Nothing good tends to come from something that is broken. It’s bad enough to have to deal with problems in the factory: The average manufacturer deals with around 800 hours of downtime annually, which translates to almost $1 million in lost revenue.
What’s even more costly is when automotive makers have to deal with problems on the road. Back in 2014, more than 64 million vehicles were taken off the market due to defects. Since the start of 2018 we have seen Tesla recall 123,000 Model S cars; Ford recall nearly 350,000 F-series pickup trucks and expeditions; and Toyota recall 21,000 Toyota and Lexus cars.
Recalls are expensive. Connected technology and simulation can not only mimic real life, but also offer a confidence rating. For example, there might be a confidence level of 10% that a belt needs to be replaced in the next five days. This maintenance doesn’t sound too worrisome, so it’s skipped. Three days later software might show that the confidence level has increase to 95% that the machine needs a new belt. This not only prevents downtime from machines breaking, but alerts technicians before a machine might start producing parts that are out of tolerance and would need to be scrapped.
After the Tesla announcement alone, shares fell 4%—a decline made all the more pertinent in June when almost 10% of the company’s global workforce was made redundant, as it looks to steady the ship and win back investor confidence in its business model. Ultimately, recalls, mistakes, and broken parts make headlines around the world, costing brands millions in repairs and untold reputational damage.
For years, the conventional approach was to fix what was broken. Then more manufacturers began to take a preventative approach that used a combination of physics-based models of the machines and knowledge of past failures to determine at what interval a particular type of machine is likely to need repairing in order to catch known problems in time. This approach was effective for handling known problems, but couldn’t detect the unknown problems which happen much more often in the field.
Now, with the advancements in cognitive learning and the proliferation of IoT sensors across production lines, we are entering a new stage of predictive maintenance. This is where AI, machine learning, and predictive analytics can enable manufacturers to gain full visibility and control of manufacturing processes—not only to prepare for the known issues, but also to effectively predict and prevent the unknown.
Not only can simulation software help prevent recalls, but collecting data on cars in the field can tell users when maintenance is needed before they become stranded. (Pictured: 2018 Dodge Challenger SRT Demon)
Looking ahead. The use of cognitive predictive maintenance is not a luxury or a toy reserved for the biggest manufacturers. This technology is becoming essential in an industry where the smallest advantage can mean millions of dollars. The most exciting part is that this is just the start in terms of where cognitive technology will take the automotive industry.
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