We welcome you behind the scenes at Kugler Maag Consulting: Our magazine Kugler Maag Concepts gives you an insight into our ideas workshop. In each issue of Concepts, we introduce you to a future topic that the process experts at Kugler Maag Cie are working on. Read today what will impact the automotive industry tomorrow.
Come and talk to the concept crew of Kugler Maag Cie
Change all over the car industry: automated driving, connectivity in a system of systems, digital services, artificial intelligence, new mobility concepts, and electric vehicles – feel free to add your own project requirements to this list of newcomers. Planning and decision-making are like a guessing game where the only thing you can rely on is that the car will have wheels. How should Jane or John Doe, the average project engineer, or even a departmental head, cope with all this overload?
Well, you won’t be surprised to hear that no-one at Kugler Maag Cie has mastered this new conjuring trick either. There’s no such thing as a silver bullet. It remains a fiendish problem, or to use the words of Berkeley scholar Horst Rittel, »wicked.« German speakers know what he means when he refers to »ill-defined planning problems.« To respond to the challenges facing the industry, we take an in-depth look at integration. Of course, we don’t pull a catch-all solution out of the hat. But we do address the key challenges of each issue and ask what the real requirements are. It’s no mean task and we’re nothing like finished yet. At the same time, we’d like you to be part of the solution. That’s why we’re starting with this workshop report.
And there’s plenty to report. With each issue of Concepts, you’ll gain a more complete picture. In this first issue, we introduce you to »Automotive integrated Development«.
AiD is, in short, the backbone of our methodology – an approach that allows development tasks to be aligned from the initial concept down to each fine detail, so practitioners will find it useful. AiD is even designed to handle conflicting requirements faced by engineers – process maturity, innovation, responsiveness, functional safety, cybersecurity, usability, to name a few. And let’s not forget robustness, because the entire vehicle should interact reliably with vehicles specified elsewhere. Does this pique your interest? Then you should get to know AiD.
Spoiler alert! In the next issue of Concepts, you will discover how we integrate our machine-learning process model with AiD. This approach will also be demonstrated on a real customer case. As the number of challenges faced by industry grows, so does AiD.
For the SW-defined car, the integration of complex systems is key. Engineering must come together, notably mechanics and electronics, the latter of which is increasingly moving towards new software architectures. As the solutions become more complex, so too do the requirements in terms of system integration. Traditional approaches are no longer fit for purpose or must be expanded to accommodate additional requirements.
One very promising solution is systems engineering. This methodology combines both the process and product perspectives as part of an integrated development approach. It enables users to manage their work more transparently, especially when combined with our Automotive integrated Development (AiD) blueprint, which we introduced in the last edition of Concepts. AiD joins forces with systems engineering methodologies for a complete Automotive Systems Engineering approach, which identifies all functional and non-functional requirements and ensures each task is scheduled and all dependencies are transparent.
So far so good, but integration is not just a macro-level requirement; additional steps in this direction are also needed within each individual engineering domain. A good example of this can be found in software engineering. Traditionally, developers program every individual task that the software performs. But Machine Learning (ML) has changed all that. Instead, tasks are described in abstract terms and learning algorithms trained to perform them. In other words, the software writes itself. Traditional approaches to software engineering now need to be adapted to accommodate and validate software artifacts generated by ML. Read on for our interview with our Machine Learning Engineering experts to find out more.
Open source software is a potentially valuable tool in developing the complex solutions required by connected cars. Will the new up-and-coming vehicle architectures see OS used more widely? To find out the answer to this fascinating question, we surveyed a group of experts. You can read the results in our industry barometer report Automotive FLOSS. State of Practice 2021.
//More on FLOSS
Integration can feel like a juggling act. But don’t worry. To help your team leads keep all the different balls up in the air, we’re currently developing an intensive training program in Automotive Systems Engineering.
We hope you enjoy reading this edition!