Advanced Analytics Informing Supply Chain Insight
It’s ‘survival of the fittest’ as companies consider how to integrate and grow analytics operations.
The emergence of predictive and prescriptive analytics is stretching across industries, changing the way an array of businesses make decisions and manage their supply chain operations.
The material handling and supply chain field “definitely is more on the leading edge than the lagging edge in leveraging analytics,” said Carrie Lee, product manager—controllers for MHI member Omron Automation Americas.
“What we’re seeing is people are really looking at data to help them make their decisions,” Lee said. “There’s less of the gut feel, or ‘we always did it this way’ attitude. People are realizing the value of that data.”
Predictive analytics employs data to predict and anticipate future outcomes, while prescriptive analytics uses that data and predictive analytics to recommend courses of action. Sebastian Titze, business development and strategy manager for MHI member Beumer Group, said analytics helps bring a higher level of clarity and certainty to the supply chain.
“The general importance of data analytics in recent years is that it allows you to make decisions based on what you know instead of what you think or what you guess,” Titze said. “And that is a very big difference, especially in an environment like a distribution center that has so many variables.”
Lee agreed, saying analytics helps companies get a better view of what’s happening rather than “maybe” what’s happening.
“In the past when you didn’t have that good view of what was happening, you might have spent all day trying to fix a problem that was being caused somewhere upstream or downstream, and you didn’t have that realization of what was causing it,” Lee said.
As a result, Oscar Hasburn-Babich, Sr., strategic advisor, AI/ML Transformation at MHI member Dematic, said supply chain companies are becoming increasingly enthusiastic about data analytics. He said the risks to companies of failing to embrace analytics are clear. He sees a possible “Darwinist survival of the fittest” ahead.
“Just like e-commerce redefined the way we did business, and organizations who did not transform soon enough ended up subordinated to those who did, the data-driven revolution leading our ‘cognitive’ era may do the same,” he said.
Fortunately, John Schriefer, marketing communications manager for MHI member Lucas Systems, said advances in artificial intelligence tools have made these insights more accessible.
“The recent advances in analytics have less to do with improvements in data collection, and more to do with new artificial intelligence tools and technologies that are able to use and interpret all of the available data that is already collected in the distribution center,” Schriefer said. “AI—and, in particular, machine learning—allows DCs to turn data into predictive and prescriptive insights. Those insights were typically only available to a few companies that could afford to invest in highly engineered systems for things like workforce management and slotting. Those engineered systems are expensive to build and maintain. Machine learning-based solutions makes predictive analytics available to all.”
