Beyond the Hype: Practical Applications of AI in Industrial Automation
If we look at how AI is treated in the manufacturing sector, the primary focus area would be the transformation of industrial automation from being rule-based into a sophisticated, context-driven automation wherein AI systems decide the course of action for every machine or equipment used.For years, the manufacturing and industrial sectors have faced a significant skill challenge when experienced employees and technicians retire or switch careers. The learning curve and dedication required to master complex workflows and processes in the production cycle often created roadblocks for replacement employees and led to significant productivity loss for the business.
Industrial automation enabled machines to improve their throughput, but there were still decision-making and oversight activities that needed extensive skilled manpower.
This is where artificial intelligence (AI) can step in to make a major impact in the sector. The ability to analyze, interpret, understand, and adopt knowledge makes AI systems a formidable entity that can address the serious problem of skill shortage in the industrial sector. Studies estimate that the market size for Industrial AI is poised to grow to nearly USD 191.76 billion by 2034.
If we look at how AI is treated in the manufacturing sector, the primary focus area would be the transformation of industrial automation from being rule-based into a sophisticated, context-driven automation wherein AI systems decide the course of action for every machine or equipment used.
Where does AI in industrial automation stand realistically?
Over the past couple of years, there has been considerable movement across businesses in all sectors thanks to AI adoption. From Generative AI to self-driving vehicles, the scale of impact has been tremendous. But in the case of industrial automation, it takes more than just flashy concepts to streamline business operations. Key stakeholders need to understand the realistic opportunities they have at their disposal to transform industrial automation with AI.
On this note, let us explore some of the key areas where AI can go beyond the hype and offer real-world practical applications in the industrial automation scene:
Predictive maintenance
As competition heats up, manufacturers need to stay nimble and agile to respond to demands and accelerate productivity with minimal delays. But standing in their path is the unpredictable maintenance cycle that industrial equipment often faces. But with AI, things are not unpredictable anymore. Machines generate tons of data about their operational cycles. From patterns of vibrations to cycles of revolution of turbines and other components, tons of operational parameters can be represented by data streams.
With AI systems, these data streams can be processed and analyzed to unwrap underlying conditions that may lead to loss of performance. Thus, these insights can help in deciding predictive maintenance schedules to boost equipment health. Capturing earlier signs of performance degradation can help in optimizing work cycles accordingly and increase the longevity of machinery. This will also eliminate unexpected disruptions when fault levels escalate, resulting in machine failures.
Anomaly detection
Industrial systems often handle hundreds of thousands of moving parts in their operational work cycle daily. Like performance drops, there may be instances where equipment may falter or deviate from its intended production behavior due to several reasons. This will ultimately impact the end product and can escalate into serious repercussions for the enterprise when customer experience is impacted.
However, by deploying AI, it becomes easier to identify and isolate anomalies and further investigate their root causes. Deviations in operational behaviour can be easily captured by AI systems by constantly analysing production data. Benchmarking output data streams against defined outcome objectives will reveal any deviations. Discovering such deviations happening at a granular level is nearly impossible when done manually. But AI systems can easily solve this challenge. Detecting anomalies in machine or factory outcomes helps businesses to quickly implement remedies and ensure that the end experience of customers is not impacted.
Process optimization
With AI, industrial organizations can implement an advanced level of process optimization for their end-to-end operations. By understanding demand, supply chain dynamics, production cycle behaviour, and other key factors that play a role in the whole operations, AI systems can help create the most optimized production processes. It can focus on efficiency, cost control, optimal resource and machine utilization, strategic market demand fulfilment, and other major areas.
Using key metrics of performance, AI systems can create operational workflows that eliminate bottlenecks, provide enough capacity utilization for machinery, and support energy conservation initiatives. Combined, these process optimization strategies help in transforming industrial companies into highly competitive enterprises that can easily scale their production to meet growth ambitions.
Unlocking the true value of industrial automation
Considering the real-life impact that AI can bring about in industrial automation, it is important for manufacturing companies to invest in the right AI technology to unlock the true value of industrial automation. From boosting efficiency to lowering operational costs, the tangible benefits are too sweet to ignore. However, gaining an edge in AI-driven transformation requires industrial organizations to have a well-defined AI implementation roadmap that accounts for all strategic growth objectives of their business.
This is where an influential technology partner like CSI can help bring measurable value to the mix. Get in touch with us to learn more about building the most optimal value-driven impact with AI on industrial systems.