JUNE 2016MANUFACTURINGTECHNOLOGYINSIGHTS.COM8IN MY OPINIONThe 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 picture, manufacturers can unlock the true potential of machine learning. Network-focused machine learning algorithms will include data sets like inventory, material cost and labor cost, machine capability and performance ­ factors 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.The Gig EconomyLooking beyond the machines themselves, machine-learning algorithms can reduce labor costs and improve the work-life balance of plant employees. By utilizing more data from across the network of plants and incorporating seemingly disparate systems, we can better enable the "gig" economy in the manufacturing industry. For example, you might employ a very specific skillset based on the products you build or machines you run. Using advanced data and machine-learning algorithms you may have identified that the likelihood of mechanical issues or production disruption is imminent. Instead of having the specialized labor arrive either too early to be fully productive or too late to avert the issue, an organization can be more prescriptive as to when and where they deploy key resources if at all. And while many companies do this now with seasonal or surge labor, we've seen that this model can be utilized effectively in many of the new consumer-based business models that are emerging. A shorter work day that provides the same amount of productivity for both the worker and the plant is a win-win, it's the theory of working smarter not harder.Machine Learning in Manufacturing: Moving to Network-Wide ApproachBy Paul Boris, CIO, Advanced Manufacturing, GEPaul Boris
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