A large plastics manufacturer with more than 36 industrial plants across three continents produces more than 32 billion pounds of thermoplastic resins and petrochemicals annually. Because each customer order is unique, the company must transition manufacturing settings for each order and then test each batch to ensure quality. Lack of real-time visibility into current quality causes significant lag time between orders. Miscalibration in product quality can cause entire orders to fail quality testing, leading to millions of dollars of lost revenue.
By implementing the BHC3™ Process Optimization application, the manufacturer reduced average product transition times by more than 30%, allowing the firm to understand exactly when a product moves in and out of spec, saving millions of dollars. BHC3 Process Optimization machine learning predictions vary considerably less than lab tests during steady-state production periods, providing further confidence in the accuracy of the machine learning models.