Continuous intelligence: a breakthrough hard to exploit
We are heading into the world of continuous intelligence: companies across all industrial sectors are witnessing a significant increase in the volume, speed and variety of data they receive. The challenge of managing this ever-increasing amount of information has only grown in the last year, as a result of the additional stress generated by the pandemic and the sudden shift to new ways of working relying on new technologies. Our factories and the way we understand decision-making processes have now entered the world of continuous intelligence.
Industrial manufacturers such as Gurelan recognise that, in the 21st century and in the aftermath of a global pandemic, having faster access to higher quality data is critical to improve operations and reduce costs. In order to remain competitive over the next three years, over 90% of companies believe they will need to increase investment in real-time data analytics solutions.
While this is a good starting point, industrial companies still have a lot of work to do in the development of Industry 4.0. For example, new smart machines controlled in real time by their manufacturers are producing huge amounts of data. So how can we manage this data and how can we make the most of it? The challenge is to implement a continuous intelligence process able to analyse historical data and real-time information, so that they are continuously combined for real-time monitoring. This leads to fast and accurate decision-making driven by automation.
What is continuous intelligence?
Continuous intelligence (not to be confused with artificial intelligence, although they are related) is a brand new process, born out of the massive amount of data generated by our companies, machines, economic networks... Beyond the pure analysis of information, continuous intelligence means that real-time analytics of all types of data become integrated into industrial and commercial operations. The aim is to define actions in response to specific situations and events.
Widespread acceptance of the value of real-time analytics is now evident. However, the accelerating pace of technology suggests that many companies may not be making the most of their Industry 4.0 investment by not acting fast enough when it comes to real-time decision making, at any stage of the industrial development or manufacturing process.
How to take advantage of Industry 4.0 data?
The manufacturing industry is particularly at the heart of this phenomenon: according to a study conducted in February by KX (global data analysis software developer and vendor), more than 50% of the industrial companies surveyed noticed a significant increase in the flow of data, of all kinds and from various sources: customer data, information provided by sensors in the factory, reports from machine suppliers...
Access to the underlying technology needed to collect this information in an effective way is the main obstacle to fully exploit the continuous intelligence generated by our 4.0 factories. Once the data are collected, the challenge is to extract their relevant value in order to detect faults, make predictions and provide recommendations to improve operations in real-time.
Why is continuous intelligence held back in Industry 4.0?
Even if an industrial company can access this information, if the analytics process takes hours or even days to be executed, the value of the information drops quickly. As manufacturing processes become faster, so does the need to take decisions as fast as possible. Evaluation and decision making must be performed at process speed, such as determining whether a Zamak or Magnesium die cast part should be optimised, or if there is a defect and it should be rejected.
When done effectively, the use of data to understand and optimise machines and maintenance can make the difference with the competition in a whole industrial sector: that is what continuous intelligence is really about. For now, the problem seems to be at technical level: technology is progressing at a pace that most industrial companies are not able to follow, meaning that it is currently extremely difficult (and expensive) to fully exploit its potential.