OPERATIONAL PROCESS HEALTH · PROCESS, QUALITY & OEE
Run a healthier process — shift after shift.
Turn live process, quality and OEE signals into prioritised decisions and coordinated action — so drift is caught early, the golden batch is repeatable, and every shift lifts the line a little further.
Process signals are everywhere. Coordinated decisions are not.
Plants are full of telemetry — control systems, historians, LIMS, MES, inspection results, operator logs. Quality drift, OEE losses and process variability all leave traces. The hard part is turning those traces into prioritised action across operations, quality and maintenance — before the next batch is off-spec, the line stalls, or the deviation has to be written up.
Drift, anomalies, SPC excursions and OEE losses fire across multiple systems — without lining up against the procedures, batches and crews they affect.
Operators chase the loudest alarm. Process engineers investigate after the fact. Quality logs the deviation. The decision loop is fragmented across people and systems.
Yield, throughput and first-pass quality stay below what the process is actually capable of — while the data needed to fix it has been there the whole time.
The best process knowledge sits with a handful of people.
On most lines, the difference between a healthy process and a struggling one is a small number of senior operators, process engineers and quality SMEs who know how the unit behaves — what a good run looks like, where it tends to drift, when to intervene, and what to ignore. When they're on leave, between shifts or stretched across multiple sites, decisions slow down and the line drifts back to baseline.
XMPro helps capture that expertise inside repeatable decision workflows and MAGS-powered agents — so the way your best shift runs the process becomes the way every shift runs it.
From scattered signals to a healthier process loop.
XMPro connects live process data, quality results, OEE losses, batch context, procedures, recommendations and MAGS-powered agents — so teams can move from reactive alarms to guided, coordinated process health decisions. The goal isn't only earlier detection. The goal is a better decision loop, run every shift.
- 01
Detect drift & loss
SPC excursions, quality drift, OEE losses and bottleneck signals surfaced from live process and inspection data.
- 02
Diagnose root cause
Match the signal to the likely cause using batch context, golden-batch comparison and SME-codified rules.
- 03
Prioritise response
Rank by yield, quality, throughput, safety and the intervention window the crew on shift actually has.
- 04
Coordinate corrective action
Route to operators, process engineers, quality or maintenance — with the procedure, evidence and approvals attached.
- 05
Capture evidence & outcome
Record what was done, what worked, and what changed in the process — ready for audit, review and continuous improvement.
- 06
Improve the standard
Feed outcomes back into golden-batch rules, SPC limits and process strategy — the next shift starts from a better baseline.
From monitoring to autonomous control — at your pace.
Teams progress along three operating phases as confidence, evidence and governance allow. Same canvas, same connectors, same governance — just more of the process-health decision loop carried by the platform over time.
PHASE 1
Monitor & Predict
See drift before it costs a batch.
Live process, quality and OEE signals surfaced into unified dashboards. SPC excursions, quality drift and equipment losses flagged early to the team that can act.
PHASE 2
Advise & Coordinate
Recommend, prioritise, coordinate.
Recommend corrective action, prioritise across competing losses, attach the right procedure and evidence, and coordinate the response across operations, quality and maintenance.
PHASE 3
Operate Autonomously
Act within policy boundaries.
Trigger selected setpoint adjustments, evidence capture or work orders within approved governance boundaries when confidence is high — humans on the loop, not necessarily in it.
What operational process health looks like on the line.
Eight process-health use cases customers run on the platform today — spanning OEE, golden-batch repeatability, quality assurance, SPC, yield optimisation and the bottleneck and corrective-action coordination that makes them stick.
Agent templates built for process health.
Six agent patterns that codify the decision work operations, quality and process teams do every shift — ready to deploy from the AI Agent Library, customise to your process, and govern under your control modes.
Equipment Health Monitor
Surfaces live equipment condition and OEE-relevant losses across the line so operators see the assets dragging performance first.
AI AGENTProduction Rate Performance Optimizer
Tracks production rate against target, identifies the constraining loss, and recommends adjustments to lift throughput without breaking quality.
AI AGENTQuality Control Guardian
Watches quality results in process context, flags early drift against SPC limits, and recommends corrective action before the batch is off-spec.
AI AGENTProcess Optimization Specialist
Compares the current run against the golden batch, isolates the variables driving the gap, and recommends setpoint moves operators can verify.
AI ADVISORCSTR Quality Control Advisor
Asset-specific advisor for continuous reactors — monitors product quality and reaction conditions, and recommends interventions to keep yield on target.
AGENT TEAMOEE Optimization Team
A team of cognitive agents coordinating across availability, performance and quality losses — the full process-health decision loop, governed.
Trusted by industrial operators.
Where to go next.
Operational process health sits inside the wider Agentic Operations Platform. Continue exploring the surrounding solution surfaces and architecture.
Run a healthier process — every shift.
Bring process, quality and OEE signals onto one canvas with the workflows, recommendations and Expert AI Agents that turn them into coordinated action — under governance you control.
