Smart Manufacturing in the Mid-Size Company

“Perhaps the company isn’t managing the plant’s performance to an integrated set of Key Performance Indicators (KPIs) for a crucial step in their operations; but instead, [it] is managing just to productivity and cost,” Swink explains. “Once the team agrees to identify and manage to an integrated set of KPIs then data prioritization, targeted sensor infusion, and the selection of smart systems and intelligent machines can help you do that.”

This optimized plant environment better manages such factors as energy, water, quality, safety and yield to achieve desired outcomes. “Let’s say, for a certain customer, the issue is quality,” Swink explains. “You can manage the plant to improve quality by taking a minor hit, say, on energy or water costs involving this customer. These are informed decisions… It all depends on the company’s particular goals, which in most cases are driven by its customers.”

“By embedding sensors and real-time computing techniques to analyze vibration and other performance metrics, vital repairs can be done before it is too late.”

Moosehead launched its smart process with a cross-functional team in charge. Wayne Arsenault, VP of operations and human resources, says the group initially benchmarked competitors of similar size to discern possible performance improvements, only to learn that a sensor here and there would not provide the broad, competitive advantages the company sought. “Simply tweaking the plant was not an option; we needed to make a fundamental shift in how we utilize our assets, which required a multi-year investment in the physical transformation of our bottling line,” he explains.

This transformation involved the use of sensors on the conveyor line to determine a jam in the flow of bottles. “We use photo cells similar to the sensor preventing a garage door from coming down on your car to see when bottles are bunching up,” Coleman explains. “The operator receives a red or yellow light on the dashboard telling him there is a problem at a particular juncture along the conveyor line, and [he] can then slow it down. Previously, we’d have to shut down the entire system and have someone physically inspect the problem.”

Sensors accompanied by real-time data analytics have long been used in military and commercial helicopter engines to predict failure risks, notes Phil Shelley, former CTO at Sears Holdings and current president of Newton Park Partners, a data analytics firm. “Obviously, the failure here can be catastrophic. By embedding sensors and real-time computing techniques to analyze vibration and other performance metrics, vital repairs can be done before it is too late.”

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