When you see what’s happening but not why
Challenge: ”We see what’s happening – but not why.”
Many industrial companies have a good overview of how their production is performing. KPIs, dashboards, and reports clearly show when something is out of the ordinary. But even when the numbers are clear, it is often difficult to quickly understand why problems arise – and even more difficult to systematically prevent them from recurring.
In complex production environments, it is not enough to track results. You need to understand what actually happened in the process when the outcome changed.
Here we go through why traditional follow-up is rarely enough – and how event-based analysis in AVEVA™ PI System provides faster root cause analysis, better decision support, and more proactive improvement work.
When KPIs are not enough for improvement
Tracking production figures and availability is standard practice in most factories today. But in complex process environments, it is not enough to know that something is wrong – you need to quickly understand the cause.
In both the process industry and life sciences, root cause analysis is time-consuming and dependent on specialists who manually compile data from different systems. This delays action and makes improvement work reactive.
AVEVA PI System enables the analysis of events in context – batches, shifts, stoppages, and process variations. At Roima, we help you structure your analysis so that improvement work can take place continuously and systematically, not just when major problems arise.
Common limitations with KPI-based monitoring are that:
- deviations are detected, but the causes remain unclear
- analysis requires manual compilation of data
- actions are delayed until specialists can be freed up
- improvement work becomes more reactive than preventive
When deviations become statistics instead of learning
In many businesses, deviations are recorded but not fully analyzed. OEE drops, energy consumption increases, or batches have poorer outcomes, but the work stops at noting the change.
To really understand why something went wrong, you need to look back at the process in its context. But manually going through historical data, filtering time periods, and finding connections takes time – and is often left to the most experienced specialists.
The result is that only the biggest problems are analyzed. Minor deviations become part of ”normal variation,” and the potential for improvement is lost.
Events as units of analysis instead of individual data points
One of the great strengths of the AVEVA PI System is the ability to structure the analysis around events in production, rather than individual signals. An event can be a batch, a shift, a recipe change, or a stoppage.
By gathering all relevant process values, conditions, and quality data around an event, it becomes easier to see patterns, compare similar situations, and understand what affects the outcome.
This provides a more business-oriented analysis that does not require the user to be an expert in raw data.
When analyzing events, you can:
- compare good and bad batches in a structured way
- see differences between shifts, products, and operating modes
- identify variations that are not visible in individual trends
- link process data to operational status and quality
Faster from question to answer
When analysis tools are structured around real events, the time from question to answer is drastically reduced. Instead of searching manually, users can quickly filter relevant periods and compare outcomes with just a few clicks.
This allows improvement teams to focus more on action and less on data collection. It also allows evaluation of minor deviations on an ongoing basis – not just the most critical ones.
Over time, this creates a more proactive improvement process that takes place continuously, not just during major projects.
Support for both process and quality analysis
In the process industry and life sciences, the relationship between process parameters and quality outcomes is often complex. Small variations can affect product characteristics without being visible in the KPIs.
By linking quality data to batches, shifts, and process events, it becomes clear how different parameters affect the result. This provides a better basis for decision-making for both the quality organization and the operations teams – and simplifies work in regulated environments where improvements must be carefully documented.
From expert dependency to collaborative improvement
When analysis requires deep technical expertise, improvement work becomes dependent on a few key individuals. With a more structured analysis model in AVEVA PI System, more roles – production, quality, engineering, maintenance – can work with the same data foundation.
This creates consensus and makes improvement work more distributed and less vulnerable.
When more people can contribute, it means that:
- production, quality, and engineering share the same picture
- improvement proposals are closer to the process
- decisions can be made faster
improvements are made on an ongoing basis in operations
Roima’s role – analysis that supports decisions
At Roima, we help you build analysis models based on how production works in reality – not just on how the signals happen to be set up. By combining the AVEVA PI System with MES, quality systems, and business data, we can create analyses that reflect the entire production chain.
Our goal is for analysis to support decisions – not just fill reports.
Summary
Knowing what is happening in production is not enough. You need to understand why.
With event-based analysis in AVEVA PI System, you can connect process data, operational status, and quality outcomes – and move faster from question to answer.
Roima helps you structure your analysis so more people can participate in improvement efforts and actions can be prioritized based on both technical and business impact.
Want to see how this works in practice? Watch our on-demand demo of AVEVA PI System.
