Data Overload Problem? Here’s How Supply Chain Executives Handle it

By Dr. Jamie Daigle, Business Faculty

As a college instructor that specializes in supply chain management and statistics, this is an exciting time to be a component of educational growth for college students and business leaders, as this is the time when artificial intelligence (AI) and machine learning are surfacing in supply chain management. Incorporating matching learning and artificial neural networks within the classroom experience has shed light on how much AI can do to solve real world issues through machine learning through its uncanny ability to grapple with and make meaning of 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 oftentimes leaves executives scratching their heads when they are forced to understand all 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 data collected that has several layers and countless moving components that are oftentimes too complex for one to understand.

[Tweet “One thematic area that leaves supply chain executives perplexed is how to make use of information overload.”]

The crux of the problem is how to have increased visibility across end-to-end processes in real time when there are several interwoven variables that complicate such visibility, something my students and I have been actively addressing with neural networks. Neural networks are becoming increasingly instrumental for artificial based supply chains, as machines can learn and adapt to information that is ever changing, like how the human brain processes information. The good news is that 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 utilize to understand their data in an illustrative and meaningful way.

One local business that I have had the pleasure of working with is TSD logistics.  Located in Texarkana, Texas, this company offers diversified transportation services throughout North America, including dry bulk, over-the-road freight shipments and specialized logistics services. This business is highly impressive, professional and is heavily involved in the Texarkana community through its contributions to local and regional charities. It was an amazing experience helping TSD utilize machine learning to help connect meaning to the wealth of data that is processed through their facility.

“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.”

~ Ryan Berry, TSD Logistics CEO

When local supply chain industry leader, Ryan Berry, CEO of TSD Logistics, saw the output that artificial neural networks produced for the first time he thought, “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 oftentimes 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, or even a company, could not do alone.

[Tweet “AI can process unsurmountable information instantaneously, learn from its own experience, comprehend new concepts, and develop efficiencies that one person, or even a company, could not 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 approach can learn from adaptive and ever-changing information, comprehend new efficiencies and develop them simultaneously. This 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.