Use case · Manufacturing
Predictive maintenance in manufacturing
Automating predictive maintenance where plant networks are unreliable and decisions must happen locally in milliseconds.
- Sector
- Manufacturing
- Systems involved
- 5
- Regulations in scope
- 4
What makes this hard
In manufacturing, plant networks are unreliable and decisions must happen locally in milliseconds. Applied to predictive maintenance, that means the automation has to carry an audit trail and a clean escalation path before it carries any speed benefit at all.
We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
Built by engineers who ship production systems, not by a practice that subcontracts the build. You own the code, the models where they are open-weight, and the documentation to run it without us.
How we sequence it
- 01BaselineMeasure the current cycle time, touch count and error rate on predictive maintenance. Without that number there is no way to prove the automation worked.
- 02Map the exceptionsDocument what actually happens when the process does not run cleanly. The exceptions, not the happy path, decide whether this automation survives contact with real operations.
- 03Integrate firstConnect to MES and SCADA and PLC before building any intelligence on top. A model that cannot reach the system of record cannot finish the work.
- 04Ship narrowAutomate the highest-volume, lowest-variance slice and put it in front of real users, with anything uncertain escalated to a human.
- 05Measure and widenReport the straight-through rate against the baseline, then absorb the next tier of exceptions. Coverage rises over time rather than being promised on day one.
Context
- Workload
- predictive maintenance
- Sector
- Manufacturing
- Sector constraint
- plant networks are unreliable and decisions must happen locally in milliseconds
- Systems of record
- MES · SCADA and PLC · ERP · CMMS · quality management systems
- Regulations in scope
- ISO 9001 · factory safety regulations · environmental compliance · sector-specific quality standards
Questions
Can predictive maintenance be automated reliably?
The high-volume, low-variance portion can, with anything uncertain escalated to a human. In manufacturing, plant networks are unreliable and decisions must happen locally in milliseconds, so the escalation path matters as much as the automation itself.
What does it integrate with?
Typically MES, SCADA and PLC, ERP, CMMS, quality management systems. We assess your specific estate during discovery rather than assuming a standard setup.
What about compliance?
ISO 9001, factory safety regulations, environmental compliance, sector-specific quality standards are in scope for this sector. Audit trail and human oversight are built in from the start, not added before go-live.
Do we need to upgrade our machines?
Usually not. Most value comes from data your PLCs and cameras already produce and nobody is currently using.
What if the network goes down?
Edge deployment keeps inference local and tolerates disconnection, syncing when connectivity returns. On a shop floor that is a requirement, not an option.
Other manufacturing workloads
Automating predictive maintenance?
Bring us your current cycle time. We will tell you what is realistically removable.
Or email bd@dtrasglobal.com · call +91 74118 77878