| | May 20168CIOReviewIn MyOpinionMachine Learning in Manufacturing: Moving to Network-Wide ApproachBy Paul Boris, CIO - Advanced Manufacturing, GEhe challenge with machine learning in manufacturing isn't always the machines; it's often the people as well. For nearly 30 years, the industry has talked about the coming of one big interconnected network of plants, supply chains, enterprises and technology that creates a digital-lean-manufacturing nirvana. While we're well on our way to reaching that mountain-top of just-in-time delivery and zero waste, a risk-adverse culture has slowed the implementation of machine learning.Up until this point, machine learning in the Industrial Internet has focused on optimizing at the machine level. We have access to a ton of data about machine function and productivity that we have used to run our machines at full capacity for as long as possible and predict many maintenance issues.But now it's time to take the next step and start looking at network-wide efficiency. By moving beyond the nodes of machine data and analyzing the bigger pic-ture, manufacturers can unlock the true poten-tial of machine learn-ing. Network-focused machine learning al-gorithms will include data sets like inventory, material cost and labor cost, machine capability and performance ­ fac-tors that have been considered on a plant-by-plant basis already. However, by opening up the entire network's worth of data to these network-based algorithms we can unlock an endless amount of previously unattainable opportunities. Optimal WorkflowWith the move to network-based machine learning algorithms, engineers will have the ability to determine the optimal workflow based on the next stage of the manufacturing process. We already have the ability to run machines at extremely high productivity rates, but what's the point of stressing a machine if the next piece has been delayed for two weeks? Machine learning algorithms will give plant engineers the knowledge that they can run certain machine at a slower to reduce the wear on the equipment, while still completing its output in time for the next stage in the manufacturing process. The engineer needs the authority and the ability to move in and amongst the data, letting the algorithms understand the impact of the current performance on the next action and recommend a course to the operator that most effectively meets the business objectives.T
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