Recently, we have been in a series of digital events promoted by Business Sweden in Poland, Austria, and Hungary. It has been a very enriching experience, not only from the deal-making side, but also because it gave us perspective into how leaders are approaching the use of data analytics to improve overall plant performance.
For us at Viking Analytics, the view of the pain points of heavy industries is directly related to our core value of offering a product that really puts data into the hands of operations, processes, and maintenance experts. Therefore, we see these meetings as more than just sales opportunities, but also a way of understanding what professionals need from data analytics and from us.
And if we could borrow a reference from Hollywood to summarize how these professionals see the use of data, it would definitely be Fast & Cautious. The importance of using sensor data is already clear and they know they need to act fast to improve production KPIs. Their companies re in a race to become more productive through technologies that power Industry 4.0 and some have already started global digitization projects. However, plant managers and leaders are still cautious about how to get it done and where the real value is.
Don’t overcomplicate life
One of the reasons for this caution is previous disappointing experiences in attempts to tackle data analysis. As a manager from a brewing company said, they have already had many digital tools that led to nowhere because it required people being dedicated full time to it. Another factory leader, also in the beverage sector, started the meeting with a simple question: “How sophisticated is the solution? I’m just an engineer.”
Professionals in charge of operations and management of a plant have hectic routines and are extremely time and performance pressed. Any software that intends to simplify their journey towards predictive operations should not require them to become data experts to use the tool.
MultiViz enables effortless transformation of raw data into relevant and useful features. For example, data templates for feature engineering and transformation can simplify the work for the entire team if they are customized, saved and reused for different projects. Simplicity and usability can also help break internal resistance towards new technologies, a challenge mentioned by a director in a manufacturing company.
Data is collected but still not fully transformed in productivity
Another common point was that companies are already collecting production data but have yet to turn it into profitability. And this can be seen in a spectrum that ranges from industries that collect data and never analyze it to others that have data displayed in dashboards but haven’t been able to unlock the potential benefits. Some even mentioned that it is hard to prove the investments in Industry 4.0.
In data analytics, success relies on asking the right questions about the business, the stakeholders and the KPIs. Framing a business question properly is critical to avoid incorrect insights. A good start is understanding what pain points and target KPIs affect your business. Do you need to tackle micro stoppages in the production line? Or is it more important to reduce downtime with predictive maintenance? Then we recommend doing a data quality check to find out if your sensor data contains enough information related to the business case and KPIs you are targeting.
Micro stops and energy optimization are important goals
It’s been well discussed that Covid-19 pandemic has had an impact on all businesses, and industries became even more pressed to reduce costs and increase productivity in order to compensate for the decline in demand and revenues. And while we see an increased demand for digitization products and initiatives, industries have their priorities very clear: optimize energy consumption and tackle micro stops that now lead to big losses in production.
Both are very complex problems that sensor data can help solving. In the Netherlands, a major chemical company used MultiViz and annotated data to build mathematical methods for real-time prediction and optimization of the energy consumed by electrolyzers in a chlorine plant. We have also seen interesting developments in the use of vibration data to determine normal and abnormal operational status of machines.
Heavy industries in all sectors want to beat their competitors in the race to become more productive, to reduce costs and to increase quality in production. But while production data is already recognized as an important way to solving these issues, many industrial managers haven’t figured out how to unleash its potential in an effortless and quick way. And if you can see yourself and your team in this description, let’s talk and see how Viking Analytics can make your data clear. Contact us!