reactively, consulted after a stoppage, not acted on before one.
The reasons are practical. The feeds are in different protocols, the definitions differ between lines, and nobody has time to watch a dashboard while running a shift.
The work that changes outcomes is collecting at the edge, translating protocols, agreeing what each metric means across lines, and then alerting on deviation in the place the supervisor already looks.
Predictive maintenance follows from that, not preceding it. A model needs consistent history and labelled failures, without those it produces confident noise, and we will tell you when you are not there yet.
From the machine to the decision.
Each layer exists because the one below it speaks a language the one above does not. Skipping a layer is why so many plant IoT projects stall at the pilot line.
Six pieces of manufacturing work we do repeatedly.
Production monitoring
Live line status and OEE built on one agreed definition, not four per-line interpretations.
PLC · SCADA · edge · MESMES & ERP integration
Connecting the shop floor to the business systems so production reality and planning share a picture.
MES · ERP · middlewareQuality & traceability
Batch and serial traceability that survives an audit or a recall, including the supplier chain.
Quality system · batch recordsPredictive maintenance
Where the failure history supports a model, and an honest answer where it does not.
Vibration · temperature · run hoursVision inspection
Automated defect detection on the line, with the borderline cases routed to a person.
Cameras · edge inferenceEngineering document intelligence
Making decades of drawings, specifications and change notices searchable and answerable.
Drawings · specs · change controlBefore you start a plant project.
Three questions we are asked by almost every manufacturing client.
Our machines are twenty years old. Can they be instrumented?
Almost always, and usually more cheaply than expected. Old equipment tends to expose more than people assume; serial output, PLC registers, or at minimum a signal that can be read with an external sensor. Where a machine is closed, retrofitting a current clamp, a vibration sensor or a counter gets you the signals that matter for utilisation and condition. The bigger constraint is usually not the machine but the network in the plant, and that is worth surveying before anyone specifies a solution.
Is predictive maintenance real or a sales story?
Both, depending on your data. It works where you have consistent sensor history and a reasonable number of labelled failures to learn from. It does not work where you have six months of telemetry and three failures, which is the situation in most plants that ask, the model will find patterns that are not there and you will lose trust in it after two false alarms. The honest sequence is to instrument, collect properly, and label failures as they happen; the model becomes viable a year later. Condition monitoring with sensible thresholds delivers real value in the meantime and is far easier to trust.
Can we do this without connecting the plant to the internet?
Yes, and in many plants that is the right architecture rather than a compromise. Edge collection and local processing can deliver monitoring, alerting and inspection entirely inside the plant network, with only aggregated data crossing to a central system, or nothing at all. Operational technology security is a genuine concern and the separation between OT and IT networks exists for good reasons. We design to that boundary rather than asking you to weaken it.