When looking for cost-savings and efficiency measures, supply management organizations often turn to sustainability, data analytics and newer technologies like artificial intelligence (AI). Organizations are increasingly using AI to gather data streams to analyze, predict and optimize energy consumption based on a variety of internal and external factors, says Sudhi Sinha, vice president and general manager of digital solutions at Johnson Controls, a Milwaukee-based smart-buildings solutions provider.
“Supply management professionals think of the world broadly,” he says, “and sustainability is a broad, holistic issue. There is a great synergy when taking an enterprise view and looking not only at what happened in the past, but the future.”
AI helps supply management and facilities professionals prepare for that future, Sinha says. “When you are trying to sift through all the different data, you can take a heuristic approach or an AI approach,” he says. “In a heuristic approach, you need to know what problem you are solving and the conclusion you want to reach.”
But supply management and facilities management professionals don’t always know the problems, risks and issues that exist or could occur, he says. A facility’s sustainability and energy supply/demand are impacted by internal factors like its occupancy rate and the condition of its heating and cooling system, as well as such external complexities as natural disasters and climate change.
“A lot of things can change,” he says. “AI helps sort through the data, find the patterns and identify trends — without having to actually know what the problem is or what you’re looking for,” Sinha says. “AI brings that intelligence on the data. So, it’s a lot more powerful way of analyzing the unknown.”
Sinha notes that AI is being leveraged to impact sustainability in a number of ways, particularly to forecast future energy demand or consumption. “It can model different scenarios about what the future could look like,” he says. “We can also use AI to better identify faults or opportunities for improvement.”
An advantage to AI is that it isn’t bound to historical “rules” guiding how a situation is analyzed or dealt with, Sinha says. He offers this analogy to make his point: “For example, when I got my first car 25 years or so ago, I was told that I had to take it for an oil change every three months. So, I showed up every three months, paid $20 to get the oil changed, and my car was fine. Today, a lot of different factors influence the oil’s health in a car. Whether you need an oil change depends on how much you are using the car, the battery and the oil; the road conditions; and your driving habits, among other factors.
“So, to translate that to sustainability in a building, there are a lot of factors to consider. How do you identify your problem? AI helps you with that because you can lead with a lot more unknowns and deal with lots of parameters. It drives better optimization capabilities.”