How AI Is Making Inroads in Auto Manufacturing

June 14, 2019
Progress is slow, but these best-use cases illustrate possibilities for high benefits with low complexity.

Large automotive OEMs can boost their operating profits by up to 16% by deploying artificial intelligence at scale in their manufacturing. Despite this potential, the industry is making slow progress in taking AI from experimentation to enterprise deployments.

One of the primary reasons for this is many OEMs don’t know what use cases to focus on. But new Capgemini research reveals prime opportunities in the areas of manufacturing and operations.

We analyzed 45 AI use cases across different functions – from R&D to customer/driver experiences – to assess which provided the greatest benefits. We evaluated them based on the relative complexity to deliver a scaled solution and the anticipated benefits.

The following manufacturing-focused use cases offer high benefits with low complexity, ideal candidates for OEM AI investments:

  1. New visualization and productivity optimization options to improve Overall Equipment Efficiency (OEE) in production – Developed as part of lean manufacturing, OEE can improve manufacturing by increasing availability, performance, and quality. For systems and components supplier Cooper Standard Automotive, data integration of multiple plants means it can track OEE, safety, shipments, and more with a mobile phone. Audi is testing an AI-based system that employs smart-cameras with image recognition software to test and identify tiny cracks in sheet metal. The system can potentially detect the finest of defects using millions of images and automating visual quality inspection.
  2. Predictive maintenance for equipment to reduce manufacturing downtime (e.g., robotic arm failure) –General Motors, for example, deployed a cloud-based image classification tool on nearly 7,000 robots. This pilot, designed to detect component issues before they happened, found 72 instances of failure that could have led to unplanned production delays.
  3. Smart asset management using AI – Any asset management program relies on large amounts of data to succeed. Big Data and analytics are a significant opportunity to improve asset management, and artificial intelligence should provide the engine to recommend future plans and actions. For example, Cooper Standard Automotive pools its data to track its global plants. This gives the chief manufacturing officer a single view and assets or talent can then be moved to the right plants in real-time.
  4. Energy consumption management in plant operations/warehouses – For example, one OEM in the report pointed to AI’s increasing role in plant operations. The company does not build the same car in one plant, in one place. It replicates that part in two, and in some cases three, plants around the world. Using AI allows the company to scan the facilities, scale what it does very successfully in one plant, and replicate it exactly in another. AI provides the ability to conceptually prove its operations will fit into the new space, and still deliver parts and cars, and manage the facilities.

Identifying the most valuable use cases is one roadblock to successful AI adoption, and others include legacy IT systems that do not talk to each other, question marks over availability and accuracy of data, and lack of skills.

Based on our survey and conversations with companies that have successfully scaled AI, OEMs can propel their AI efforts once use cases are identified through:

  • Strong AI governance: Process is important to accelerate the rate of innovation. It means a company can prioritize AI investments, secure support from top management, and align expert resources.
  • Investment: Initiating AI projects requires significant resources, ranging from skills to software. OEMs will need to find ways to attract the talent and fund the projects that drive value.
  • Hiring the right talent: Skills are changing, and OEMs will need to hire AI expertise and proactively upskill employees to remain competitive. It may also lead to acquisition of AI companies to gain their talent and technology.
  • IT maturity: Accurate data and information is vital to AI. Companies doing AI well have harmonized data collection from a variety of sources, upgraded IT systems that allow for AI integration, and have IT leading the AI implementation from start to end.

Apart from improvements in manufacturing and operations, AI has enormous potential to reinvent the end-to-end customer experience. This includes repairing fragmented operational processes that impact nearly every area of the automotive value chain from how cars are made and sold to creating meaningful and differentiated customer experiences.

These efficiencies are going to fuel the innovation to compete in the future automotive industry, which will include players such as Google, Uber, and Microsoft. These savings are not about the current vehicle lineup, but the connected and electric cars of the future. Failure to take advantage of the savings provided by AI will reduce the funds available to innovate the next generation of vehicles.

Mike Hessler is the North America Automotive and Industrial Equipment Lead at Capgemini, a global leader in consulting, technology services, and digital transformation.

About the Author

Mike Hessler

Mike Hessler is the North America Automotive and Industrial Equipment Lead at Capgemini, a global leader in consulting, technology services, and digital transformation.

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