How AI tells the story of information
As a college instructor who specializes in supply chain management and statistics, I stay enthusiastic about being a component of the educational growth of college students and business leaders, as this is the time when artificial intelligence and machine learning are redefining supply chain management. Incorporating matching learning and artificial neural networks within the classroom experience has shed light on how much AI can solve real-world issues by grappling with and bringing meaning to billions of lines of data.
I am oftentimes consulted by industry leaders to make sense of data so that they can make decisions with statistical significance. One thematic area that leaves supply chain executives perplexed is how to make use of information overload. The overwhelming amount of data collected by corporations leaves executives scratching their heads when they are asked to understand the complex and adaptive information that is processed through their businesses, especially when problems arise or decisions are to be made. Complex information is generally collected data that has several layers and countless moving components that can be too complex to understand.
The crux of the problem that executives face is how to have increased visibility across end-to-end processes in real time, when there are several interwoven variables that complicate such visibility. My students and I are addressing this issue with neural networks. Neural networks are becoming increasingly instrumental for artificial-based supply chains, as machines can learn and adapt to evolving information, like the human brain processes information. The good news is, neural networks can process, streamline and funnel this ever-changing data rapidly and quicker than the human brain, and produce an output that industry leaders can use to understand their data in an illustrative and meaningful way.
One local business that I had the pleasure of working with was TSD Logistics, for whom I employed machine learning to help it connect with its wealth of data. When its supply chain leader, CEO Ryan Berry, saw the output that artificial neural networks produced for the first time, he said, “Finally, a way to use real-time data to drive our decisions. Previously, we have relied on subjective thought or at best, spreadsheet analytics to evaluate past performance or forecast future growth. Machine learning has now allowed us to not only establish an objective baseline of where we are today, but it also has allowed us to define our future goals in terms that matter. Basically, Dr. Daigle has taught us how to trade in our dream for an actual plan.”
In an era of higher supply risk, greater demands, increasing competitive intensity and larger populations than ever before, supply chain excellence depends on an organization’s ability to orchestrate a complex process that involves acquiring raw materials, converting them to finished goods and delivering them to consumers. Supply chain management has become far more information-intensive, and supply chain professionals have been seeking a way to manage sometimes unmanageable amounts of information. Artificial intelligence has been in existence for decades, however it has been underutilized in supply chain management.
AI can process unsurmountable information instantaneously, learn from its own experience, comprehend new concepts and develop efficiencies that one person, nor company can do alone.
This is an exciting time for business executives, as they can now incorporate machine learning and neural networks in their processes to alleviate data inundation. Examining data with a focused lens, which incorporates statistical probabilities for decisional analysis, formulates a scientific approach to make judgements with supported evidence. A neural network can learn from ever-changing information, comprehend new efficiencies and develop them simultaneously. It then funnels the information in a meaningful way, rather than being inundated with information overload that makes it hard for executives to connect meaning to data.