What is industrial AI?
Industrial artificial intelligence, or industrial AI, is the application of AI to industrial use cases like the movement and storage of goods, supply chain management, advanced analytics, and automation and robotics in manufacturing.
Industrial AI is often differentiated from other types of AI because it’s more focused on the application of AI technologies than in the development of human or human-like systems. The datasets for industrial AI tend to be larger, but potentially lower in quality, than those for general AI. Industrial AI also has zero-tolerance for false positives or negatives, delayed insights, or unreliable predictions.
Industrial AI is uniquely appropriate for process plants because the huge amount of data and quickly changing circumstances are too complex for manual or even digital management.
Why does industrial AI matter to process manufacturers?
Process manufacturers are increasingly using AI-powered solutions to optimize operational efficiency, drive innovation, and improve profitability. Some of the use cases for industrial AI in process plants include:
- Predictive analytics/predictive maintenance that combines IoT data with deep learning to model large-scale networks, helping spot the earliest signs of anomalies anywhere in the plant, reduce unplanned downtime, and fine-tune maintenance scheduling.
- Self-aware “smart” equipment that can independently measure performance to generate alerts when degradation reaches a critical point or performance is reduced for any reason.
- Robotics and automation on the production floor can replace human involvement, thereby increasing efficiency and boosting production while improving human safety.
- Faster root cause analysis that investigates, understands and resolves process plant issues more swiftly to reduce bottlenecks in manufacturing flows.
- Complex supply chain management that increases visibility into every step of the process, including tracking raw materials, inventory, warehouse management, logistics, and last-mile distribution.
How should process plants implement industrial AI?
Spearhead a culture change
Industrial AI depends upon a data-driven culture that is willing to trust AI algorithms and predictions and ML models. Before introducing industrial AI, it’s crucial to educate your employees about the value and limitations of AI approaches, so that those individuals who will use and apply your new solutions are willing to trust their guidance.
Prepare the data foundation
All types of AI, including industrial AI, are powered by data. It’s not enough to simply generate the data; process plants need to set up a system of collecting raw data, verifying data quality, cataloging data, and mapping data types. Data should be stored in data lakes or repositories that are easily accessible for industrial AI applications.
Decide on industrial AI applications
Like with every business strategy, process plants need to begin by defining the most relevant short, medium, and long-term use cases for industrial AI across the organization, and then grouping and prioritizing them to identify which use cases will deliver the greatest ROI to help quickly reveal value for the new approaches.
Gather the necessary AI talent
Industrial AI can require new skill sets and capabilities that process plant HR teams hadn’t previously considered when hiring talent. It’s important to assess the skills already present in your workforce and define which roles need to be filled. These could include data engineers, analysts, and more process engineers, as well as data scientists and solution architects.
How does industrial AI improve productivity in process plants?
By helping process manufacturing companies to automate workflows, increase safety, enhance supply chain management, improve performance, and cut downtime, industrial AI can remove roadblocks holding plants back from reaching operational efficiency. Industrial AI affects multiple manufacturing use cases that continuously drive profitability and help companies maintain their competitive advantage in the long term.