It’s full steam ahead in the race for CASE (Connected, Autonomous, Shared, Electrified)—or is it now ACES?
While the industry seems aligned on this point and the massive transformation that it entails, the forecast and timing horizon is less obvious. For example, “electrification” is underway, but there is no clear technology winner in this space. Will it be battery, electric, fuel cells, hybrids—or all of the above?
Similarly, there are many visions for the future of connected and autonomous vehicles and the emerging business models, but a dominant view has not yet emerged. The result is that OEMs, suppliers, technology providers, and others in the changing automotive ecosystem must remain flexible enough to quickly react/respond to changing conditions; adaptable enough to provide a variety of solutions to the market; and they must partner to ensure all relevant capabilities are brought to bear.
Oh, and by the way, while doing all this planning, investing, strategizing, and transforming for the future, automakers still need to design, build, market, and sell great vehicles to their customers.
This reality is especially impactful in the manufacturing area. New concepts in machine learning, artificial intelligence (AI), Big Data, and predictive processes present manufacturing leaders with new complexities and opportunities as they move from traditional manufacturing processes (Mode 1) to intelligent manufacturing (Mode 2) capabilities, all while maintaining current production volumes, quality levels, costs, and timing.
The ability to operate in this bimodal manufacturing environment allows intelligent manufacturers to build vastly different components and assemblies within a confined, integrated, and flexible environment. Intelligent manufacturers can build traditional capability products alongside those completely outfitted for future transportation models and associated digital services.
The road to intelligent manufacturing involves digitizing operational processes from the shop floor to the top floor. There is data and process consistency—and more importantly, small lot size flexibility—to determine what to build and where, how, and when to build it. The use of a digital twin reduces expensive prototyping activities and accurately conveys information across all manufacturing stakeholders and suppliers. Devices and microservices connect to this intelligence network, allowing complete control and adaptation of manufacturing operations anytime and anywhere.
For additional insights and information on the path to the Intelligent Enterprise, please join me at the fourth annual SAP Best Practices for Automotive event, where industry thought leaders and influencers will share their insights and experiences in moving towards the Intelligent Enterprise of the future.