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Taming Complexity, Variation in Automotive Engineering with PLE, Digital Twin

Cathy Martin
By Cathy Martin Vice President, BigLever Software

Automotive engineering has never had so much complexity to address. Producing millions of vehicles per year is a daunting feat. This complexity grows exponentially with the introduction of next-generation intelligent features and functions for enabling autonomous driving. Manufacturers are facing myriad challenges in taming this complexity, managing variation and bringing these innovations to market rapidly and efficiently, while providing the highest levels of safety and reliability.

From autonomous driving and advanced safety systems, to adaptive cruise control and lane-keeping assist, innovation is in overdrive. Autonomous vehicles require an intricate web of interacting software, and electrical and mechanical parts to enable radar-based sensors, 3D mapping, image recognition, vehicle-to-vehicle communications, artificial intelligence and more. Automakers and suppliers are being pushed to the edge of their capability. Engineering teams are increasingly consumed by the mundane tasks of managing this mounting complexity.

There is an extraordinary need and opportunity for dramatic improvements in the way automotive product lines are engineered, delivered and evolved. Traditional “product-centric” approaches—where individual products within a product line are designed, produced and maintained separately—are simply no longer viable.

Some manufacturers are now transitioning to feature-based Product Line Engineering (PLE), an emerging discipline that provides a unified variant and complexity management approach across an entire product line, and across the full engineering and operations lifecycle.

This approach is called “feature-based” because it allows vehicle engineers to start the requirements and design process by considering features first. With feature-based PLE, features determine the parts, rather than parts determining the features. This gives everyone across the enterprise a common language.

While the approach has been adopted across industry sectors, the most leading-edge PLE deployments are taking place in the automotive sector. Manufacturers who adopt PLE are reporting order-of-magnitude improvements in efficiency, time-to-market, product line scalability and product quality.

Along with the dramatic increase in engineering and manufacturing complexity, automakers are now challenged by the added complexity of maintaining and evolving vehicles once they leave the factory. Digital twin technology is another area of innovation, in addition to PLE, to help address this issue. Gartner identified digital twins as one of its Top 10 Strategic Technology Trends for 2018, and an article in Forbes mentioned that, “All indications seem to predict we are on the cusp of a digital twin technology explosion.

In automotive, a digital twin—the digital image or virtual representation of a physical product—is used to maintain a connection with each vehicle as it goes out into the field.

Digital twins are by-products of the feature-based PLE process, which automatically generates a digital representation based on the features contained on a specific vehicle. Once a vehicle is manufactured, and is ready to leave the factory floor, the digital twin is also complete. Manufacturers can effectively monitor, maintain and evolve the vehicle in the field based on the specific features contained in that vehicle. For example, when the vehicle is serviced, or certain parts are replaced, that information is incorporated into its digital twin to ensure the mirror-image is maintained.

With digital twins, quality issues are much easier to handle proactively, since companies can quickly identify which products have a particular feature found to contain a defect and fix it across all products that contain that feature.

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