Supply Chain Software Innovations
When we look across the supply chain, we look at the DNA from the experts that have worked in the industry for 20 to 30 years. As a technology business, we recognize that being able to harness that knowledge into the DNA of a software platform is critical. This is a path critical to organizations. It is necessary to help organizations move forward with the next-generation supply chain software.
We have contemplated the tools required to create intelligence in the DNA of a software platform. One of the things we have to contemplate is the machine learning aspects. It’s obvious that there are tools around machine learning. But, we also have to contemplate the capability of taking head knowledge from an industry expert. This is an expert who has worked for a third-party logistics provider or for a shipper for an asset carrier.
Enabling More Proactive Business Decisions
We also contemplated having a platform that can translate the head knowledge of the resource into the DNA of the software. This helps that company make bit better and more proactive business decisions. We deem this path as critical in moving the company forward in the industry. This is from a succession perspective as well.
So, a couple of years ago, we talked about and launched an acronym in the industry around this innovation. It’s now what we call applied intelligence because it’s not only artificial intelligence. The application of intelligence is contextualizing data and information that resides in data sources within organizations. It’s contextualizing that information. And converting that to actionable tasks and recommendations. This is to the benefit of the organization’s P&L and their profitability.
LES: Making Decisions with Prescriptive Capabilities
LES is an acronym that we introduced into the industry about two years ago. It stands for logistics expert system. As we contemplate quadrants from a Gartner perspective, we try to push the needle from an innovation standpoint. We think about legacy TMS quadrants, WMS quadrants, all the various supply chain software technologies. As we do, there’s a method of thinking that we want to try to create across the industry. And that is for everybody to start thinking of their system as a logistics expert system. Not only a WMS or a TMS with bits and bytes and functionality.
There’s an interest in recommendations. We have seen AI moving in the direction of starting with prescriptive or starting with predictive capabilities. Then it goes into prescriptive. The question is: at what point does the system become the means by which decisions are made? This is instead of giving recommendations to a human who then acts on those recommendations. That’s a great topic. It’s a two-fold process. It’s kind of like climbing a mountain so you don’t jump to the ladder within an organization. Part of the relevant usage of a machine and the machine learning and the AI component is a trust factor.
Applied Intelligence and Supply Chain Software
So, a lot of times organizations don’t want to dive head first into all things AI across their organization. Being able to create the user intelligence combined with machine intelligence. This is what we term applied intelligence. Making that move from that predictive to prescriptive from a platform capability standpoint. A merger of data that gets entered into a system then gets analyzed by various ML experiments. But, also user capabilities from a decision point to further train the platform for those desired business outcomes.
From a tool based perspective, you can follow what’s called a machine managed machine learning. This is where you have users providing input on the predictive component. But, you have a machine learning component. This is where there’s no user interaction. It’s where the platform itself evolves and recognizes what the business means to achieve.
Software that Helps Execute Decisions
It then starts helping make decisions and executing those decisions. It’s all around a trust factor of allowing the tool, the machine, and the software to make those decisions based on contextual recommendations. It then executes interests and uses the word trust. Or whether that output is predictive or prescriptive. Is it going into one end coming out the other? Is it a black box in the middle? Does the human user at the other end understand how the system came to that conclusion? Or again, is it a matter of, trust me, I’m a machine. I’m getting better and better all the time and I know what’s right for this company also.
It’s best to answer that question from a trust factor perspective. The user doesn’t know how the machine came to the prescriptive recommendation and execution. But, the user cares about the results. From a business perspective, you think about business value and using software to drive business value and improve your company’s profitability. It is about initial trust to allow the machine to do the work.
Using the Right Tool for Supply Chain Software
On the other side of that, what you’re evaluating is did the machine produce better business outcomes based on what we’re trying to do. So, a user can understand and comprehend that it’s kinda like digging a ditch. Would you rather do it with a shovel? Or would you like a tool or a machine, ie. an excavator, to help you dig through that ditch, whatever the definition of ditches. And the business that tried to dig the debts.
You don’t know how all the components work. But, if you can see it working and you can see the results. That allows you to use the machine from a trust factor to make better and more business decisions on the organizations. Does that lead us to assume that going forward in the future, the percentage of decisions made by the machines is going to increase? We see that evolution.
We would like to see that evolution within our industry. Other industries have adopted AI and ML type methodologies and tools for the industry’s advancement. So, one of the things that we’re hoping, and the reason we introduced the acronym LES. Not that an expert system is new. But, when you combine that to a logistics expert system and that capability of intelligence in that layer of intelligence can exist. Added to that the preexisting software platforms across our industry. It becomes a mindset shift to change the trajectory of how organizations across the supply chain look and evaluate their system.
Is My Software Intelligent?
Organizations can ask themselves a question, is my software intelligent? Is my software more intelligent than my users? We see a future where the machine and the software application and the platforms should make better decisions. Because when you have a machine analyzing data across a lot of data sources, most users don’t have admin logins across their companies. This also includes many platforms and software systems. But data from a WMS and data from a TMS and data from an ERP, whether a single user has admin access to all three platforms.
There is data residing within those three systems that could, in fact, make a user within a TMS more intelligent. You can pull that into a data lake. Then you can have an intelligent supply chain software application in a platform that can analyze data among the three systems. It can then give recommendations to the user, and then help execute.
That’s a world where we’re going to see some gain efficiencies. That’s an exciting world I’m describing. This is all to understand where we’re all going in the world. Software intelligence and artificial intelligence experts, systems, and the like. Thanks for being with us. Thanks for having me. Thank you very much for watching.