How SAS® Helps Lower Energy Usage and Cost
Identify optimum shift parameters
Dynamically compare current and historical production runs and show the drivers of energy consumption.
Minimize energy consumption while maintaining quality
Determine the exact process setpoints to apply by using a set of mathematical optimization solvers.
Improve sustainability efforts
Reduce carbon footprint and help meet ESG requirements.
Why choose SAS for lowering manufacturing energy usage and cost?
Easily gain the insights you need to make setpoint changes fast.
- IoT data integration. Access, integrate and enrich data from varying sources across your plant, including raw materials, dryers, kilns and weather.
- Built-in data model. Combine environmental, MES, PLC, SCADA and LIMS data for automated explainers of the process parameters most affecting energy consumption.
- Prebuilt machine learning models. Easily deploy these models to learn what changes you need to make across the plant with best-in-class analytics.
- Prebuilt visualizations and dashboards. View data analyses across the organization and enable engineers to take quick, insight-backed actions that result in significant energy savings.
Accelerate time to value with integrated energy modules.
- Automated explainers. Provide engineers with the missing insights needed to affect key process parameters with a relationship analyzer for energy consumed and process parameters.
- Golden shift analyzer. Identify optimum shift parameters for each process setting using advanced analytics models to dynamically compare current production runs to historical runs with very low energy consumption.
- Setpoint analyzer. Gain recommendations for operators on the exact setpoints they should apply to the process to minimize energy consumption while maintaining quality using mathematical optimization solvers.
SAS Energy Cost Optimization
Watch how Wienerberger AG has benefitted from SAS Energy Cost Optimization.
With SAS Energy Cost Optimization, you can reduce energy usage, carbon footprint and CO2 certificate spending while maintaining quality and yield. Take the guesswork out of making improvements – combine and analyze IoT and process data from across the production process, use prebuilt analytic models to predict specific energy consumption and make sense of variable impacts. These capabilities deliver mathematically optimized insights that empower production operators to quickly tune the process recipe setpoints, accelerating time to value and providing an average energy cost savings of 10%.