Predictive Simulation

Optimize manufacturing efficiency with Predictive Simulation 

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Seebo Predictive Simulation, enables production teams to simulate how a production process will behave in different scenarios, and specifically, how to avoid the anticipated process disturbance.

Avoid process disruptions using predictive simulation

When machine learning is used to predict upcoming process disruptions, the generated insight is just not enough.

Process engineers need to determine how the anticipated production disruption can be avoided by changing specific production parameters.

But with thousands of production parameters at hand, it’s unclear where to start. This is where Automated root-cause analysis should be used to determine the specific causes of an upcoming disruption.  After understanding the disruption causes, predictive simulation is used to enable the user to determine the best-fit values in order to avoid the process disturbance.

Benefits of using Predictive Simulation

Close the loop
and take action on
analytics recommendations

Adjust only the production settings that will eliminate production disturbances

Reduce the risks
in mis-adjusting
production settings

Predictive Simulation Applications in Manufacturing

Reducing waste in food manufacturing

Seebo predicts and prevents production waste by identifying areas of loss and prescribing focused actions that reduce product defects and inefficiencies. The solution employs predictive analytics and automated root cause analysis to anticipate process failures that yield wastage. By implementing predictive simulation, process engineers test production parameters until optimal values are determined for minimizing waste and rework.

Minimizing process inefficiencies in chemical plants

Seebo Production Optimization employs ‘predictive’ simulation, allowing process engineers to visualize and understand how specific production processes will behave in different scenarios, and how certain process inefficiencies will be avoided. The solution predicts when process inefficiencies will occur in chemical production – for example blockages, formation of side products, losses during separation and purification, and more.

Optimizing quality and throughput in a refinery

Seebo leverages predictive simulation capabilities in order to continually regulate and control chemical processes in oil refineries. By leveraging predictive simulation, quality and process deficiencies are dramatically reduced. Process deficiencies can be caused by a number of various factors, for example; excessive heat, sulfur-containing compounds from naphtha, throughput fluctuations, and more.

Access our free case studies

How a global manufacturing enterprise implemented Artificial Intelligence tools to increase uptime, reduce quality issues, and improve production capacity.

How a multinational manufacturer of Ethylene Dichloride increased production yield and improved quality by implementing process-based Industrial AI.