November 29, 2017 | Written by: Kal Gyimesi
Categorized: Automotive | Blog
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A far-reaching transition is underway in the world of complex product engineering. Entirely new development systems, agile methods and the introduction of cognitive, AI-driven analytics are dramatically improving how IoT-enabled vehicles are brought to market and updated through their lifecycle. Consumers want the connected products they interact with to not only work flawlessly, but to be updated with new capabilities for as long as they’re using them.
Automakers and their partners are using IoT to improve performance, safety, and development processes.
Automotive companies in particular are striving for leadership in several interdependent areas, where requirements are evolving rapidly even as capabilities are being perfected. These include:
- Development of new cloud-based information services
- Cybersecurity in connected and autonomous vehicles
- Alternative propulsion systems
- Improved activity safety
- Racing toward autonomous driving
- Differentiating the in-vehicle experience
So, is that enough on their plates? Only a fraction of this was in play a decade ago. And it requires dramatic changes in how cars are being developed. In the premium segment, vehicles will have 10s of millions of lines of code that must be verified, tested and maintained in order to produce the most exciting products on the planet. Soon, the days where your 10-year-old vehicle that is frozen in time from the days before smartphones will be behind us. And cars will be updated with new capabilities just like smart computing devices.
To help you understand all these changes, we’ve written a series of articles about the various ways that product engineering in general, and the automotive industry in particular, must adapt to the new methodologies.
Part 1: Transitioning to Model-based Systems Engineering
The overall approach to the discipline of engineering is experiencing a long transition to model-based systems. These require deployment of new software systems and processes, particularly in the area of systems engineering. Though they have used CAD models for many years to document designs, even the most sophisticated auto manufacturers have relied on text- and document-based systems when developing functional systems and product architectures.
Text-based systems are exactly what you probably are envisioning. They include information that is input in text by engineers during development. Engineers have long utilized many of the same basic tools that are a staple throughout the corporate world: MS Office, Visio, Wikis and other text-based documentation systems to build incredibly complex products.
Process diagrams may be drawn with flow figures, lines and arrows that remain static until manually re-drawn. Other schematics, photos and development collateral may be available as well. Even companies that have done a good job with repositories and tagging still have issues with the efficiency of product development. Cars and planes will have lifecycles that are decades long. As engineering teams work on the various systems described above, how will they make sure that functional interdependencies aren’t affected by changes in one sub-system that may affect others?
This is where model-based systems engineering (MBSE) comes in. (MBSE was originally developed by the International Council of System Engineering (INCOSE), which provides a nice primer on why it makes sense.) In mid-November, IBM Watson IoT’s third Continuous Engineering Summit brought together engineers from several industries, including Automotive, to share innovation on excellence in product development and lifecycle management. Among the presentations was a discussion led by Combitech AB/Saab’s Johan Gunnarsson. He suggested that the change to MBSE will be so dramatic that automobiles are fast becoming as complex as fighter jets.
Johan Gunnarsson of Combitech AB/Saab at the 2017 CE Summit
MBSE-focused engineering software such as IBM’s Rhapsody provides the basis for developing domain models that become common communication tools among engineers. It allows the entire system to be simulated to understand interdependencies between sub-systems and components. This is particularly helpful when developing complex products like vehicles built by OEMs through a multi-tiered network of suppliers.
The transformation in systems engineering addresses the need to efficiently deal with product variants as consumers want vehicles that can be personalized both physically and digitally. MBSE also helps address managing changes to both vehicle hardware and software throughout its long life.
To find more information about all these trends and about IBM’s suite of Continuous Engineering solutions, please visit our landing page. And we invite you to join us at Think 2018, March 19-22 in Las Vegas.
Next week: part 2 – speeding product development with the Scaled Agile Framework