The company’s polymer mixing process was producing output of inconsistent quality, with yields sometimes dipping as low as 50% or 60%. The scrapping of poor batches created huge costs and was crippling production capacity. The root cause was traced to ever-changing product specifications, in addition to variations in a range of production parameters. Production engineers were unable to stabilize the process using traditional approaches as mixing polymer was stubbornly unstable and each new product formulation only exacerbated the problem. To solve this issue, this global manufacturer needed a solution that provided the following capabilities: • Ingest and integrate Internet of Things (IoT) data from machinery, sensors, the environment and other sources • Discover the critical factors and optimal process parameters through correlation, visual analysis, and optimization algorithms • Provide deep process insight, enabling daily decision support and continuous improvement • Dynamically and continuously optimize the process through machine learning