Recently, Nissan Motor announced that they will release a self driving car by 2020. According to Karl Brauer of Kelley Blue Book, “The technology to create self-driving cars is already here, [...] as sci-fi as it sounds, self-driving cars that don’t ever crash, reduce traffic congestion and make valet attendants obsolete are coming.”
In what’s called the Internet of Things, the physical world will become a networked information system—through sensors and actuators embedded in real physical objects and linked through wired and wireless networks via the Internet Protocol. This holds special value for manufacturing: the potential for connected physical systems to improve productivity in the production process and the supply chain is huge. Consider processes that govern themselves, where smart products can take corrective action to avoid damages and where individual parts are automatically replenished. Such technologies already exist and could drive the fourth industrial revolution— following the steam engine, the conveyor belt (assembly line - think Ford Model T), and the first phase of IT and automation technology.
But even before we go to those scenarios where cars drive themselves and machines fix manufacturing defects by themselves, there are a lot of great opportunities for human-powered analytics to add value as the internet of things takes off. Auto companies have installed thousands of "black boxes" inside their prototype and field testing vehicles to capture second by second data from the dozens of control units which manage today's automobiles. These boxes simply plug into the vehicle's on-board diagnostic (OBD) port which is typically located under the front dashboard of all cars. They collect 500-750 different vehicle performance parameters that add up to terabytes of data in hours! Talk about big data!
The intent of the automakers for installing these boxes is to collect data which their engineers can later analyze to fix bugs and improve on existing designs. However a greater motivation comes from identifying potential recall situations. For example, one car manufacturer found out from this data that their minivan batteries would end up in a recall. The problem was an underpowered alternator - it was not able to fully recharge the batteries because the most common drive cycle for this particular minivan (think soccer mom taking kid to practice) was less than 3 miles. As a result, there appeared to be a lot of complaints about dead batteries and the company was potentially facing the recall of millions of minivans which had this alternator. The boxes collect information about driving cycles and this data was really useful in understanding the real reason behind the dead batteries. The test vehicles which had short drive cycles were the ones which reported dead batteries! Simply changing the alternator to higher capacity could fix the problem. Now it was an easy fix to extend this solution to the entire fleet.
But what else can we do with such data from a predictive standpoint? The opportunities are literally endless, ranging from early fault detection (predicting when a particular component is likely to fail) to automatically adjusting driving route based on traffic pattern predictions. The ultimate test of predictive analytics in the internet of things is of course fully autonomous systems, such as the Nissan car of 2020 or the Google self driving car of today. In the end all autonomous systems will need the ability to build predictive capabilities - in other words, machines must learn machine learning!
Google claims that their self-driving car of today has logged more than 300,000 miles with almost zero incidence of accidents. The one time a minor crash did occur was when the car was rear-ended by a human-driven car! So, when the technology is fully mature, it is not just parking valets who become obsolete, other higher paying professions such as automotive safety systems experts may also need to look for other options! Predictive analytics is the enabler that will make this happen.
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