Artificial Intelligence, Machine Learning, and Predictive Analytics – What’s Best for My Business?
Transportation management processes create data, and lots of it, from transactions among carriers and shippers. It’s what you do with this data that can revolutionize your business. Some businesses use data from TMSs to improve efficiencies in transport processes and performance of transport partners.
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.
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.
In a recent 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.”
While Artificial Intelligence (AI) is a system designed to act with intelligence; Machine Learning is a system designed to use information and learn from it, creating a decision or insight. 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.
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.