What is the difference between operational analytics and a dashboard?

A dashboard shows the current state of the operation: what is happening right now on each line or machine. Operational analytics works on historical data: it looks for patterns, correlations and causes in the data accumulated over weeks or months. The dashboard answers "how are we doing?"; analytics answers "why does this happen and where are we losing?". They complement each other and often live in the same project.

What data is needed for an operational analytics project?

The starting point is the data the plant already generates and has connected: process signals, production records, stoppages, quality rejects and consumption. It does not have to be perfect; part of the initial work is reviewing what history exists, its quality and which questions it can answer. If connectivity is missing, that part is covered by the Connect unit before any analysis.

What kind of questions does operational analytics answer?

Specific business questions that today are answered with impressions: in which shifts or conditions rejects appear most, which process variables move before a quality defect, which stoppage causes accumulate the most lost hours, or whether energy consumption per unit produced is drifting. The rule is always to start from a question with economic value, not to analyse data for its own sake.

How much data history is needed to find reliable patterns?

It depends on process variability and on how often the phenomenon under study occurs. A daily problem can be analysed with a few weeks of data; a seasonal or infrequent one needs a longer span. Each project first assesses whether the available history is enough for the question at hand and, if not, defines what to capture and for how long before drawing conclusions.

Captia AI

Operational Analytics

Analysis of historical and real-time operational data to identify patterns, trends and opportunities. We work on connected plant data to answer specific business questions with evidence rather than impressions.

Operational analytics: from symptom to root cause with data

Operational analytics is the analysis of historical and real-time operational data to identify patterns, trends and opportunities. Where an operational dashboard shows what is happening right now, analytics looks back over weeks or months of connected plant data to explain why things happen: which causes of downtime really weigh the most over a year, which process conditions repeat before a quality defect, and where capacity is quietly being lost through micro-stoppages, slow restarts and accepted rejects.

The method is consistent: start from a specific business question with economic value, audit the available history, look for patterns and validate them against the plant team's knowledge, and turn each confirmed finding into an action, whether a process adjustment, a monitoring rule or a predictive model. Confirmed correlations often become the basis for predictive maintenance and early alerts.

Scope and approach

Captia's industrial AI unit works on data that is already connected; data capture and integration belong to the connectivity area. Based in Xàtiva, Captia Technology delivers these projects for industrial plants across the Comunitat Valenciana, with findings validated on the shop floor and no conclusions the data cannot support.

How it connects to the system

Captia AI is a capability of Captia Technology. This solution relies on data Connect integrates and priorities Consulting defines.

Key concepts

Talk about this solution ← Captia AI

Frequently asked questions

What is the difference between operational analytics and a dashboard?: A dashboard shows the current state of the operation: what is happening right now on each line or machine. Operational analytics works on historical data: it looks for patterns, correlations and causes in the data accumulated over weeks or months. The dashboard answers "how are we doing?"; analytics answers "why does this happen and where are we losing?". They complement each other and often live in the same project.
What data is needed for an operational analytics project?: The starting point is the data the plant already generates and has connected: process signals, production records, stoppages, quality rejects and consumption. It does not have to be perfect; part of the initial work is reviewing what history exists, its quality and which questions it can answer. If connectivity is missing, that part is covered by the Connect unit before any analysis.
What kind of questions does operational analytics answer?: Specific business questions that today are answered with impressions: in which shifts or conditions rejects appear most, which process variables move before a quality defect, which stoppage causes accumulate the most lost hours, or whether energy consumption per unit produced is drifting. The rule is always to start from a question with economic value, not to analyse data for its own sake.
How much data history is needed to find reliable patterns?: It depends on process variability and on how often the phenomenon under study occurs. A daily problem can be analysed with a few weeks of data; a seasonal or infrequent one needs a longer span. Each project first assesses whether the available history is enough for the question at hand and, if not, defines what to capture and for how long before drawing conclusions.