AI for Process Control and Optimization
Maximize productivity and profitability with AI-powered process control and optimization
Business process control and optimization is critical to delivering best-in-class efficiency, cost minimization, and product quality, whether in manufacturing, service delivery, or any other area of business. Artificial intelligence provides the power to continuously monitor and analyze thousands of real-time process KPIs, providing insights that will make your business run more smoothly and profitably.
What is process control and optimization?
Asset performance and lifespan in power generation
Reducing quality problems in manufacturing
Maximizing productivity in aviation
How does AI enable process control and optimization?
These process control and optimization methodologies employ proprietary AI algorithms and cutting-edge deep learning technology to ensure that your manufacturing and operational processes operate at peak performance.
Safe workers are more efficient and productive workers
The total cost of worker injuries in 2019 was in excess of $170B, including lost productivity, wages, and foregone opportunities. Process control and optimization starts with safe workers, and safety is enabled directly by AI-driven process optimization. This is partly due to reduced instances of catastrophic equipment failure, and partly due to AI’s ability to create a generally safer work environment through enhanced HSE practices.
Ensure equipment stays up and running
Nothing damages business process control and optimization more than unexpected equipment failures. SparkCognition solutions deliver greatly extended asset/equipment lifetimes, reduced maintenance costs, and efficient repeatable process execution, i.e., the dependable infrastructure companies need to remain productive and profitable from one day to the next.
Improve quality control processes
A huge driver of company business success in a highly competitive landscape is the quality of what the company creates and sells. AI-enabled business process optimization ensures that products are manufactured not only efficiently, but also consistent with quality standards that consumers expect. That means detecting and eliminating defects in the process chain as early and as automatically as possible.
AI-powered process control and optimization solutions across industries
AI-powered process control and optimization in oil and gas
Drive greater profitability, longer asset life, and improved environmental sustainability through more effective and efficient process control and optimization.
- Improve process cost control and profitability.
- Extend asset/equipment lifetimes (i.e., reduced CAPEX).
- Achieve greater operational efficiency.
AI-powered process control and optimization in manufacturing
- Improve product quality assurance.
- Gain better operational cost control.
- Extend asset lifetimes.
AI-powered process control and optimization for the power industry
- Respond more quickly to market pricing fluctuations.
- Achieve sustainability/environmental goals.
- Improve generation productivity.
AI-powered process control and optimization in aviation
Prevent failures and accidents while also eliminating the high cost of grounding aircraft (upwards of $4-$5M per day per incident). AI-powered maintenance process control and optimization can mitigate such events and help ensure your continuing competitiveness.
- Optimize AI-driven aircraft maintenance.
- Ensure regulatory/FAA compliance.
- Eliminate unnecessary reactive maintenance activities.
How SparkCognition delivers process control and optimization
SparkCognition process control and optimization applies our patented machine learning algorithms to your existing process performance data to predict and prevent asset failures that can slow or stop production processes.
Our process control and optimization implementation approach is straightforward and effective, led throughout by SparkCognition AI and domain experts.
Step 1: Data ingestion
To ingest and analyze your performance data, SparkCognition employs a number of powerful ML techniques. These are effective ways to handle large training sets with extensive sets of features. The models created effectively generate failure and maintenance predictions quickly and automatically.
Step 2: Training data
After subjecting the data to quality validation and cleaning, deep learning models search for and identify anomalies in resource consumption, asset condition, and overall process efficiency.
Step 3: Model development
Models are constructed based on local context for real-time execution and KPI reporting. As additional data is ingested and analyzed over time, the solution predicts future problems based on historical trends it has identified.