Artificial Intelligence 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.

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What is process control and optimization?

Process control and optimization is a critical element of any successful operational strategy. Ensuring the correct utilization of resources and the productivity of staff requires a rigorous focus on asset performance, defect reduction, throughput maximization, and product quality.

Asset performance and lifespan in power generation

Process control solutions predict the impending failure of expensive assets and facilitate maintenance schedules based on the assessment of real-time performance rather than unnecessarily costly recurring schedules. In this way, process control and optimization keeps critical equipment up and running, leading to improved process performance.

Reducing quality problems in manufacturing

Process control and optimization can improve product quality by detecting defects early in the process flow. This can also be accomplished more quickly than by traditional inspection methods, since various tasks can be automated that were previously performed manually.

Maximizing productivity in aviation

Preventing asset failures and accidents is critical for process-intensive industries. Unexpected incidents can grind operations to a halt for extended periods of time, necessitating expensive repairs and reducing output. Process control and optimization is, thus, key to continuing business productivity.

How does AI enable process control and optimization?

AI-enabled process control and optimization technologies provide operational cost reduction while also ensuring maximized asset lifetimes, product quality, and worker safety. This is accomplished using efficient, robust, and scalable cognitive modeling to enhance efficiency, productivity, and resource utilization, while also minimizing resource waste, work stoppages, asset failure, and the plant-wide carbon footprint.

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 Industrial AI Suite helps to extend 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

Drive your product manufacturing to higher levels of efficiency and effectiveness with AI-powered business process control and optimization, while also maximizing product quality and asset endurance.

Improve product quality assurance.
Gain better operational cost control.
Extend asset lifetimes.

AI-powered process control and optimization for the power industry

Increase power production, enhance worker safety, and drive more efficient day-to-day operations by implementing business process control and optimization.

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.

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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.

Case Studies: Learn more about AI for process control and optimization

The oil and gas industry can’t afford inefficient operations

Learn how the oil and gas industry reduces operational whiplash from market price swings, while also eliminating the resource waste associated with unnecessarily running assets to failure rather than performing AI-enabled predictive maintenance. Read our white paper

Why machine learning is the future of maintenance for manufacturing

Unexpected accidents and equipment failures can grind manufacturing operations to a halt, requiring unnecessary repairs and leading to reduced profitability or even compromised worker safety. Combining available sensor data with powerful AI process control and optimization models can mitigate these challenges and drive worker productivity and improved operational performance. Read our solution sheet

Machine learning is the future of predictive maintenance in aviation

Discover how process control and optimization helps the aviation industry avoid accidents and unnecessary maintenance activities. Predictive analytics and maintenance contribute directly to streamlined day-to-day operations and enhanced profitability. Read our solution sheet