The IIoT is having a dramatic impact on the auto industry. How can manufacturers—of any kind—benefit from a future of machine learning and anomaly detection?
The Industrial Internet of Things (IIoT) is changing everything. From the food we eat to the machines we use to the cars we drive. Everything is rapidly transforming, and for the better. With more things getting connected to the internet, the IIoT is swiftly extending its tentacles to the factory floor—the center stage for all the industrial drama.
The automotive industry is among the highest impacted by the IIoT. According to Mary Barra, CEO, General Motors, “The auto industry is poised for more change in the next five to ten years than it has seen in the past 50.”
In fact, a study revealed that the number of networked cars will rise 30% a year for the next several years. This means by 2020, one in five cars will be connected to the Internet.
For more than a century now, the automotive industry has created competitive advantage mainly through engineering excellence. Going forward, this will no longer be sufficient.
The future will be an age where parts can monitor and evaluate their own performance and even order their own replacement if required. Real time analytics and cognitive systems will detect and predict anomalies, and turn data from components and systems into valuable insights.
These can then be used by automotive manufacturers to increase the reliability of cars and offer new value-added services to customers. In other words, technologies such as Machine Learning and Anomaly Detection can extend quality assurance beyond the factory doors. However, for all this to happen, it is very critical to set the foundation right.
The biggest challenge for any automotive manufacturer today is the catastrophe caused by unplanned downtime. One leading auto manufacturer estimates unplanned downtime in a factory can cost as much as $20,000 per minute. Huge losses in matter of seconds!
Most often, these situations are the result of production machine failures that could have been avoided if data from the machine was successfully analyzed to detect abnormalities, anticipate the failure and plan a repair.
Anomaly detection continues to play a vital role in most industries across the globe. Analyzing automotive sensor data to detect and predict anomalies can be extremely useful to mitigate risks of unexpected downtime. According to a report by MarketsandMarkets, the size of the global anomaly detection market is estimated to grow from $ 2.08 billion in 2017 to $4.45 billion by 2022 at a compound annual growth rate of 16.4%.
It is critical for automotive manufacturers to ask the below questions:
It is time to accelerate productivity by monetizing this data. Even a small hike of 1% in efficiency gains can yield astounding results over a period of time. A 1% increase in production efficiency can create a value of $30B in fuel cost savings.
With accurate Anomaly Detection at the right time, your IIoT technology investments can be converted into a powerhouse of infinite value.
Sign up for a free trial here to experience the power of automated machine learning to detect and predict anomalies from your time-series data.
Start a Trial Today
Anita Raj is a Product Marketing and Growth Hacking Strategist on the Progress DataRPM team, with over 10 years of experience working in the field of big data, cloud and machine learning. She brings a deep expertise in running growth marketing leadership experience at multi-billion-dollar enterprise companies and high growth start-ups.
Copyright © 2018 Progress Software Corporation and/or its subsidiaries or affiliates.
All Rights Reserved.
Progress, Telerik, and certain product names used herein are trademarks or registered trademarks of Progress Software Corporation and/or one of its subsidiaries or affiliates in the U.S. and/or other countries. See Trademarks for appropriate markings.