Digital Disruption: Shaping the Future of the Auto Industry
Published on : Monday 30-11--0001
Automakers have been keeping a steady pace of technology innovation and manufacturing excellence for over a century. Since the breakthrough of the highly efficient assembly line, auto manufacturers were in the forefront of engineering innovation, designing and building cars that were successively better, safer and cleaner. For many decades, the industry has been at the centre of the US economic development, and, to many, an industrial and social icon.
But over the past decade or so, the iconic and seemingly stable industry has been in turmoil. It has been undergoing massive changes caused by the cumulative effect of rapid technology innovation, disruptive business models, aggressive new competitors, and an emerging supply chain ecosystem whose full impact is not fully comprehended yet.
One of the most profound changes the auto industry is grappling with is the emergence of connected and autonomous cars. Most industry visionaries and practitioners portray a bold vision of a future in which cars, occupants, and cloud-based information and control systems communicate and exchange information in the omnipresent Internet of Things cloud. Cars are becoming part of the Internet, or, in today’s parlance, they are yet additional, if unconventional, “things” in the Internet of Things (IoT).
Connected to your mobile device, a digital infrastructure, and a wealth of streamed cloud-based content and services, your car isn’t a just car anymore. It is an information centre; it’s your wallet; it’s your office on wheels. Cars are no longer self-contained independent systems designed to insulate and shelter you from your surroundings; quite the opposite. Cars in the future will be constituents of a broad network and a conduit of a continuous torrent of information and services delivered by a fast-growing vibrant ecosystem.
Connected Cars Stress Product Development Tools
More than any technology revolution underway today, the connected car, and, eventually, autonomous driverless cars, pose unprecedented challenges for automotive designers and engineers.
Of course, embedded software in car isn’t new and has been used in cars for several decades. The first production car to incorporate embedded software was the 1977 General Motors Oldsmobile Toronado, that employed an electronic control unit (ECU) to control spark timing, and starting continuous innovation to fuel economy and reduce emissions.
Developing simple control software in the earlier days of software-controlled automotive systems wasn’t a very onerous task, and informal methods and basic software skills were quite adequate. As appetite for more complex software-controlled systems increased, designers managed to get by using a rudimentary software engineering environment built around open-source configuration management and bug tracking tools, and augmented by the unavoidable plethora of spreadsheets and lengthy informal email conversations.
Nowadays, software-driven functionality is expanding quickly from controlling mechanical subsystems to serving a central role in defining customer experience and driving product differentiation. Key functions and user experience features are implemented in software, enabling unique, finely-tuned features to satisfy different consumer segments and diverse tastes.
The sheer volume, number of variations, and unprecedented complexity of embedded software is straining the organic ability of OEMs and suppliers to reskill and ramp up critical embedded software development capabilities.
The obstacle isn’t in writing the software code itself. As companies adopt agile development methods and use modelling tools that generate quality code at a click of a mouse, much of the coding has become automated. Rather, managing the seemingly infinite number of features and feature-combinations, and validating the design against an ever-growing volume of complex and interdependent requirements is a monumental development burden, stressing the traditional product development methods and tools that have not kept pace with the increased complexity.
And the gap in between the pace of innovation and increase in product complexity, and the capabilities of available design and validation tools continues to widen.
On the Road to Autonomous Vehicles
As autonomous driving capabilities continue to progress towards higher level of autonomous operation, the role of electronic control systems that host advanced software-driven functionality, especially for advanced safety systems (ADAS) and autonomously-driven cars, is expanding quickly. The need to incorporate additional sensors and increase the density of electronic controllers, while struggling with physical space, weight, power consumption, and costs is driving innovation and experimentation with new controller architectures and renewed motivation to move to 48V power rails.
As designers of semi- and fully-autonomous vehicles systems rely increasingly on nondeterministic, artificial intelligence-based control systems, traditional prototyping and physical testing are no longer feasible, nor are they sufficient. As the number of theoretical test scenarios is exploding beyond control, simulation and test tools must evolve from physics-based modelling and analysis to behaviour modelling, allowing new vehicle designs to be “driven” millions of virtual miles before a system can be validated and signed off.
Siemens PLM’s Acquisition Spree
Siemens PLM has a long history of providing product development and engineering software to the auto industry and industries that share similar lifecycle characteristics, such as heavy equipment and industrial machinery. The company recognises the transformation its customers are undergoing requires new tools and methods to support the new challenges in product development and lifecycle management.
Over the past five years or so, Siemens PLM has made several well-placed acquisitions, targeting the more pressing areas in automotive product development: software, electronics design, and simulation. Siemens acquired test and multi-discipline simulation software provider LMS, which supports model-based systems engineering. Later it added CD-adapco to bolster its engineering simulation capabilities.
Siemens also acquired Polarion’s application lifecycle management (ALM) solution to help auto companies manage software development as an integral part of product development.
Two very recent acquisitions: Mentor Graphics and TASS, are particularly important and indicative of Siemens’s aspirations and intent to maintain a leading role that is responsive to the always-evolving needs of the automotive industry.
Mentor Graphics gives Siemens additional tools to address the needs of designers of high-density embedded electronics through a rich array of circuit and PCB design tools as well as comprehensive simulation capabilities.
TASS, Siemens PLM’s most recent acquisition, adds to the already diverse portfolio of simulation software tools designed to support the complex simulation needs of ADAS and autonomous vehicles development.
The Road Ahead
Siemens PLM has arguably the most broad and diverse portfolio of engineering and product lifecycle software tools among its peers, supporting a braid of range product lifecycle activities, from requirements management, to design and engineering, to digital manufacturing planning and factory automation, and to service lifecycle management.
The rapid rate of acquisitions of functionally-rich companies, the continued evolution of product development methods such as model-based engineering, and the overall neck-breaking pace of new automotive development that calls for frequent reassessment of the portfolio, is undoubtedly a major task for Siemens PLM strategists.
Siemens was not always articulate enough in telling the product lifecycle management story in a way that highlighted the combined strengths of the individual portfolio tools. The promise of PLM and the digital thread connecting the different phases of the product lifecycle didn’t always come through well enough. It will be interesting to see how Siemens integrates the acquisitions it made over the last couple of years under a uniform, open, standards-based framework.
Granted, the challenges facing OEMs and suppliers are more involved and far-reaching than can be resolved overnight simply by introducing new engineering software tools. Automotive companies that grew up on “old” development methods need to revisit deep-rooted concepts and reexamine decades-old workflows in order to take advantage of the promise of the rich PLM environment.
Automakers must enhance their capacity and the capacity of their extended value chain partners for better product lifecycle decisions by adopting digitalisation as a cornerstone strategy. They should broaden the use of PLM software and maintain a digital thread that connects value chain functions and engineering disciplines to create a rich multidisciplinary decision-making context throughout all lifecycle phases. A Digital Twin of the vehicle that provides a cohesive and complete view of product lifecycle information will encourage broader and pervasive adoption of PLM-enabling technologies and processes throughout the extended enterprise.
Judging by the recent acquisitions, Siemens is focusing on the right growth areas. Similar to previous acquisitions, Siemens is committed to integrating user interfaces and workflows, improve application interoperability, and create a unified, seamless PLM environment to enable the digital enterprise built on the company’s Teamcenter backbone.
This article was sponsored by Siemens PLM
Pix1: The 1977 General Motors Oldsmobile Toronado was the first to employ an electronic control unit (ECU).
Pix2: Embedded software is driving new opportunities in the automotive industry. Photo credit: Siemens PLM
Pix3: In tomorrow's autonomous vehicles everyone will be a passenger.