Control

From watching numbers to controlling operations

Control is the layer that closes the loop. It’s not about having dashboards. It’s about dashboards that generate action. It’s not about measuring KPIs. It’s about ensuring that when a KPI deviates, someone knows exactly what to do and does it in time.

The difference between reporting and control is the difference between knowing that something happened and being able to prevent it from happening. Reporting is reactive: you look at last month’s numbers and note what already occurred. Control is proactive: you monitor numbers in real time, detect deviations early, and execute interventions before the problem escalates.

What Control does

Control builds a complete operational control system: from defining the KPI tree to automated interventions, including intelligent alerts and trend prediction.

KPI tree: KPIs aren’t a flat list. They’re a tree with causal relationships. Operating margin depends on revenue and costs. Revenue depends on volume and average price. Volume depends on leads and conversion. The KPI tree makes these relationships explicit so that when something deviates at the top, you can quickly identify where the root cause is at the bottom.

Intelligent alerts: Not all deviations are equal. A 2% drop in average ticket might be normal. A 15% drop in a single day requires immediate attention. The alerting system distinguishes between normal variation and significant deviation, considering seasonality, trends, and context. Alerts reach the right person with the information needed to act.

Trend prediction: Machine learning applied to operational data to predict trends before they materialize. If order volume is gradually declining, the system detects it before it’s visible in monthly reports. If a quality pattern is degrading, the alert fires before it becomes a customer-facing problem.

Automated interventions: For deviations with a known response, the system can execute the intervention automatically. If a product’s stock drops below the minimum, a purchase order is generated. If an SLA is at risk, it’s escalated to the responsible party. If a workflow is stuck, it’s reactivated with the predefined action. The control loop closes without human intervention where possible.

AI where it adds value: We don’t use AI for the sake of using AI. We use it where it genuinely improves control capability. Anomaly detection that a human wouldn’t see. Correlations between variables that aren’t obvious. Predictions based on complex historical patterns. AI amplifies the team’s control capability; it doesn’t replace it.

Why it matters

Without Control, everything the lower layers build generates information but doesn’t generate action. You can have perfect Visibility and impeccable Data, but if nobody acts when the numbers say there’s a problem, the information is useless.

Control is where the organization shifts from operating by inertia to operating by design. Where decisions are made with data instead of intuition. Where problems are prevented rather than repaired.

Typical implementation

Control is implemented on top of the data and dashboards that already exist in the lower layers. The first step is to design the KPI tree with leadership, establishing causal relationships and alert thresholds. In 2-3 weeks the basic control system is operational: KPI tree, alerts, and control dashboards.

Predictive capabilities and automated interventions are added progressively as the system accumulates enough historical data to train the models. The control loop tightens over time: first it alerts, then it suggests, and finally it executes where confidence is high.