Continental

The purpose of digitalization in manufacturing tires is to enhance and streamline processes, increase transparency, and make data accessible to improve efficiency through data analysis. At Continental, we have seen consistent progress in terms of the manufacturing technology, our organization having step-by-step development from a ‘data hunter and gatherer’ to a ‘data analyzer’ using different available tools.

One of the main challenges faced by organizations today is striking a balance between sharing data with competent partners to enable effective evaluation, while retaining a competitive advantage by safeguarding valuable results. It is no longer just the conclusion of evaluated results that is important, but the raw data itself.

Furthermore, organizations face the challenge of balancing the quantity and quality of data to ensure that the right data is collected in the right format. The quality of data can significantly impact the ratio of time spent on preparing and analyzing data, which can shift from 90:10 to 10:90.

It is important to note that AI is not a magic box where data is inputted and results are automatically produced. The ability of employees to interpret data and its context is still vital. AI presents many opportunities for knowledge exploration and correlation of different parameters that may not be easily identifiable with traditional physical models. For instance, when describing viscous-elastically flow of filled polymer systems, data pattern recognition can reduce the complexity of models and computing power while enhancing the accuracy of predictions. A notable example is the mixing and extrusion process.

There are clear benefits of a more digitalized manufacturing world for people and operational excellence. Digitalization is not meant to replace human logical thinking, but rather to make processes more efficient and less dependent on human effort, thus contributing to more sustainability. It can help to reduce rework and scrap, minimize energy consumption, enhance quality, and increase output.

Digitalization is definitely also making an employer more attractive for young talents. Firstly, it promotes environmental, social, and business sustainability. Secondly, modern  working styles rely on digitalization and are an essential aspect to convince potential candidates. Lastly, by embracing digitalization, we can safeguard the proper future of our company and the lifelihood of our employees and their families.

What is the main purpose and benefit of digitalization for operational excellence?

One of the overarching goals of digitalization is to provide our customers high-quality products at competitive prices, without compromising sustainability standards. Furthermore, digitalization also enables reducing the dependence on human effort and individualism without necessarily operating with fewer workforces. As an example, while the quality requirements of our customers increase, minimizing disturbances in the processing and handling of our (semi-) products become critical, therefore digitalization is an essential contributor in ensuring process stability.

What approach are you following in the area of digitalization of manufacturing technology in your organization?

Our vision is to make manufacturing boring for 'fire-fighters' jumping from one unexpected event to another by adjusting schedules and parameters beforehand based on knowledge of equipment and material behavior. Virtual industrialization of products using simulations, virtual machine twins, and AI material models is the ultimate goal, as it provides a deep understanding of the processes and eliminates losses, human impacts, and quality compromises. Although currently, inventions are still the result of human creativity, in the future, a digitalized ‘Morphologic Box’ that evaluates all possible combinations might lead to new developments and support human beings.

What are focus areas for digitalization in your organizations and which is your digitalization roadmap?

Our focus is mainly on quality checks, process optimization and control/self-adaptation, predicting quality states and equipment faults to avoid unexpected breakdowns and difficulties during the industrialization of new products.

Our digitalization roadmap includes implementing and testing of small prototypes, evaluating them, and continuously improving them until suitable solutions are ready for roll-out. We have already gained experience with AR and VR applications, which we used during the Covid-19 pandemic when onsite support was limited. These learnings we aim to transfer onto the AI development activities.

Which are the challenges you are facing, and which are the opportunities you see in the area of digitalization in manufacturing tires?

A considerable challenge is empowering our employees for digital transformation. Although buzzwords as AI, augmented reality, and virtual reality are often used, only a few individuals understand how to apply it effectively. In order to unveil the full digital potential of the employees, a stepwise transformation process must be initiated.

The first step is to mitigate fears through explanation, ensuring understanding and guiding to smart applications. Many individuals aged over 55 are overwhelmed by the developed digital technology that is not adequately explained to them. They may struggle with the fast-changing times rather than taking the opportunity to learn. In this context, I appeal to the younger generation ‘be patient and take the time to explain the technologies in order to inspire the older generation’.

How far are you regarding the usage of AI in manufacturing tires?

The implementation of AI in the manufacturing sector has been slower; however its usage is growing fast. The primary obstacle was the needed amount of data and its availability, however, this obstacle has been overcome. After enough data became available AI has been increasingly used to analyze data, find pattern and similarities, predict performance properties, and to control processes. In particular, the modelling or prediction of viscous elastic filled semi-product behavior with AI promises high accuracy at less computer power, compared to classic approaches.

Applications in predictive maintenance are well known examples. However, for large machines such as internal mixers and gearboxes, machine-learning processes need frequent training cycles due to the low quantity of bad or critical events in relation to good results (e.g. profile extrusion with results of 99% in specification and 1% out of specification). Therefore, quite often thresholds instead of AI-algorithms are used to detect critical machine conditions. Pre-trained algorithms with expert know-how or positive events might reduce, however the time of training significantly.

“Digitalization is not meant to replace human logical thinking, but rather to make processes more efficient and less dependent on human effort, thus contributing to more sustainability. It can help to reduce rework and scrap, minimize energy consumption, enhance quality, and increase output.”

Skepticism about the application of AI often arises due to the use of ‘black-box models’ which make it difficult to understand the impact of parameters on the result. One potential solution to understand such neural network models might be explainable AI (XAI) analyzing impacts or relations of parameters backwards and offering interpretability.

How many tire plants do you have, and do you have examples of state-of-the-art technology?

Digitalization is constantly expanding in all our 21 manufacturing facilities in 17 countries. In almost all tire plants, we have installed standardized machine interfaces to acquire machine, process and quality data, and transfer them to a central data lake for evaluation. Proof of concepts (POCs) are being distributed globally to test their effectiveness. After positive results, these applications are rolled out. Some plants use these tools already in an advanced and extended mode. This means that they are drawing conclusions from the data analysis and make active decisions for their serial production (self-adjusting processes).

What were the challenges of the Covid-19 pandemic for your organization and how did it impact the progress of the digitalization for your organization?

The Covid-19 pandemic has brought many challenges and at the same time, it has also presented opportunities for digitalization. Our tire organization has rapidly shifted from digital beginners to advanced digital users. This sudden shift clearly demonstrated the opportunities of digitalization, which was not fully appreciated by everyone before. Among the development of data analyzing tools one of the most exiting digital applications are the remote installations of production lines for me. The use of gaming tools such as augmented reality (AR), virtual reality (VR), and digital twins of machines have become business relevant. These tools do not only allow the installation of such large lines but also help to shorten the commissioning time without the (extensive) need of onsite presence.

What is the big learning for you regarding the digitalization/AI?

There are at least two ways to introduce disruptive technologies: either the market or circumstances demand a change, or you have to persuade people by a compelling vision or dream. The second pathway is often the more challenging. However, the introduction of smaller prototypes throughout different areas and plants can help broaden the perspectives and shifts mindsets. At this stage, it is crucial to focus on demonstrating practical opportunities, small prototypes, rather than prematurely discussing standards. Once trust and experience have been gained, a structured roadmap can be developed to make technologies such as AI more widely available. Personally, I have learned that digitalization, and especially AI, can lead to significant improvements in production efficiency.

What will the future of manufacturing technology in the tires production look like and how can companies and employees best prepare for it?

The future of manufacturing technology lies in full digitalization and the consequent use of data to analyze and optimize processes. This does not mean that only bits and bytes matter, but it makes manufacturing more predictable when it comes to disturbances and their mitigation. Nevertheless, the plausibility checks of experts, with their extensive experience and know-how, remain essential. What should be avoided is uncontrolled human impact that could disrupt the smooth and stable production runs. The digitalization and independence of human impact also significantly supports the automation roadmap. Similar to autonomous driving, it is not the systems that are the problem, but the hybrid interactions between humans and machines.

What makes Continental’s Manufacturing Technology Engineering department a great place to work?

Manufacturing Technology Engineering is an inspiring place to work due to its practical applications of in-house developed technologies. It allows individuals to bring to life the dreams they had during their technical education, and experience firsthand the impact that technology can have on manufacturing processes.