Tag Archives: Big Data


Technology is crucial in most industries to advance safety and efficiency. The automotive sector is an excellent example of how advanced technology transforms products and, thus, the world. Big data and analytics have become an integral part of it all. So, how exactly has the automotive industry taken advantage of analytics, especially with maintenance and predictive diagnostics? How can using it benefit manufacturers? Let’s find out…

How Did Big Data Analytics Emerge in the Automotive Industry?

Big data is a relatively new concept, but its modern adaptations originated in the 1960s. For example, in 1964, IBM introduced the System/360, offering processors 100 times more potent than their predecessors. This technology is primitive in retrospect, but it was an essential first step for data processing. In the 1970s and 1980s, tech companies improved this technology to include the automotive industry.

By the 1990s, automotive technology producers began using big data analytics for vehicles. For example, global positioning systems (GPS) became more prominent this decade. These devices allowed consumers to use navigation technology well-known in the U.S. military. Many luxury cars came with this feature installed to entice consumers.

While GPS devices are still prominent, big data has improved cars enough to where they can be self-reliant. Soon, automakers will remove GPS devices once autonomous vehicles become widespread. These vehicles know where they’re going and do not need a GPS for navigation.

What Role Does Big Data Analytics Play in Automotive Maintenance and Predictive Diagnostics?

The last two decades have seen incredible growth for big data and its role in the automotive industry. Automotive professionals have used advanced technology for maintenance and predictive diagnostics. Using data helps technicians know precisely what the problem is and the necessary methods for mitigation.

Automakers primarily take advantage of big data analytics through embedded sensors in their vehicles. These devices allow the manufacturers to track cars anywhere in the world and detect where the problems lie. With this information, the automaker can notify the consumer of issues, find trends and develop solutions for widespread problems. Then, they know what issues to correct for future models of the same vehicle.

What software and technologies do automotive professionals use? These examples demonstrate how industry experts use big data for predictive maintenance and predictive diagnostics.

Machine Learning

Machine learning (ML) has become a critical part of the automotive industry because it solves complex problems and creates algorithms. For auto manufacturers, ML has helped technicians predict equipment failures.

For example, automakers use ML to analyze historical data from their vehicles. Their computers use sensor data to detect trends and see what abnormalities led to the issues. Therefore, manufacturers can catch what problems may arise when they see a particular pattern occurring in a vehicle.

Another use for ML is creating maintenance schedules for vehicles. Historical data indicate when owners of a particular model should bring their cars for routine maintenance. The algorithm is intelligent enough to combine the data with driver performance to alert when service is necessary for the vehicle.


Telematics is one of the earliest examples of big data in the automotive industry, and it’s become vital for car owners and fleet managers worldwide. Research shows about 80% of Class 8 trucks in North America use telematics for safety and efficiency.

Telematics is essential for maintenance because it monitors vehicle health. These devices often detect problems earlier than the operator can, allowing companies to act swiftly on their machines. Early detection and mitigation save money and improve safety by not putting drivers in harmful situations.

What Are the Benefits of Big Data in Automobiles?

Big data analytics is a win-win for manufacturers and consumers. All parties can have peace of mind knowing their machines are safe and efficient. The following three benefits demonstrate how automotive professionals benefit from big data.

Improving Safety

Cars are essential for daily travel, but they can be dangerous. The National Highway Traffic and Safety Administration (NHTSA) says nearly 43,000 people died in motor vehicle traffic crashes in 2022. Reasons for accidents vary, but they can originate from fixable mechanical failures.

Big data analytics decreases the likelihood of these failures by scheduling preventive maintenance and alerting when serious problems arise. Users can know if their brakes, steering wheel or battery needs attention before a catastrophe happens.

Decreasing Downtime

Big data analytics has become invaluable for fleet managers worldwide. The average fleet manager may have 10, 100 or 1,000 vehicles under their wing, making it difficult to track all of them simultaneously. However, advanced data allows them to monitor all the cars and detect trends.

Warning systems send information to the fleet manager, allowing them to act immediately. The modern supply chain demands maximum productivity from fleets, so taking advantage of big data analytics is essential.

Supporting Sustainability

Sustainability has become a significant focus for auto manufacturers. The push for more renewable energy and less waste has led to innovation across the industry. How are they achieving sustainability standards? Big data analytics is helping automakers care for the environment.

Using big data analytics extends the life of cars and reduces the need for customers to consume resources by purchasing cars. Instead, they can keep the same vehicle longer and spend less time at the mechanic.

When cars reach their end of life, many head to the scrapyard. While recycling has improved, parts and pieces still go to waste. For example, the European Union scrapped 5.4 million passenger cars in 2020. Installing telematics devices and using big data would extend their time on the road and reduce waste.

Big Data Analytics in the Automotive Industry

Automotive technology has come a long way and only improves yearly. Modern software allows auto manufacturers to utilize big data analytics with maintenance and predictive diagnostics. With this technology, manufacturers lower the cost for themselves and consumers and make their processes more efficient.

*This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.

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Where do supply chains need the most oversight? How can enhanced analytics maximize efficiency and help these areas operate more smoothly?

Big data applications in an asset-intensive industrial setting like a manufacturing plant or an oil refinery need no introductions. Business leaders in these sectors have long awaited the ability to monitor on-site equipment performance in all its granularity, measure it against historical data quickly, and aggregate unstructured gigabytes of data from disparate machinery into easily interpreted and configurable dashboards. Now that it’s readily available to them, the whole thing feels like a homecoming.

Yet, many organizations have been unreasonably reluctant to carry over big data analytics into supply chain management, arguably an area in every business particularly subject to an abundance of complexity. According to an Accenture study, although 97% of executives are aware of the benefits big data analytics bring to supply chains, only about 1 in 6 had such measures in place as of 2014.

Smarter supply chains cut costs for everyone involved, supplier and client alike, so long as partnerships develop and act on the right metrics. Where do supply chains need the most oversight? How can enhanced analytics maximize efficiency and help these areas operate more smoothly?

Use data to deploy supply chain vehicles safely and cost-effectively.

Fleet Management
Businesses concerned with fleet metrics tend to focus primarily on the KPIs directly related to spend, such as cost per mile, fuel efficiency, and even controlled vehicle re-marketing. However, there’s something to be said for stretching analytics viewpoints to include long-term value adds instead of “pinching pennies” in the short term.

Condition-based maintenance programs, for instance, typically utilize complex data sets to determine if and when vehicles need servicing. As businesses switch to “just-in-time” inventory management models, the importance of fleet availability increases, as does risk. A decommissioned truck or van not only places immediate revenue in jeopardy from a customer service perspective. It also usually requires expensive emergency repairs and may even compromise driver safety in certain circumstances. As such, supply chain and fleet management should coordinate on data-driven oversight to keep transportation operational throughout its life cycle.

When winter storm Juno froze New York in 2015, analysts estimated its economic toll would cost businesses between $500 million to $1 billion. A single storm can do a number on service and profitability, which is why any supply chain management strategy would be incomplete without weather forecasting.

“Businesses should use weather forecasting as a springboard for supplier or 3PL negotiations.”

That said, nothing is more predictably unpredictable than meteorological activity. Knowing a storm is on its way doesn’t really do much to prevent or preempt its impact to a substantial degree. Businesses should use weather forecasting as a springboard for supplier or 3PL negotiations. Business leaders should leverage data to inject flexibility into service contracts beneficial to both sides, absolving all parties of blame when weather is at its worst and hopefully securing carrier engagement/satisfaction in the process.

Decision-makers should also develop robust in-house policies for operators, drivers and warehouse crews diverse enough to accommodate any eventuality. That way, workers know exactly what is expected of them when different phenomena occur. Heavy rain? Drivers should execute safer, more defensive driving strategies on the road as defined by supervisors. Snowfall shuts down a major thoroughfare? Warehouse pickers should switch over to other value-add duties like cleaning or inventory management to avoid labor cost waste.

Demand Forecasting
This one is almost so obvious, it goes without saying – supply chain management hinges on customer demand, where it will be tomorrow and how quickly businesses can respond to it.

What might not be nearly as evident is the effect misaligned supply/demand relationship has on the business beyond supply management, in the form of surpluses, steep product or service markdowns, and inadequate customer service. Businesses shouldn’t merely turn their attentions to the metrics supporting best practices, but set notifications and alarm bells on KPIs that may forewarn them of potential supply chain mismanagement while it’s still able to be resolved.