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Operational problems surfaced before they become incidents.

A unified intelligence layer that monitors, detects anomalies and surfaces the right information to the right people — before small problems become operational failures.

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The problem

Data in ten dashboards, insight in none

  • Data in ten dashboards, insight in none

    Operations data exists but is scattered. Getting a clear picture requires pulling from multiple systems manually.

  • Problems found after the fact

    Teams discover operational failures when customers complain or SLAs are already missed — not before.

  • No shared operational language

    Different teams use different metrics, different definitions and different tools. Alignment takes meetings, not data.

  • Alerts without ownership

    Signals fire across tools but no one owns the response. Teams debate responsibility while small issues become operational incidents.

How it works

One operational picture—alerts that mean something

Step 1

Integrate telemetry

OT, IT, and business KPIs stream into a normalized event model across plants and regions.

Step 2

Detect patterns

Anomalies, cascading failures, and SLA breaches are correlated with likely root causes and impacted orders.

Step 3

Coordinate response

War-room briefs, tasks, and customer communications are drafted for commanders to approve fast.

Thresholds and escalation trees mirror your existing operations manual.

Operational problems surfaced before they become incidents.

What's included

What you get when you run this with Thinkia

A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.

Unified data ingestion

Connects to existing operational systems (TMS, WMS, ERP, CRM) without replacing them.

Anomaly detection

AI monitors operational patterns and flags deviations before they escalate.

Real-time alerting

Configurable alert rules by severity, team and channel (email, Slack, dashboard).

Root cause analysis

When something goes wrong, surfaces the most likely cause with supporting data.

KPI command view

Single view of operational health across teams, regions or business units.

Predictive signals

Early indicators of demand spikes, resource constraints or service degradation.

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Results

What changes when this runs in production

Results vary by systems landscape, data quality and operational complexity.

–70%

Earlier detection of operational anomalies vs. manual monitoring

–50%

Time saved on manual operational reporting per week

Single source

One operational view replacing 4+ disconnected dashboards.

How we work

From blind spots to one pane for exceptions, capacity, and risk

Signal design

Week 1–2

KPIs, alerts, and drill-downs are agreed with ops leaders; data latency targets are explicit.

Integrate sources

Week 3–5

ERP, MES, WMS, and tickets feed a governed model; ownership per metric is documented.

War-room pilot

Week 6–9

Daily or weekly rhythms use the tower; decisions and actions are logged for feedback.

Network scale

Week 10+

Plants, regions, or partners join with federated views; playbooks close recurring exceptions.

Plant heterogeneity and OT security constrain integrations; waves follow value and feasibility.

Get started

Ready to scope this for your context?

We start with a focused session—no commitment—to map constraints and a sensible path.

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