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This presentation will detail a case study of how one of Japan's manufacturers eliminated a long-standing global bottleneck on an automotive engine manufacturing line, and the deployment challenges they have faced in their global setting. They faced a “broken” manufacturing process which damaged their customer’s lean manufacturing process and its OEM car manufacturing strategy. Despite the inconsistent data sets across several discrete steps, PTC and the client team adopted a process to undergo the six steps of ThingWorx Analytics in conjunction with a proof-of-concept approach.
In this presentation, you will learn how PTC Japan’s team focused on unexpected prediction results of failure causes and business outcomes found via the ThingWorx Analytics decision-tree as well as the time-series model provided. Presenters will share feedback from the manufacturing clients on the successful combination of lean manufacturing and predictive analysis that eliminated waste and increased the throughput, resulting in advanced lean manufacturing. Additionally, you will learn about recent deployment challenges of this machine learning operationalization at scale for factories in different continents and the need for manufacturing master data in BOM form.
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