SEATTLE -- Microsoft and Jabil announced a new predictive analytics platform said to predict errors or failures on the assembly floor before they occur, saving customers time and money while delivering superior quality and shortened product lead times throughout the entire 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.

The EMS company 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% accuracy rate in the prediction of machine processes that will slow down or fail, contributing to a scrap and rework savings of 17%," said Clint Belinsky, vice president, global quality, Jabil. "As our customers constantly look for ways to innovate, it is very impactful to show them a predictive solution that will ensure quality and increase their speed to market."

"Jabil's digital transformation on the factory floor will reshape its industry," said Jason Zander, corporate vice president of Microsoft Azure at Microsoft. "As product cycle times shorten and products get smarter, Jabil understands how the intelligent cloud combined with predictive analytics will help it support the needs of its customers."

In addition to time and cost savings through the reduction of waste, Jabil's operators and engineers can also proactively make adjustments to equipment based on predictions, eliminating the need for unnecessary inspections that cause downtime. A demonstration of the collaboration is on display at the Microsoft Booth at Hannover Messe 2016 in Hall 7, Booth C40.

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