Industry 4.0 “the Polish way” – where are we, compared to Europe?

Interview with Dorota Dębińska-Pokorska, Partner at PwC about Industry 4.0 “the Polish way” – where are we, compared to Europe?

Industrial companies have long been under pressure to join the Industry 4.0 trend – what does it mean for industry?

In simple terms, Industry 4.0 means transforming a company towards using the latest technologies, which contributes to improving its profit and competitiveness. A key element of this transformation is the use of data sets – data derived from production processes, and more specifically – from industrial automation systems.

Many companies have been accumulating such data for years, but do not use it or use it to a very limited extent. And it is exactly data analytics that can provide solutions to streamline the production process – a process that largely determines the financial result. Companies that use data from production systems usually do so to analyse the causes of a mechanical failure. In other words, they analyse the past. The real power of data, however, lies in its ability to predict the future – so that the company knows when the failure will occur and can prevent it.

How is Industry 4.0 used by companies in other countries, especially in Europe?

Focusing on the aspect of using production data, the use of advanced analytics has been incorporated into the DNA of many European companies. Special organisational units are created with the sole task of analysing data sets from production processes, as well as from supply chain management and customer processes. Their development is driven by the awareness that analytics generates value. In the first step, companies usually analyse production data for one of two main reasons: to ensure standardisation of processes and comparability of many plants, or to solve key problems in production that significantly affect the financial result. The next step involves integrating analytics with everyday business activity of the company and using it as the basis for making production decisions.

How does it work in specific companies?

Companies use production data to ensure communication between production experts and the “business”. Such communication enables control of costs, quality and employees’ skills, contributing to the improvement of all processes and the use of the infrastructure. Such seemingly simple functionalities are particularly appreciated by companies that have production centres in many locations and, often, in many countries. Take, for example, a global manufacturer of steel products operating in several dozen countries of the world. The key thing for the company was to ensure that all the production plants “speak the same language”. Only then is it possible to compare the plants and understand why one plant performs better or worse than another one in terms of production. 

A leading manufacturer of automotive parts came to the conclusion that if it wanted to remain the market leader, it had to improve the quality and efficiency of the production process. The company struggled with failures and excessive maintenance burden. The analysis of data from machine sensors – data which was available earlier, but never used – made it possible to predict 99 percent of cases of equipment damage in real time. 

Do Polish companies start from the same position as our European neighbours when it comes to Industry 4.0?

If we look solely at production data analytics, we could say that the starting position of Polish companies does not differ at all from that of our neighbours. Differences may appear, however, within 2 or 3 years if we remain passive, while our neighbours take action. Companies from mature European countries, such as Germany, but also from other continents, such as India, have already become aware that data analytics generates value for the company. In this context, we need more activity from Polish companies. 

Today, data analytics is used in making production decisions mainly by companies belonging to foreign corporate groups. They use production data, among other things, to develop solutions allowing them to minimise low quality products, improve input quality and predict failures of key elements of the process line.