Is the best return on investment for Green supply chain improvements - especially for manufacturers - an infusion of artificial intelligence?
That was the interesting proposition recently put forth by Jane Zavalishina, CEO of Yandex Data Factory, in a recent guest column on the ManufacturingGlobal.com web site. Yandex provides such AI software, so Zavalishina isn't exactly an objective observer, but TheGreenSupplyChain believes her thoughts are worth pondering.
A big challenge for manufacturers is that many opportunities for Greening processes require major investments in production line technology or a factory's energy sourcing.
Not only are these types of initiative often very capital intensive, they are generally challenging and time consuming to roll out over several facilities, let alone globally.
This is particularly tue "when you consider that the heavy industries - that contribute most to the waste and resource consumption – operate in a slim-margin sector that is struggling to remain competitive," Zavalishina adds." "Instead, businesses are focusing on their survival rather than investments into a sustainable future."
Looking for opportunities with software may be a lot more effective and time efficient, Zavalishina says.
The type of artificial involved here is called "narrow AI," not the "strong AI" that is being used to mimic the general human consciousness. Narrow AI, Zavalishina says, is very good at specifically defined, constrained tasks, where it can deal with uncertainty better than humans do, such as in evaluating a manufacturing process over time.
In the real industrial world, nothing is ever fully known, Zavalishina says. The exact composition of raw materials vary, as do external conditions, and multiple fluctuations occur in every process.
What's more, Zavalishina says, classic science can only describe the physical or chemical processes happening inside the equipment with a degree of certainty.
"Process control is never precisely accurate: the operations happen within a permitted range and a great deal of variability, which results in inefficiencies," Zavalishina observes. "This can come in the form of the excessive use of a certain raw material, suboptimal energy consumption levels, scrap due to defected production, or poorly planned logistics. These inefficiencies are as equally as bad for environment as they are for the bottom line."
What makes AI especially attractive, she notes, is that AI can drive improvements without changing the process itself.
How?
 AI provides more precise decision-making for each individual process iteration, having learned from previous cycles. For example, Zavalishina says AI can precisely model the expected output of a process, thereby allowing the more precise identification of raw material needs. It also can recommend the best operating parameters to cut down energy use without affecting the throughput. It is also good at detecting hidden product defects early on to reduce processing of bad lots.
Instead of operating within a wide range, AI can allow manufacturers to consistently reduce variability – with no significant capital investment required.
As a parallel example, Zavalishina cites the NEST thermostat, which dynamically adjusts central heating, reducing a home's energy consumption.
"Imagine if that same practice was scaled to the whole shop floor of a massive factory," Zavalishina writes.
She also cites her own company's work with Magnitogorsk Iron and Steel Works, a major metals producers, which Zavalishina says was able to use AI to decrease of ferroalloy 5%, equating to expected annual savings of more than $4 million per year from just one AI application.
There are Green benefits in most of these improvements, Zavalishina notes, and concludes by adding that "Maybe it's time to include AI in your sustainability roadmap."
Should more companies be looking at AI to deliver operational and Sustainability benefits? Or is it overrated? Let us know your thoughts at the Feedback button below.
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