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OM in the News: Using Machine Learning to Keep the Beer Flowing

January 28, 2019

Anheuser-Busch uses this sensor to pick up ultrasonic sounds coming off conveyor belt and motors.

The world’s largest beer maker is using low-cost sensors and machine learning to predict when motors at a Colorado brewery might malfunction, reports The Wall Street Journal (Jan. 24, 2019).  The Anheuser-Busch plant was the first among the company’s 350 beer facilities to test whether wireless sensors that can detect ultrasonic sounds—beyond the grasp of the human ear—can be analyzed to predict when machines need maintenance. “You can start hearing days in advance that something will go wrong, and you’ll know within hours when it’ll fail. It’s really, for us, very practical,” said the VP.

The installation at the brewery cost just $20,000. Since the system was deployed, it has predicted pending equipment failures and prevented unscheduled production-line halts, and more than $200,000 in product loss. (The Colorado plant employs 580 people and ships 225 truckloads of Budweiser, Bud Light and other beer brands each day).

Sensors have been used for predictive maintenance in the past, but they were unable to transmit information in real time. Advances in processing data at the edge of the network, referred to as edge computing, enable companies to collect and analyze real-time sensor data from machines. Machine learning refers to the subset of AI that allows computers to act “intelligently” without being explicitly programmed. Algorithms can increase the accuracy of predictions based on large amounts of historical and real-time sensor data.

Organizations that own wind turbines or jet engines are expected to save about $1 trillion a year as a result of predictive maintenance techniques. Sound-based predictive maintenance is becoming more important for companies, as there has been a wave of retirements among workers who were tasked with listening to machines to identify potential breakdowns. The price of internet-of-things sensors is expected to fall to 26 cents on average by 2024, from 46 cents in 2018.

Classroom discussion questions:

  1. What is predictive maintenance?
  2. How does this differ from “breakdown maintenance?”
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