How AI and Predictive Analytics is Changing the OEM Industry
02 June 2021
Read time: 5 min
Read about the impact AI and Predictive Maintenance is having on the automotive manufacturing world!
It should come to no surprise that the automotive industry is under a lot of pressure to adapt to ever changing regulations, disruptive AutoTechnologies and alternative-fueled vehicles. All the while striving to maintain its profitability.
Because of this, many Original Equipment Manufacturers (OEMs) have been compelled to rethink their business models. Fueled by altering consumer consciences and market behavior, the automotive industry has recently witnessed a spate of investments in research, operational efficiency, and data analytics. Most industry stalwarts have recognized the need to use data to not only gain an edge over their competitors but also boost profitability and operating margins.
But taking advantage of that data is no small task. Due to the complexity of the software needed, and the fact that OEMs are hardware first, many have found it difficult to successfully translate their existing capabilities in data management to comprehensive insights. Too often, data intelligence is lost through the value chain and manufacturers are unable to utilize these points to the full potential.
That is where predictive analytics – one of the most prominent technologies of the industry’s recent disruption – comes in.
According to Deloitte, by using predictive analytics combined with a tangible plan with well-defined business outcomes, OEMs can expect to save upwards of $212 million USD.
What is Predictive Analytics?
Simply put, predictive analytics studies past trends to predict outcomes in the future. Using techniques such as statistical modeling, real-time data and artificial intelligence (AI), predictive analytics can help isolate future trends.
The automotive industry is riddled with data – from its manufacturing units to dealerships and repair shops to fleet owners and individual drivers – a continuous flow of data is being relayed back to them but very few are using it to their benefit. Because of this heavy stream of available data, analysis capabilities have undergone a rapid transformation in the last few years. It has grown from the limited perspective of descriptive analytics (i.e. detailing what happened in the past), to diagnostic, and now to predictive. Data is no longer tied down to historical outcomes. Integration with AI, IoT, and machine learning has enabled data to analyze historical data points, glean insights, and repackage them to predict what will happen in the future.
With real-time telematics at their disposal, car manufacturers can identify new growth strategies, refine their operations to improve effectiveness both in their vehicles as well as business models.
So where is predictive analytics making the biggest impact?
Reducing Production Line Disrupts
Some OEMs still currently use only descriptive analytics across many of their machinery within the production lines. Similar to vehicle data, these operating machines spew out thousands of data points that can be analyzed for past disruptions and identify recurring issues.
But for most OEMs, they know how extremely inefficient this method really is. Modern technology has only made the production process more complex with many moving parts dependent on each other. Probing historical data might not prove to be a very accurate measure due to many reasons including changes in machinery, lack of sensors, and missing data. Not properly predicting machine breakdowns can have a damaging effect on output and profitability.
According to the National Institute of Standards, machines that integrate advanced analytics can increase production capacity by 20%, lower material consumption by 4%, and improve yield rates at every level.
The role of predictive analytics in this scenario is to help improve the efficiency of current downtime forecasting models. By using data to identify the causal reason behind downtimes such as defects, faulty parts, material failures, and manual errors, predictive analytics can preemptively warn floor managers of potential downtime which results in lower downtimes, maintenance costs, and higher overall efficiency.
Reduced Warranty Costs
In 2018, AlixPartners estimated that recalls cost manufacturers $22 billion USD. This number is predicted to only go upwards without the proper use of advanced analytics and prevention.
The reason for the predicted increase is that consumers are currently bombarded with choices – new cars, new tech, new models, new fuels. This abundance of choice puts OEMs in a classic problem of speed over efficiency, as more models and makes usually result in direct increase of warranty claims.
A predictive analytics software can help reduce warranty claims by helping manufacturers improve the quality of vehicles before going to market. A software like Pitstop can analyze not only the current model but also centralize hundreds of thousands of data points across the value chain. Using advanced data analytics, engineers can categorize and process claims faster, identify issues, and implement countermeasures.
Not only does predictive analytics directly reduce the money spent on claims and callbacks, but it also creates a better consumer experience which generates a positive overall impact on brand identity.
A Greener Future
Predictive analytics also has a big impact on the automotive industry’s future, by being used to analyze engine performance in order to reduce emission and make hybrid and gas-engine vehicles as emission-compliant as possible. With the direction of electric vehicles in ever-increasing momentum, it will be equally important to evaluate battery performance and properly predict any future failure or safety issues.
As vehicles turn into more complex “software on wheels”, incorporating predictive analytics along with the currently deployed descriptive analytics solutions can have a massive impact on the bottom lines of all OEMs. With measurable outcomes and benefits, predictive analytics has already garnered a lot of positive press worldwide. It is already making a large impact on those that have implemented software like Pitstop.
Combined with a concrete action plan that details the challenges and desired outcomes, predictive analytics is helping OEMs throughout the entire manufacturing process, face current challenges by boosting profit margins, asset efficiency, and revenue growth without having to overhaul their entire business models.
For more information on how to get started with predictive analytics for your vehicles, book a free demo here or email us at email@example.com