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Case study · OperationsAlarm at 2:14. Resolved by 2:31.
The production incident, end-to-end
The night shift does not have to wait for morning. An agent diagnoses the alarm on machine data, raises the ticket, reserves the part and wakes the right person on Teams — only when truly necessary.
Executive summary
Line alarms get lost between shifts; response depends on who is around and what they know.
The agent diagnoses on MES/CMMS data, launches the procedure and involves people only for decisions.
Shorter MTTR, less downtime, every failure with a complete history.
MES, CMMS, spare-parts store, Teams; UiPath agents + robots, Maestro™ orchestration.
A three-shift plant, 350 alarms a month
The 2:14 alarm lands on a panel nobody watches continuously. The operator finishes the round, comes back, classifies it. If it is beyond the shift, it waits for the morning briefing.
By morning it turns out the part should have been ordered overnight, and maintenance is the last to know. MTTR grows not because failures are hard — but because information logistics are.
The current reality
- SystemThe alarm appears in the MES
- WaitingWaits until someone notices it between tasks
- HumanThe operator classifies from experience
- HumanPhone calls: foreman, maintenance, parts store
- Error riskKnowledge of similar failures lives only in heads
- WaitingThe part is ordered only in the morning
- HumanThe failure report written after the fact — or never
The hidden cost of the current process
Downtime is only the top of the bill
- 105 hours a month of human alarm triage — 60% of which is noise.
- Every MTTR hour on a bottleneck line means an unrecovered plan and weekend overtime.
- Failure history is scattered — the same problems return because nobody sees the pattern.
The cost of doing nothing
Manual work is an operational tax: the automation investment is finite, the manual cost is paid again every month.
The process after automation
- AutomationThe agent reads the alarm and machine data in a second
- AutomationMatches against history: known cause, known procedure
- AutomationRaises the CMMS ticket and reserves the part
- HumanWakes the technician on Teams only for a real decision
- AutomationTracks SLA to closure and writes the failure report
What the automation handles
- Round-the-clock triage of every alarm
- Procedures: ticket, part, notifications, escalations
- History and report of every failure — automatically
When a human decides
- Line-stop decisions
- Novel failures with no pattern in history
- Priorities when resources conflict
Before
After
Value model — example assumptions
Business benefits
- MTTR in minutes where today it is hours
- Night shifts with the same support as day shifts
- Repeat failures caught as a pattern, not bad luck
- Maintenance works on data, not phone calls
Board-level KPIs affected
What management gains
- One live picture of failure rates across lines
- Hard data for overhaul and capex decisions
- Procedural discipline without disciplining people
Estimate it for your organisation
An illustrative estimate based on your inputs. A model of released capacity — not a savings promise.
Systems in this scenario
Inputs
- MES / SCADA
- CMMS
- Magazyn części
Mientha agentic layer
- UiPath Agent Builder
- UiPath robots
- Maestro™ · Action Center
Core systems
- CMMS
- ERP (zamówienia części)
- Teams
Human approval: Teams / Action Center
What we deliver
- Analysis of alarm logs and failure history
- A diagnosis agent with a known-cause base
- Automated procedures: ticket, part, escalation
- Teams notifications and decisions for the shifts
- MES/CMMS/ERP integrations
- A monthly failure report and pattern review
What we need to start
- Alarm logs from 1–3 months
- Response procedures (even if only in foremen's heads)
- CMMS access and the critical-parts list
- A shift foreman for 2 workshops
Implementation roadmap
Discovery
We map the process, data and exceptions with process owners.
Design
Target flow, business rules, approval thresholds.
Build
Agents, robots and integrations in your environment.
Validate
Tests on real cases, exception handling.
Go-live
Controlled rollout with human oversight.
Optimise
Monitoring, reporting and continuous improvement.
Typical duration depends on systems and rules — a single process is usually weeks, not quarters.
Risk and controls
Autonomy under control
- The agent never controls machines — it works on information and procedure
- Stops and safety decisions always belong to people
- Every action logged: who, what, when, on what basis
- OT-network access only via existing, approved interfaces
Why now
- Night-shift staffing gaps will not disappear — the support model must
- Every month is ~140 alarms handled slower than necessary
- You already have the machine data — tonight nobody is reading it
Why this matters to:
Less unplanned downtime and a predictable production plan.
The night shift stops being a blind spot.
The team arrives with a diagnosis and the part — not to find one.
Questions we usually hear
We agree. That is why the agent never touches control systems — it organises the information and procedures around the machines. The OT layer stays untouched.
They do — and that is exactly the knowledge the agent captures. Instead of leaving with every retirement, procedures become plant assets.
We start with one — the most expensive to stop. Its patterns and integrations halve the rollout time on the next lines.
When this may not be the right solution
- Only a dozen alarms a month, all of them novel
- No CMMS or failure history at all — foundation first
- Machine data unavailable outside the control room
A question for your next board meeting
Which of last quarter's failures would have ended differently if the response had started one second after the alarm?
What does one hour of downtime on your key line cost?
One month of alarm logs is enough — we will show which of them the agent would close without people.
Let us talk about your plant