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Jabil using machine learning to save time - and cost

EMS provider Jabil has built its predictive analytics solution – which aims to save time and cost savings through the reduction of waste – on Microsoft Azure Machine Learning.
The new platform aims to predicts errors or failures on the assembly floor before they occur, which then in turn saves time and money as well as shortens product lead times throughout the supply chain. Jabil has rolled out the platform in two of its “megasites” in Penang, Malaysia, and Guadalajara, Mexico, and plans to deploy the solution to its facilities worldwide.

In digital manufacturing, quality assurance across the assembly line is imperative. Identifying errors, slowdowns and potential failures before they occur rather than after they happen can therefore help companies be more proactive and improve productivity.

Through a collaboration with Microsoft, Jabil is using Microsoft Azure services to analyze millions of data points from machines running dozens of steps throughout the manufacturing process. Through Azure Machine Learning, Jabil can help predict failures earlier in the process, for example, at step two in a 32-step process instead of step 15.

"Since deploying the Microsoft predictive analytics solutions we have seen at least an 80 percent accuracy rate in the prediction of machine processes that will slow down or fail, contributing to a scrap and rework savings of 17 percent," said Clint Belinsky, vice president, Global Quality, Jabil.


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September 15 2017 9:25 AM V8.7.1-2