The benefits of aggregated production data

"Data is a goldmine - it already contains all the answers to what works and what needs to be improved," said our senior expert consultant Per-Åke Södergren, a specialist in Data Analytics and Machine Learning.

Main areas of production data

There are five key areas where data from production can be used:

1. Performance optimization: Identify inefficient processes and adjust parameters to achieve maximum efficiency.
2. Quality assurance: ensuring that production is of the right quality without creating unnecessary margins.
3. Preventive maintenance: Plan maintenance based on real-time data to minimize downtime.
4. Cost efficiency: Reduce waste by optimizing equipment operation.
5. Regulatory compliance: facilitating reporting and ensuring industry compliance with regulatory requirements.

Each area offers great opportunities for businesses to streamline their operations, while ensuring quality and sustainability.

From gut feeling to data-driven decisions

Traditionally, production decisions have often been based on experience and gut feeling. Cecilia Jakobsson, Business Area Manager at Plantvision Technology, explains:

"When I worked as a process engineer, we often had an intuitive feeling that something was wrong in production. It could be the sound or the smell - but without concrete data, it was difficult to convince others of the problem."

With modern data collection and visualization, this feeling can now be supported by facts. Per-Åke adds:

"It's not about a single measurement, but about looking at multiple parameters that together show that something is going wrong. By visualizing the data, operators can act in time, before problems escalate."

Models and simulations for optimization

Modeling and simulation play a central role in predicting and improving production performance. By comparing real-time data with a mathematical model of optimal operation, deviations can be identified early.

"In today's production, you often only get the results after half a day. With modern models, we can get an indication of whether we're on the right track or need to make adjustments during the process," says Per-Åke.

This approach allows for fine-tuning of production parameters, which not only increases productivity but also creates confidence that limits are being met.

Maintenance that reduces costs and risks

Maintenance has traditionally been one of the biggest cost items for industry. Preventive maintenance based on data is one of the most cost-effective strategies. Cecilia points out:

"We often maintained equipment that was actually working perfectly, which often led to problems afterwards. By relying on data instead, we can maintain at the right time and avoid unnecessary interventions."

Using machine learning, patterns in production data can be identified and linked to potential breakdowns. This enables action to be taken before equipment breaks down, saving both time and money.

Energy optimization and sustainability

At a time when energy consumption and sustainability are high on the agenda, production data plays an important role. By optimizing energy use, companies can reduce costs and environmental impact. Per-Åke emphasizes:

"It's about using the equipment in the right way - not too hard, not too easy. With modern tools, we can now see how production is going in real time and prevent exceeding limits that lead to sanctions."

Reporting of environmental and energy data has also become easier thanks to automated systems, relieving staff and ensuring compliance.

Conclusion: A data-driven future

Production and process data are key to creating an efficient, sustainable and competitive industry. By starting to collect and analyse data, companies can optimize processes, cut costs and improve their environmental performance.

Per-Åke concludes: "If you are not already collecting data, start now. And if you have data, start exploring its potential - the values that can be discovered are amazing."