Predictive applications in IoT


The value of any data lies, of course, in its use to make better decisions that lead to more favorable results – like increased revenue and profit, improved customer satisfaction, and increased safety. The Internet of Things is giving us a lot more data. The challenge is how to manage all of it, and make good use of it.

Internet connected sensors, such as on vehicles or manufacturing equipment, provide a huge new source of data that can be exploited to great benefit. A KDnuggets article cited 150-300 sensors on a typical Formula One car, and nearly 6,000 sensors on an Airbus A350 (generating 2.5Tb of data per day!). As a race car driver, or a passenger on a commercial airline, one is happy all this data is being put to good use, such as for predictive maintenance.

Volvo Truck“Machine learning and artificial intelligence are areas we’re putting a lot of emphasis on right now,” said Conal Deedy, director of Connected Vehicle Services for Volvo Trucks North America. Volvo is enhancing their remote diagnostics, monitoring and analyzing trouble codes to recognize situational patterns. With better ability to predict failures, and ability to perform over-the-air updates, trucks have improved uptime. When trucks do have to be called in for service, off-the-road time is reduced compared to the 2.3 day industry average.

Another interesting application is real-time analysis of weather data from remote sensors, both in fixed locations and on vehicles. For example, local conditions (like dangerous fog or icing) transmitted from moving vehicles could complement weather forecasts, improving the margin for travel safety. And such information can also deliver economic benefits, by improving transportation planning at shipping and delivery companies.


About Author

Mike Gilliland

Product Marketing Manager

Michael Gilliland is a longtime business forecasting practitioner and formerly a Product Marketing Manager for SAS Forecasting. He is on the Board of Directors of the International Institute of Forecasters, and is Associate Editor of their practitioner journal Foresight: The International Journal of Applied Forecasting. Mike is author of The Business Forecasting Deal (Wiley, 2010) and former editor of the free e-book Forecasting with SAS: Special Collection (SAS Press, 2020). He is principal editor of Business Forecasting: Practical Problems and Solutions (Wiley, 2015) and Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Wiley, 2021). In 2017 Mike received the Institute of Business Forecasting's Lifetime Achievement Award. In 2021 his paper "FVA: A Reality Check on Forecasting Practices" was inducted into the Foresight Hall of Fame. Mike initiated The Business Forecasting Deal blog in 2009 to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.

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