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How the Automotive Industry Can Monetize Digital Transformation Investments

The benefits of digitization in the Automotive industry start at the connected products and operational technology layer, according to Schneider Electric

How the Automotive Industry Can Monetize Digital Transformation Investments
How the Automotive Industry Can Monetize Digital Transformation Investments

According to the analyst firm Research and Markets, automotive industry investments of $19.57 billion US in 2015 are expected to increase to $82 billion by 2020 as a result of growing digitalization and technology advancements. Innovations in electrification, supply chain digitalization, connected vehicles, autonomous driving, and shared driving are all offshoots of this greater digital transformation trend. The speed of the automotive business demands is increasing — real-time smart controls are needed to improve areas such as safety, efficiency, reliability, flexibility and availability, to reduce environmental risk all the way through to improving profitability. Although these changes open the door to new business opportunities, some significant challenges need to be overcome in order to capture those opportunities.

Within the automotive industry, digitization trends are all encompassing. They will impact each stage of the automobile design/build/operate and support process, spawning many terabytes of data. For example, a new generation “connected” and autonomous car will produce 50 megabytes of data each second. That’s, on average, 20 gigabytes of data per day when on the road. In order to exploit and monetize all of that data, IT systems across automotive enterprises that gather, process, analyze and store that data, will need to be re-architected and reconfigured.

Traditionally, most car makers are engineering-focused organizations. In this new world of mobility, a cultural shift to a more marketing and services-driven business model is required. In many cases the financial subsidiaries of these auto makers are taking the lead in the development of these services. Companies like Daimler and BMW, for instance, are undertaking joint efforts to develop new mobility services.

In order to facilitate these business model changes and the new influx of data, the various pockets of IT assets across centralized data centers, warehouses, manufacturing sites, R&D facilities, regional sales divisions and local dealerships will need to be reconfigured and expanded.

To ensure the data generated are leveraged, auto makers are motivated to make such IT infrastructure investments, for several reasons:

• Additional revenues – By analyzing in real time and cost-effectively the huge amount of data continuously generated by the connected cars on the road, they will be able to generate customized and differentiated mobility services, quickly adding to profits and revenues.
• Traceability – By enabling better traceability of parts and tracking of vehicle performance in the field, potential auto recall impacts will be lowered. This will result in faster repair cycles, enhanced brand image and increased customer experience.
• Value-chain digitization – In order to evolve to smart manufacturing, automotive companies will implement new digital solutions in supply chains, production and processes, thereby enhancing productivity, safety, and quality. This will empower the workforce and enable management teams to perform lean manufacturing. The data generated on a daily basis will be continuously monitored, leveraged and benchmarked.
• Sustainability and energy consumption – Traditional data centers consume lots of energy. In the new “big data” environment, it will become more important to control data center-generated energy costs. New scalable power and cooling technologies allow for both much higher energy efficiency and higher density computing in each rack. In addition, new generation data center infrastructure management (DCIM) software systems helps to bolster data center performance by enhancing systems uptime. This improves systems availability and lowers maintenance costs as problems can be identified before an unscheduled downtime occurs.