How Machine Learning is Revolutionizing the Automotive Business

Written by guest contributor Roy Emmerson, a technology enthusiast, a loving father of twins, a programmer in a custom software company, co-founder of TechTimes.com, and marketing specialist of Itrate.co

Machines have always been in place to make work easier. Take the lever as an example. It’s a straightforward machine that has enabled people to reduce the heft of loads and lift more items within shorter periods. And over time, machines have become more effective, more so in light of technological advancements.

Thanks to machine learning, it’s become possible for machines to learn how to do things and build on these processes based on progressive adaptations. Machines can now rely on algorithms and statistical data to work out patterns and use these inferences to carry out instructions. So, instead of a machine following path A-B, it can follow path A-C-B if it proves to be the better option.

We look at how this intelligence in computer systems has greatly changed the automotive business.

The Impact of Machine Learning on the Automotive Industry

The automotive industry relies on precision. It takes only one mishap for production to come to a halt. Moreover, one production error can force a manufacturer to recall a car model even when it’s already on the market. So, what good impact can machine learning have on such a sensitive industry?

Predictive Analytics

Thanks to artificial intelligence, machines can process large amounts of data simultaneously. As a result, the machines can predict when a system is due to fail, which comes in handy in the following ways:

• Prevention of breakdowns: A delay in production not only increases operational costs but also adversely impacts the profitability of any business. Thus, manufacturers do all they can to ensure that things run smoothly 24/7. Unfortunately, machines are imperfect and will sometimes break down, especially when there are anomalies in their setups. Thankfully, machine learning enables the systems to predict when any component is likely to fail. Employees can then step in and resolve the issue before it escalates into a breakdown.
• Scheduling of repairs: More manufacturers now rely on predictive analytics when maintaining their machines rather than focusing on preventive maintenance alone. These analytics break down the warranties in the systems, their past and present performance statistics, and their patterns. If anything seems off and predictions show that the machine cannot withstand more operations, the team can step in and intervene.

The result? Operations can run throughout, enabling manufacturers to meet their quotas without worrying that a system breakdown can cut off their supply. 

Projected Insights

In the past, the easiest way to gauge how long manufacturing would take and how much it would cost would be to rely on estimates. In some cases, manufacturers would practically manufacture the automotive to get data. But now? They don’t need to go to that extent when figuring out the profitability and feasibility of any manufacturing process. Instead, they can model it using machine learning and predictive analytics.

Doing so requires them to replicate similar conditions to those in the manufacturing plant. They can run the entire simulation, get the data, and use it to infer when deciding if they should stick with or change their manufacturing plans. Moreover, the simulations come in handy when predicting possible issues that the manufacturer could encounter during manufacturing.

Decisions have become much easier to make with such insights being readily available.

Automated Retrieval

Relying on employees to retrieve items during manufacturing results in varying retrieval times. As a result, the production on one line can be faster than on another, creating variations in the production times. Manufacturers have found an easy way to streamline the processes by relying on self-driving vehicles.

These vehicles feature sensors that allow them to move around the plant without bumping into each other, humans or any items in the way. When they reach the storage facilities, they grab what they need and present it in the production line. All this while, the humans can keep working on the production, undistracted.

These vehicles are easy to monitor and allow the manager to deduce how long a production line will take. They can also be configured to do other things in the plant when need be, ensuring that employees can focus on the most crucial tasks. Thus, they have proven to be crucial to the production processes and have weaved their way into our roads. So, they are pretty much useful in various aspects of our lives. 

Bridging the Labor Gap

Even with the rising unemployment rates worldwide, manufacturing has been dealing with a considerable labor shortage. Manufacturers have realized that relying on systems can help them face this problem head-on as they look for ways to attract and retain more employees.

Machines have been tasked with the following duties: 

• Predictive analytics, 
• Quality assurance, 
• Item retrieval.

Meanwhile, humans cater to the more critical tasks. This cooperation has proved fruitful even for the employees who now have less work on their plates.

Tailored Products

Machine learning is effective in drawing conclusions from consumer insights. With this data, a manufacturer can tell what kinds of features will appeal to the market and can produce automobiles that fit this picture. Why does this matter?

Reduced Conversion Costs

Whenever an automotive manufacturer releases a new product in the market, the sales and marketing teams must work hard to relate the automotive to the market. They must outline why users want such an automotive and convince people to buy it. 

Generating leads and converting them into customers is difficult; not every product is always a hit. And that is where machine learning comes in handy – the data in the systems can help a manufacturer hone in on precisely what the market wants. Once a manufacturer has met this need, convincing people to buy the automotive will require much fewer sales and marketing efforts. And the manufacturer can reduce their operational costs by more than 50% by streamlining the sales funnel.

Moreover, machine learning can facilitate the team’s work and eliminate the need to expand it. This is also profitable because, as we know, the hourly rate for software developer or SEO specialists is $40-$60/hour. 

Higher Productivity

Manufacturing automotives that may not have a ready market is quite a gamble. If a manufacturer gets it right, they make a profit. But if they produce a product that’s not readily acceptable to the masses, they will have used resources on what could be a flop.

The beauty of machine learning is that manufacturers can tell which products will be a hit on the market. They can thus focus on those specific products and give them their all, resulting in higher output and better profitability.

Customer satisfaction is always the ultimate goal, which is quite possible with machine learning. 

Lower Operational Costs

With inflation rising daily, manufacturers must figure out a way to maintain their operations without increasing the cost of their goods. After all, they cannot pass the rising costs to consumers already grappling with the high costs of living. So, what’s the alternative? Machine learning!

Machine learning enables manufacturers to understand the resources that go into the making of locomotives. It comes in handy in the following ways:

• Reduced consumption: Machine learning can find the processes where wastage occurs and cut back on such costs. For example, if a manufacturer realizes that they can spend less energy on one process, doing so can have a significant cost reduction on the rest of the manufacturing chain. Moreover, machine learning can uncover bottlenecks in production lines and unprofitable production lines;
• Increased sustainability: Manufacturers can determine the areas which could do with a bit of tweaking. For example, if green energy is more profitable in the long run, they can embrace it to reduce operational costs while aligning with climate change directives;
• Higher yield rates: Besides reducing operational costs, manufacturers constantly aim to push out more products. And what better way to do this than by streamlining the manufacturing processes to increase the number of automotives produced per period?

Over time, manufacturers can manufacture automobiles at lower prices and maintain their profitability without passing on all the increasing overheads to their clients. 

Defect Control

We’ve all heard cases where a minor defect in an automobile resulted in the recall of all automobiles in that model. Not only is it frustrating to the consumers, but it also costs the manufacturer significantly. How can machine learning help with this?

Machine learning can not only detect defects, but it can also check products to ensure they meet a given quality standard. Any automobile that does not meet the prescribed quality level or shows signs of defects does not leave the production facility. Machines are much more capable of honing in on imperfections than humans, and their use has seen defect detection increase by more than 80%. Moreover, these systems are cheap in the long run as they do not require much in the way of hardware and can run on open-source environments. Over time, the systems improve at detecting anomalies, allowing manufacturers to focus more on production and leave quality assurance to the systems.

This improvement has been a plus for manufacturers and has given consumers more faith in their automobiles. 

Conclusion 

Machine learning is set to become bigger and better as the systems evolve to capture scenarios that manufacturers did not even know existed.

Machines have always been in place to make work easier. Take the lever as an example. It’s a straightforward machine that has enabled people to reduce the heft of loads and lift more items within shorter periods. And over time, machines have become more effective, more so in light of technological advancements.