Digitization in practice: Production

Industrial manufacturing has always undergone major changes and has always used the latest technologies to optimize workflows, make them cheaper and easier. The many possibilities that have been made feasible and affordable by the digital transformation are no exception.

These digital factors are: the dramatically changed customer expectations, the connection of devices (connected devices) and technological progress, which in addition to production also significantly influences the upstream and downstream industries, such as the supply chain. The effects are already clearly visible and only intensify.

This is associated with many advantages. Today, companies can respond more quickly to change, or even predict them before they arrive. It is therefore not surprising that 50% of industrial companies are already investing in digital technologies, such as artificial intelligence.

General overview

Digital transformation has long since taken shape in industry and is often referred to as “Industry 4.0”. This primarily summarizes technological and process changes. However, socio-cultural innovations are also of lasting importance, albeit less pronounced than in other sectors.

Examples of the innovation that is being brought to production by digitalization include artificial intelligence or the Internet of Things, which describes the comprehensive networking of devices among themselves. Other topics,such as predictive analytics (also in the form of predictive maintenance)or the use of 5G networks, also have a strong impact.

Along with global events such as US political instability, the global Covid 19 pandemic, and the associated difficult economic situation, as well as profound changes in sales markets (empowered customers, faster innovation cycles), the entire industry is changing.

Digitisation as a challenge for industry

If in any other field, even in production, the list of possible obstacles that can block a digital transformation is long. Often it is the managementthat should welcome and push the changes, but instead slow down or refuse.

Concrete reasons for this rejection are, for example,

  • High demands on IT infrastructure and digital performance. The need for new ways of working (e.g. Agile), new technologies (e.g. Cloud Computing, Big Data) and general investments appear to be insoluble tasks and act accordingly as a deterrent.
  • The transformation of the manufacturing processes places high demands on internal communication and the motivation of the employees. Otherwise, the changes can quickly be overwhelming.
  • The industry has traditionally been particularly sensitive to financial challenges, as profit margins and expenses are usually optimized to the maximum possible. Since digitization comes at a high cost, a natural aversion arises here.
  • As with money, the workflows are optimized in time so that changes can easily confuse this good oiled machine. The comprehensive innovations proposed in the context of transformation efforts therefore seem to those responsible to be highly risky.

It is only slowly that success stories and best practices are spreading to these new challenges within the industry. It is therefore not necessarily surprising that outspoken opponents of the digital revolution are still finding themselves in the industry. However, the acceptance of these innovations is increasingly manifested as a critical decision that can determine future success.


Digital transformation in manufacturing stands for more than just automation and collection and use of additional data. As in other industries, a fundamental change in attitude and self-image is needed to benefit from the benefits.

In concrete terms, this may look something like this:

  • The launch of a separate platform for exchange with customers (both in the B2B and B2C area) enables customer centering through regular feedback. Technical improvements and optimization of sales channels (consolidation to one channel) facilitates, streamlines and accelerates sales.
  • The use of big data provides new insights that can be seen in process optimizations, savings and quality improvements. The use of machine learning as a quick result of better data collection is a good example of technologies with extremely high potential for industrial enterprises (increasing productivity in the double-digit percentage range is not uncommon)
  • Predictive Maintenance uses sensors and networking from the Internet of Things in combination with big datato determine the ideal time for machine maintenance. The savings in maintenance costs and minimization of downtime are quickly noticeable. The principle of data-driven forecasting can also be used to optimize work processes and has enormous potential.
  • Wearables support human workers in the production process and increase productivity, safety and job quality. Augmented reality glasses are used, for example, in warehousing, where they project the fastest way to a storage bin for employees and automatically record the removal/storage of a good. Vehicles can also be detected and their routes optimised with the same principle, both on the factory premises and worldwide. Delivery services are already using this method with great success and serve as an example.

Key technologies for digitalization in industry

Industry 4.0

The “fourth industrial revolution” combines production and digitization. Its main starting point is the automation and real-time recording of all work processes. As an umbrella term, this concept in turn includes other parts, which often themselves represent collective terms.

For example, the Internet of Things, Big Data, Machine Learning, Predictive Analytics, Artificial Intelligence and other buzzwords fall under the umbrella of Industry 4.0. This collection of state-of-the-art technologies and systems quickly makes the term seem vague. In its simplest interpretation, however, it can be summed up as “digitalization of industry”.

Internet of Things

IoT is a key technology in Industry 4.0 and digital transformation in general. It describes the connection of tangible objects through a network and their communication with each other. A large amount of information is exchanged, as the range of sensors that can be integrated into the network is extremely large.

Inthe retention of the network, the collected data is processed algorithmically and corresponding derivatives are made. Production plants that have a well-established IoT concept can thus achieve an unprecedented level of automation.

Machine Learning

The enormous amount of data available is almost impossible to process with previous systems. Fortunately, it provides the perfect basis for the use of artificial intelligence. A sub-area of this is machine learning, in which sophisticated algorithms are used to derive meaningful recommendations for action from huge amounts of data.

In this context, previous findings can be clarified and completely new ones can be gained, which leads to performance advantages and high savings. Predicting events is one of the most impressive functions and provides particularly valuable insights that can be used to prevent hazards, predict, improve work quality, and more.


While the general shape and tasks of future robots will not deviate excessively from current models, a huge change in behavior and quality is to be foreseen. Intelligent recognition patterns and the learning capacity of modern systems are increasingly achieving better results.

Thanks to the increasing number of connected systems and sensors and the data generated by their interaction, the available information base is multiplying. Innovative, extremely powerful technology is the result.

Positive effects of digitalization in manufacturing

In view of the great hype surrounding digitalization in production, it is only logical that correspondingly attractive advantages await companies that rise to this challenge. These positive effects fuel the general interest in Industry 4.0 and can be roughly broken down:

Better use of your own data

Companies generally “know” more than they are able to use in the day-to-day workflow. The problems of merging and evaluating different data sources have long been a thorn in the side of strategists and data scientists alike. By improving the use of our own resources and the technologies of the big data world, the often extensive wealth of knowledge in companies can finally be used.

Improved business processes

Process optimization has long been a worthwhile field, but in recent years it has reached a limit of usefulness. Once all processes have been detected and optimised, there are few innovations left to realize.

With digitalization, new technologies are entering the industry and are also confusing business process management. Real-time monitoring and an unprecedented arsenal of sensors and networked objects enable new insights to be gained. The operational implementation is expanded with new possibilities and leads to better results.

Process specialists are given new tools to help them renew and optimize workflows.

More innovation

Innovation creates innovation. The use of new technologies allows you to build on these and raise further potentials, because the new possibilities inspire IT experts, engineers, managers, data scientists and many more. Digitisation is thus accelerating itself by giving innovators the tools they need.

As this is a global phenomenon, the same impact can be observed for partner companies, customers and suppliers. Here, too, mutual reinforcement effects are taking place, providing fertile ground for innovation.

Better ways of working

New technologies enable intelligent monitoring, maintenance, problem solving and optimization. This can be used to correct disadvantages in existing models. Outsourcing and nearshoring are becoming sudden valid options for companies that are often reluctant to use these methods for good reason. However, digitalisation can quickly and easily eliminate these reasons.

Working methods such as “Agile” are changing industries in a sustainable way by offering employees and managers new perspectives and opportunities. Not only the results of frameworks such as SCRUM or Extreme Programming are often far superior to traditional hierarchies; they also provide new incentives for employees, promote experts and enable personal growth.

Customer centering

The idea of putting the customer at the centre often failed in practice due to the lack of knowledge about the same customers, their wishes and opinions. These times are finally over with the digital transformation.

The result is a comprehensive knowledge of consumers and, for the first time, the opportunity to act directly and flexibly on the basis of this knowledge at any time.

What it takes

Digitisation is an integral part of our professional and private life, which cannot be effectively refused. As suppliers, customers, private individuals, administrations, banks, traders… Simply, everyone feels the effects of this transformation and does their best to follow them, the same is expected in the manufacturing industry.

Innovating based on the thrust of digitalization is therefore an absolute duty.

Fortunately, because of the extreme breadth of these changes and the millions of companies and individuals that affect them, best practices have long since emerged for each industry. And even if dubious consultants like to claim the opposite in sales talks, there is still time to start adapting to this new reality.

On the other hand, it cannot be denied that the clock is ticking: those who ignore the signs of the times can no longer meet the demands of the market. This is a development that can be observed in many places and which is becoming a disaster for companies in series.

However, those who focus on the “digital pillars” – networking components, extensive use of data, use of modern technologies – can quickly reap the first fruits of the transformation.