Artificial Intelligence, Machine Learning, and Predictive Analytics in the Supply Chain
The world of transportation and logistics management looks completely different than it did even 50 years ago. Gone are the days of pen and paper and jotting down haphazard notes when on the telephone with a carrier booking freight. Now, technology is now ruling supreme. With the advent of advanced cloud-based transportation management systems, there is a cornucopia of detailed data that can be stored and accessed on the cloud. Just about every touchpoint in the supply chain can create data, and lots of it, from initial order through final mile delivery. You might hear this type of data referred to as “Big Data.” Simply having Big Data isn’t enough to improve your supply chain, however. It’s what you do with the data that can revolutionize your business.
There are several buzzwords circulating the technology industry that relate to the use of this new-found trove of information. These terms are “Predictive Analytics,” “Machine Learning (ML),” and “Artificial Intelligence (AI).” Each of these buzzwords refers to advanced processes for leveraging Big Data to improve processes and business outcomes.
If you’re like many shippers in an industry undergoing rapid change, you’re probably wondering how these terms apply to you.
Definition: Predictive analytics refers to the concept of extracting information from data (such as from Big Data) using technology in order to decipher patterns and extrapolate likely future outcomes. In other words, using data to forecast what might happen in “what-if?” scenarios.
You might be able to imagine a situation in which predictive analytics could help your company’s supply chain. Maybe you want to know the likely delivery times on a specific lane so that you can determine the lead time you need for manufacturing your product. Or perhaps you want to estimate the likely disruption you’ll experience in the wake of a forecasted hurricane about the hit your service area. These and many other “what-if?” questions can be answered (as close as possible) with the help of predictive analytics.
If you’re like many shippers, this type of advanced technology might seem outside of your grasp. With the help of a transportation management system with built-in predictive analytics functionality, however, any shipper can leverage this futuristic tech. TMSs can provide predictive analytics to give you the immediate intelligence you need to make better logistics decisions every day. Whether it’s holding your carriers accountable through carrier scorecards, managing your yards and docks more efficiently, or simply ensuring that you are paying the lowest rates for the best service, predictive analytics gives you the information you need to make decisions that will be real game-changers for your business.
Artificial Intelligence (AI)
Definition: Artificial intelligence, often refered to as simply AI, is the practice of training computers to perform tasks that would typically require human-level intelligence to complete.
You’ve probably come across several different forms of AI in your day to day life. Common examples include Apple’s Siri and Amazon’s Alexa technologies. These are artificial “humans” which can listen and provide back answers as though having a real-life conversation. In the supply chain industry, artificial intelligence can come in the form of information gathering platforms for customers and suppliers to interact within. Chatbot interfaces and other data-gathering technologies can help retailers, manufacturers and customers work together more collaboratively. AI can help to identify trends and analyze changes in demand.
Definition: Machine learning is the a branch of artificial intelligence and refers to the method that computers use to learn and change their behaviors based on data gathered through analytical model building. This concept is based on the idea that a computer can process data, much like a human’s brain can, and change its decision making processes to suit the new information without human intervention.
Machine learning and artificial intelligence often get confused because of their close correlation. The simplest way to understand their differences are through examples. One example of ML-based technology is that of any streaming music app. These apps make suggestions to the user based on location, demographics, and other inputs. This is an example of AI. What makes it an example of machine learning is the fact that music apps often “learn” their users’ preferences. As a user spends time listening or fast-forwarding past certain songs, the technology learns the user’s preferences and can suggest more relevant music. Other examples of technologies that “learn” include spam filters on email servers and ads displayed on social media accounts based on past purchases.
While AI is a system designed to act with intelligence, ML is a system designed to use information and learn from it, creating a decision or insight. In the supply chain, machine learning uses historical data to improve existing processes, define new routes, uncover bottlenecks, discover shipping errors and more. It is adaptive so that the data utilized increases efficiencies while providing value to shippers and carriers for things like pricing models.
In an article in Forbes, Machine Learning (ML) is described as making it “possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks’ success, while constantly learning in the process.”
Determining What’s Best for Your Business
Many people are confused about the differences between predictive analytics, machine learning and artificial intelligence. Predictive analytics uses data to help you understand possible future events by analyzing the past. It uses a variety of statistical techniques, including machine learning and predictive modeling, along with current and historical statistics to predict future outcomes, which may be customer behaviors or market changes.
Bill Cassidy in the JOC says to “think of AI as Machine Learning on steroids. It functions through an ongoing series of algorithms and internet-connected devices, the Internet of Things (IoT), to make data-based decisions before shippers overlook something.” AI can help to better manage freight bills by automating audit and payment processes to uncover billing and compliance issues, for which it can then trigger chargebacks to carriers.
With AI, you can proactively identify potential disruptions, such as changes in weather patterns that can lead to flooding. Proactively mitigating risk ensures your shipments can be made on time to the right place for the right price.
Predictive analytics, AI and ML may overlap in certain areas, but these technologies can help us to uncover hidden capacity or make important cost-to-serve decisions by viewing carrier rates side-by-side. The bottom line is that technology is making shipping operations smarter for companies of all sizes.