Strategic autonomy: digitalization & value chain integration

Cyber Physical System

TNO focuses on unlocking data through a so-called digital plug, modular data architecture, cyber security, and AI applications that bring together the models and processes of a manufacturing company in a cyber physical system, or ‘digital twin’. The challenge is to build a digital replica of an industrial process that is so good that companies can see in advance on the screen what an appropriate solution would be, and then base their decisions for the real process on that.

Future-oriented approach – digital twinning

The added value of a digital twin depends largely on what a company wants to achieve with it. Creating such a digital replica only makes sense if it generates new insights that contribute to the intended goal. Is that actually the case? Then TNO will deploy the following three components of the digital factory to help a company move forward:

1. Structuring: during this consultancy phase, TNO specialists will look at what is needed to get the clearest possible picture of the digitalisation approach.
2. Specifying: what machinery and software will be needed to achieve the desired production method? And how should they be installed to get the best results?
3. Vision: here, we look a little further into the future, painting a picture of what an ideal autonomous factory would look like. In other words, a concept study, with the great advantage that it also immediately formulates future requirements, so that they can be taken into account at an early stage.

Turning digital knobs

Is the goal to make as many products as possible in as short a time as possible? In this case, a different approach is needed than when there is a strong focus on quality. This is where being able to turn some knobs beforehand in the digital world of the digital twin can be useful. Without too great an investment, this will quickly reveal which adjustments will lead to the desired production process, while also immediately revealing, for example, the impact on the degree of robotisation, efficiency, lead time, and costs. And these are all areas where companies want to have as clear a picture as possible before making the necessary investments.

Smart data sharing

Those who want to produce competitively must design their production process flexibly and efficiently. This requires smart combinations of data from machines and ICT systems. But it is not just about collecting data. You need to also be able to share the data securely and confidently with partners. A significant challenge is making solutions scalable. We make this possible with data sharing. The result is a ‘smart supply network’, a network of flexible and connected factories whose application is not limited to one company or a few machines.

Improving production processes

With data sharing, we are working on solutions to share data within or between factories. Applications include better collaboration between clients and suppliers and achieving smarter production processes. Think of improved production planning, optimised production scheduling, or overall equipment effectiveness (OEE) improvements that consider machine availability, output, and quality delivered. But which also prevent errors and reduce maintenance costs. We rise to the challenge of making this happen, even when different machine or software vendors are involved.

It is important that manufacturing companies are in control, so that each party can decide who gets what data. Technologies such as the European International Data Spaces (IDS) and new cloud technologies such as GAIA-X play an important role here.

Blueprint for data sharing

In many applications where data is collected, analysed, and used, we see that companies have to cross a threshold. This is why it is important to work together with the technology provider. Smaller manufacturing SMEs do not always have the same resources and options as larger companies.

Therefore, more standardisation is needed. We are working on a blueprint for data sharing in the manufacturing industry. This blueprint will provide manufacturing companies with an overview of standards and technologies for their technology suppliers. But it will also help suppliers; by standardising interfaces, they can expand their market and deliver new innovative products faster.

Artificial Intelligence and real-world challenges

One of the key technologies that will need to be strategically deployed concerns data and AI. A technology that requires radical change and innovation in the primary process of the entire manufacturing value chain. Manufacturing companies must now anticipate how to adequately unlock data in production processes to harness the power of AI applications. This can be achieved with the deployment of a so-called ‘digital plug’ to make the data interoperably accessible and applicable in a digital twin.

Real-time insight into the stage of the production process, continuous analysis to identify potential bottlenecks, and specific instructions to help human operators run the process optimally. And that is just a fraction of what is already possible thanks to robotics and digital technology. After all, these are real-world challenges and real prototype solutions.