Technology Predictions for 2021 - Further Digitalization of Quality Management

Matthias Grossmann, Vice President Strategy & Digitalization, Formel D
Matthias Grossmann, Vice President Strategy & Digitalization, <a href='' rel='nofollow' target='_blank' style='color:blue !important'>Formel D</a>

Matthias Grossmann, Vice President Strategy & Digitalization, Formel D

Quality management in general is about ensuring that an organization, product or service is consistent. In times of enormous changes, both in terms of market drivers and products, quality management faces tremendous efforts to adapt, especially in the automotive industry. We as Formel D focus exclusively on this industry with our quality services and work with our clients as partners to secure and improve their product and process quality. The key technology trends & predictions for quality management follow directly the changes the industry is confronted with: the move to electric vehicles and the push for further development of autonomous driving components but also the pandemic and the resulting changes in our work life.

Each of the four main components of quality management – quality planningquality assurancequality control and quality improvement – is faced with its own changes. Our goal as Formel D is to actively develop solutions for each of them.

Prediction 1 – Further Acceleration of Digitalization: A major key will be an even faster push to further develop digitalization concepts for all four areas. Teams will continue to work remote and data is being gathered through new input channels. Therefore new platforms that utilize as much knowledge as possible from within and outside the company but are specific to the competence field will be important to stay or get ahead of the competition. We have seen that project management evolved in the last years which will require also systems that can support these new methodologies to track progress and quality.

Prediction 2 – Artificial Intelligence (AI) will be used more and more: This is especially true for quality planning, -assurance and -improvement, where AI will assist in pushing further (enhancing) process stability as well as improvement goals.

As an example, we use AI to analyze motion data from vehicle test drives in order to a) determine the quality of the test plan, b) improve the quality of the execution of the plan and c) reduce the actual miles driven, as the results can be used to virtually test certain ADAS components.

Within the quality control space more and more process data is collected and can be analyzed to find patterns to optimize processes. This also leads to opportunities for further changes, especially when companies need to produce new high quality components like new batteries or when safety relevant ADAS components like LIDARs need to be ramped up quickly to perform at the high levels of quality. The new challenges require new tools to drive quality. However, the traditional processes can benefit from the new technology as well. Using the data pool available with the knowledge that can be either built into the AI engines or trained into their decision making engines, better and more stable processes can be developed and established.

By using data that generations have developed in a structured way through categorizing work related documents and their content, we are ensuring that improvements can start from the most efficient level and can be communicated to any production site worldwide we work at efficiently through our digital platform.

Our outlook beyond 2021:As these systems will mature in the coming years and their usage will show consistent returns, we are expecting them to eventually cross also the boundaries of companies and use data that currently would not be accessible.