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Before the Internet of Things emerged, automobile data was highly limited and hard to gather. For manufacturers, informed decisions were difficult to make — and it was nearly impossible to act proactively. 

Fast forward to today, however, and automakers have every reason to celebrate. The emergence of connected cars has taken the world by storm, and the future is all about harnessing this potential goldmine of data.

Connected automobiles have the computing power of 20 modern PCs and process up to 25 gigabytes of data per hour. By 2020, 80 percent of all new vehicles will have data connectivity capabilities, and at that point, Gartner predicts that there will be nine times more connected cars in the world than there are today. 

The momentum is building, and before we know it, the world will be full of connected vehicles. As we creep toward this reality, manufacturers are beginning to learn about the tremendous opportunities they now have to gain insight into countless underexplored realms, such as driving patterns, warranty claims, inventory management, maintenance optimization, fuel consumption, and customer segmentation.

As manufacturers scramble to position themselves to create value in this up-and-coming connected chain, they’re also realizing that they need robust and reliable tech infrastructures that can automatically separate the signal from the noise amid billions of sensors. Otherwise, they’ll waste infinite amounts of time and money retroactively analyzing data that’s likely outdated or irrelevant. 

Automation in Predictive Maintenance

The integral characteristic of our impending IoT-driven future — or Industry 4.0 — will be automation. The countless actuators, sensors, and machines and the gargantuan amount of data generated by today’s and tomorrow’s automobiles necessitate cognitive analysis abilities

Manufacturers must utilize algorithms that automatically infuse data-driven insights into a factory’s workflow in real time. This is the only way to continuously and consistently identify the right signals from all the noise and constantly glean actionable information.

Here are three ways cognitive predictive maintenance will make connected cars a data goldmine for auto manufacturers:

1.Leveraging Predictive Maintenance: Pre-IoT, automobile maintenance mainly revolved around predetermined schedules and milestones — not real-time data. With this outdated approach, mileage and time were essentially the only two factors that determined whether a vehicle was due for service, resulting in unnecessary visits to the mechanic, potential issues going diagnosed between appointments, and minimal useful insights for manufacturers.

With sensor data from connected cars, however, automakers can constantly assess the performance of any and all of their parts in real time. This opens the door to a predictive maintenance approach, where trending issues are monitored and addressed as they happen, limiting the fallout from large-scale recalls, minimizing unnecessary wrench time, and potentially saving lives in the process.

2.Creating Receptive Supply Chain Networks: Customer service and speed of delivery is where tomorrow’s best-in-class automobile manufacturers will set themselves apart — and that begins with optimizing the supply chain through data automation. According to Cisco, the manufacturing world is poised to save $2.7 trillion by improving this area.

The issues illuminated by predictive maintenance will have a ripple effect on the supply chain that must be addressed as early as possible in order to avoid costly delays in production. Through IoT data, manufacturers can monitor and control material consumption in real time throughout the entire chain. With this approach, there are no surprises. All issues are addressed before they become a reality, helping reduce waste and raise overall customer satisfaction.

3.Optimizing Shipping and Showroom Inventory: In an environment where road and sea shipping costs are high and safety risks are top-of-mind, deciding how and when manufacturers deliver the finished product to showrooms is a hot-button issue that data science can help optimize — both on micro and macro levels.

Similar to predictive maintenance, IoT data can be leveraged to stock showrooms on an as-needed basis rather than reactively or on a regular basis. Further, even the actual shipping process can be optimized through data science. The Michelin Group, for example, analyzes data from tire sensors to coach truck fleet drivers on how to save fuel.

With the deep, holistic, real-time insights made possible by cognitive predictive maintenance, every aspect of an automaker’s operations can be connected through the IoT and leveraged into smart, efficient decision-making. 

Operational costs and efficiencies are what separate success and failure in the auto industry, and those who capitalize on smart data analytics are best poised to thrive in the future.

Sundeep Sanghavi is a highly accomplished data junkie, innovator, and entrepreneur with more than 20 years of experience using data as the currency to perform advanced analytics. Throughout his career, Sundeep has learned to productize data in day-to-day business workflows, which has led to multibillion-dollar savings. Sundeep founded Razorsight, a leading provider of cloud-based analytics solutions for communications service providers. Under his leadership, Razorsight experienced 11 consecutive years of growth and raised more than $30 million in venture capital before its acquisition by Synchronos. Prior to Razorsight, Sundeep served in management roles at industry leaders, including Cable & Wireless Worldwide and Arthur Andersen. Known for his what-if mindset, he co-founded DataRPM to tackle the business problem of maintenance inefficiencies in industrial IoT. Sundeep and his co-founders, with deep experience in data science and machine learning, have set out to automate data science for predictive maintenance, going back to using data as currency.

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