Managing and predicting industrial performance with artificial intelligence

Contributed Article
May 4, 2020 9:04 am
Ahmed Khashan, Cluster President Gulf Countries, Schneider Electric.

Artificial intelligence and automation’s value in the industrial space is undeniable. Artificial intelligence has the potential to skyrocket rates of profitability in manufacturing by an average of 39% by 2035. Artificial intelligence and automation present a great opportunity to augment the essential human expertise of asking the right questions based on the specific needs of the environment and context. 

This learning is placed into a trained model, which can be deployed as close to the action as possible, transforming both the rate and the accuracy of prediction and decision making.

Artificial intelligence and machine learning have evolved significantly, but we are now really starting to see the effects of what intelligent systems can do. In the data center, algorithms that have been built for task automation and predictive maintenance are becoming more refined, allowing administrators to focus less on routine tasks and more on future planning.

As artificial intelligence and machine learning algorithms get more refined, their accuracy improves. Already, machines such as intelligent UPSs can alert us when they need a new battery or troubleshooting. Going forward, algorithms will leverage historical data to predict more precisely when something needs maintenance. So, in addition to telling something is about to fail, intelligent systems can minimise the chances of failure thanks to data-driven predictive maintenance models.

There is an endless set of applications in the industrial space of the Internet of Things for automation and artificial intelligence, be it smart factories, oil and gas facilities, petrochemical plants, office buildings, and even smart homes. 

Wherever there is infrastructure, there is an opportunity to use artificial intelligence and automation. Before we look there, customers need to start connecting devices and start looking at how to aggregate data. As you aggregate that data, as you can start to look at broader trends, you could start to bring in things like machine learning.

In the industrial space, that question often is, how can I constantly improve efficiency while ensuring uptime? Artificial intelligence can answer this question with data-based models made to predict outcomes such as when will this asset fail?

Critical operations and industries demand accuracy, so investing in experimentation is crucial for building the right models, which always will be as dynamic as the human intelligence they are meant to emulate.

Data scientists do not always know what any given artificial intelligence model’s outcome will be, as outcomes depend on how predictive the data are. Artificial intelligence models therefore must start with a certain level of accuracy and improve over time and, in turn, be re-trained, re-versioned, and re-deployed within situational context.

Schneider Electric understands the importance of artificial intelligence. Schneider Electric is invested in developing predictive analytics and condition management tools, for example, to enable customers to predict failure long before downtime actually happens.

Key takeaways

  • As AI and machine learning algorithms get more refined, their accuracy improves.
  • AI has the potential to skyrocket rates of profitability in manufacturing by an average of 39% by 2035.
  • Data scientists do not always know what any given artificial intelligence model’s outcome will be.
  • Wherever there is infrastructure, there is an opportunity to use artificial intelligence and automation.
  • AI can answer this question with data-based models made to predict outcomes such as when will this asset fail.

By Ahmed Khashan, Cluster President Gulf Countries, Schneider Electric.

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