Deploy advanced machine learning to predict performance variations before they happen and proactively initiate a remedy.
Let optimization algorithms prescribe the best-fit operating set points for control parameters based on historic data and expert objective models.
Determine the probable causes that adversely impact throughput, cost and quality by baselining and comparing process variables and predictions from different assets and processes.
Choose the KPIs that need to be optimized. Then choose quality as your most important criteria by defining a narrow tolerance even if it takes more energy—or energy can be the primary concern.
Consistently reduce energy costs from less than 1% to 15% based on the level of variances the plant experiences.
Built-in analytical models offered as SaaS allow you to avoid costly infrastructure cost and gain quick momentum for your project.