Process, products and components generate large amount of data containing historical information. With information processing technologies we can extract useful knowledge from this large datasets to get significant improvements in the process. For example, equipment faults can be detected, the number of items to be ordered can be predicted or optimal control parameters can be determined.
Moreover, raw materials and resources and complementary involved processes have also widely varying costs with additional energy costs. Combining these process variables contributes to obtain final product properties involving manufacturing costs. With process information analysis and optimization algorithms applications we can minimize the manufacturing costs and reduce energy consumption keeping the quality of the final product.
Overview of process optimization using system information
The objective is to improve process efficiency using information of management systems and combining data analysis tools with advanced optimization techniques.
With this approach, we develop algorithms to support decision making in the manufacturing process based on cost optimization with different applications:
- Improve the efficiency and profitability of processes.
- Minimize the production of faulty products.
- Remove unproductive processes.
- Prevention of error sources in process.
- Reduce costs associated with process deviations.
- Efficient management of raw material.
- Quality control of finished product.
- Reduce energy consumption.
These algorithms could also be integrated with the existing applications, as a process control tool to make adjustments in the process based on information of real manufacturing parameters and detecting deviations in the process.
This opens a whole new world of possibilities for many industries to solve process control problems.
