Exciting technological advancements are taking the automotive world by storm. Learn how artificial intelligence and IoT are creating the cars of tomorrow
The average manufacturer deals with around 800 hours of downtime annually, meaning almost $1 million in lost revenue each year. Further, product recalls can be costly; for instance, in 2018, we saw Tesla recall 123,000 Model S cars. After the company’s recall announcement, shares fell 4%.
Now, more manufacturers are trying to take a proactive approach to prevent situations like the above. And with advancements in cognitive learning and the proliferation of vehicle sensors across production lines, we are entering a new stage of predictive maintenance.
You’re likely already aware that the Internet of Things (IoT) and artificial intelligence (AI), a technology that uses data and algorithms to replicate human decision/thinking ability, is not being implemented in the automotive industry as quickly as other industries like financial services, retail, and healthcare.
Yet, these technologies offer scores of benefits to the industry as a whole.
Let’s take a look at a few.
Some of the most common usages for AI in automotive manufacturing include design, supply chain, production, and post-production. In addition, AI is also being implemented in control units and driver assistance and driver risk assessment systems, which are providing new ways for drivers to monitor their performance on the road, increasing safety for drivers across the board. Finally, aftermarket services such as predictive maintenance and insurance are also transforming through the usage of AI and growing in scale.
AI is helping robots work with humans and learn automotive manufacturing skills like design, part manufacturing, and assembly. Currently, AI helps humans to develop cars using Exoskeletons, which helps improve the productivity of the plant by decreasing the use of the human worker’s waist and lower body muscles by 80 percent (which reduces fatigue). Further, AI-powered material handling systems can navigate autonomously around a manufacturing unit.
A revolutionary force in the transportation segment, AI is presently playing a crucial role in driver assistance technology and is commonly used in modern vehicles. Now, software is continuously being updated to improve performance and the safety of the car. Further, AI is used to calculate real-time risk assessment, helping drivers avoid potential emergency situations.
Today, many AI applications predict problems related to engine performance, battery performance, and more that may occur in the future. This type of predictive maintenance uses historical service data (like service orders, IoT-enabled data, and technical manuals) to repair and maintain connected vehicles.
Move over, electric vehicles – AI is expected to boost the demand for autonomous vehicles in the next eight to ten years, with roughly 80 million autonomous cars being sold by 2030. Further, the market is expected to witness a CAGR of eight to ten percent from the present day to 2030.
Currently, the application/adoption of AI in an autonomous vehicle is in a nascent stage and mainly used in parking assistance and partially automated driving applications. However, in the next ten years, AI technology in the automotive industry is expected to reach maturity, with around 85 percent of vehicles being (to some degree) autonomous cars. As AI is the backbone of autonomous driving, it only makes sense that today’s car companies are starting to implement the technology.
Most manufacturers are no stranger to IoT-connected cars and devices, which are the key to reducing downtimes, reacting more quickly to market changes, supporting remote operations, introducing new business models, and creating a better customer experience.
Yet, there are many challenges in fully enjoying the benefits of the IoT. Some of these barriers include figuring out how to wrangle heterogeneous systems and application landscapes or determining which functions (like supply-chain management) need to be supported by specific applications and technical systems. Further, where should these systems be deployed? The list goes on.
Business, Organization & Technology, Oh My!
The first step of scaling the Industrial IoT involves identifying, prioritizing, and rolling out use cases using both top-down and bottom-up approaches. Use cases should be prioritized by financial impact and how easy implementation will be. While some use cases will be unique one-offs, others will be replicable at least once, and some, potentially, multiple times. Further, a roll-out plan should be created that is clear, occurs in phases, and imagines impact at each phase.
When it comes to the organization, a plan to scale IIoT should consider performance, capabilities, and culture. First, a team should be put in place that will monitor progress toward clear target values for transformation. Second, changes to organizational structure may need to be made. A framework should be established that includes a common governance model, harmonized processes that link IT and OT, and centralized data and security management.
In this stage, organizations need to design the platform, enable the cloud, and build the ecosystem. The platform design is chiefly focused on creating the future target architecture for IIoT. The cloud should also be utilized, as it provides access to new AI and machine-learning algorithms and links to new products and services. Finally, the ecosystem should include a solid platform on which to create and manage applications, run analytics, and store and secure data in order to generate value from an IIoT ecosystem.
With more than 30 years of experience in the Automotive Industry, the Stefanini Group combines operational excellence, agility, and fresh thinking with our services and solutions to transform traditional operations into smart digital operations while generating outstanding business outcomes. With our pool of unique talent, we are leveraging innovative methodologies and technologies to sustainably capitalize on the real benefits of the DevOps Strategy.
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