What is machine learning and how does it apply to logistics? It seems that as of lately, machine learning has been another term that gets thrown around repeatedly when people talk about the supply chain, logistics and all around optimization of processes. In order to better understand this phenomenon, we first need to be familiar with the term, what it means and how it applies to logistics in ways that are of our interest and concern. Machine learning is a way to apply advances in the area of artificial intelligence, that allows systems to improve performance based on experience, or in order words, learn as they go along. The purpose of this aspect of computing is not so much the programming of the computer, but instead in giving those computers the ability to create programs that can access and interpret data directly and learn autonomously. Machine learning and computational statistics are two disciplines that go hand to hand because both focus on making actual predictions based on algorithms created to properly process data and make decisions based on that information.
Machine learning algorithms are usually divided into two categories, supervised and unsupervised learning.
Supervised machine learning algorithms focus on the application of information that has been previously learned and labeled to predict future events. What that means is that based on data acquired previously, the algorithm allows producing an inferred function to make a prediction about accurate output values ON top of that, the algorithm also compares these values with the original intended output and makes corrections for future iterations. On the contrary, unsupervised machine learning happens when the information the systems use to learn, is not labeled nor classified. In these cases, the system doesn’t necessarily have to figure out a right output, but instead it can draw inferences to find similarities and expose hidden structures found within uncategorized data. In summary, machine learning is able to analyze massive amounts of data in an organized and efficient manner, so it makes sense that it can be used alongside with other applications that we have mentioned previously here at David Kiger’s Blog, such as the Internet of Things, Big Data and cognitive technologies having to do with Artificial Intelligence. All of this is done with the mission on the mind of being able to predict and anticipate many of the variables that are present and that govern the world of logistics.
New technologies in the areas of information and telecommunications have certainly made a strong impact in many sectors. Not only is logistics included in such list, but it is also one of the regions that have been impacted with much force. Think about the basic purpose of what logistics represent and add to that the possibility of using such applications to not only analyze massive amounts of data but also to put that information into good use by generating solutions, anticipating needs and learning from the decisions that are being made. The possibilities are almost endless when it comes to logistics and advanced artificial intelligence working together, and it touches upon on pretty much every aspect of the field from manufacturing to warehousing and shipping.
It is worth mentioning that as groundbreaking as this information may seem, it isn’t something new in the field of logistics, is just that it is only today that we are able to collect data and analyze it at such staggering pace that it seems like we are walking into a new area of technological advances.
Some of the ways that machine learning can apply to logistics include for examples applications of face recognition that can now be used in stores or warehouses or also to identify other types of goods by their shape, color and size. Driverless vehicles can also be aided by the use of machine learning and that is something that people have been talking a lot about lately. Perhaps one of the best and most popular applications of machine learning deals with optimization methods and predictions and forecasting. In the case of transportation, for example, you can use tools like this to predict weather patterns and to forecast conditions in order to avoid delays in shipments or mistakes by operators. Another big win can be gained from predicting consumer behaviors and that sort of analysis. Being able to predict the way consumers interact with the market is a great way to stay ahead of the curve and to be proactive about the things that people want, need and are willing to compromise when it comes to achieving a balance between fast shipping times, product quality and customer service. It is important to understand that machine learning is an amazing tool that is here to stay, and with the latest advances in technological development, it is already changing in ways that will amaze you, so it is imperative to get with the times and start taking advantage of the opportunities that it provides.
* Featured Image courtesy of Pixabay at Pexels.com