Can We Predict the Adoption of Machine Learning?

 | December 12, 2017 12:00 am |

In 2016, Forbes predicted 10 Ways Machine Learning will Revolutionize Manufacturing, focusing on predictive data analytics to enhance capacity, improve performance, and increase yields. In 2017, shares Present and Future Use-Cases, which highlights major companies like Siemens and GE who are developing machine learning tools to use for their own manufacturing purposes, for quicker optimized solutions with greater predictive accuracy, as anticipated by Forbes. For instance, Siemens’ neural network-based AI system was able to reduce specific gas turbines’ emissions through the monitoring of production variables 10-15% further than attempts by human experts.

Outside of improvements to production efficiency and yields, also touched on the advancement of industrial robots. With the integrated learning approach for these robots, training time is significantly reduced since they are able to train themselves. This greatly minimizes down time and allows for the capability to perform more varied tasks.  The advancements in machine learning also create a much safer environment for humans and robots to work alongside each other, further allowing more flexibility in manufacturing.

Billions are being invested on machine learning technologies as the market opportunity is $11.6 trillion, according to the UN. Our perspective is that machine learning will tilt the playing field for the companies that adopt it first.  It will allow these companies to provide mass customization, resulting in many forms of consumer benefits beyond traditional manufacturing themes first suggested by Forbes. Additionally, the pace of change will only become more rapid as new use-cases continue to be piloted and these pilots move to implementation.

Felix M. Gelt is a Director in the Performance Improvement practice and leads our Expense Reduction Analysis offering at Farber. Felix can be reached at 647-796-6004 and