Logistics anomalies can create problems in the shipping process, contributing to issues that affect the business and customers. They put a kink in an otherwise good process to cause problems within the supply chain and affect business operations as a whole.
Technology can help. In particular, businesses can rely on machine learning in logistics as a solution to anomalies. What is anomaly detection, and how can machine learning be used in logistics to help with anomalies? This article covers the answers to these questions.
In addition, we present seven use cases of machine learning detecting anomalies in shipping activity or spend. These cases show how machine learning in the logistics industry facilitates finding these anomalies, allowing the company to take action and correct the mistake.
What is machine learning in logistics? An article by Addepto explains that it can carry out the tasks of analyzing data sets to improve operation methods. It facilitates this benefit in numerous ways, including by streamlining:
The Addepto article further shares ways companies can improve performance tasks with machine learning. It finds supply chain patterns that impact logistics operations the most. Through this data, businesses can see ways to perform better in all aspects of logistics, including warehouse management and analysis, demand prediction, supplier selection, route optimization, and supply chain planning.
Improving performance in these logistics processes means reduced theft, better supply management balanced with demand, reduced shipping costs, and more. These insights turn into efficient business processes, better profits and business growth.
Let's take a closer look at the last one on the list of logistics benefits from machine learning: anomaly detection. This is a frequent purpose for relying on machine learning. This purpose enables companies to discover outliers to protect the company from threats like adversarial attacks, fraud, and network intrusions.
As the term implies, anomaly detection is the finding of anomalies. What is an anomaly? In basic terms, it's something that deviates from the standard. For instance, you might notice a significant increase in activity that doesn't fit the usual pattern.
An anomaly like this can be cause for concern. It often indicates a problem, which could include an attack, fraud, an error, or something else that needs to be addressed. It's important to look for and catch these anomalies when they occur, which is where anomaly detection comes into play.
In logistics, anomaly detection catches exceptions and outliers that are impacting efficient warehouse management and shipping processes, keeping the supply chain running as smoothly as possible.
Machine learning provides a way to look through massive amounts of data and catch outliers faster and better than humans can do on their own. Plus, you gain the support of technology rather than putting staff hours toward this task.
With the support of artificial intelligence, machine learning can find anomalies in data related to freight and shipping. Once it is set up, machine learning takes care of this task independently without needing supervision. It can help when you know something is wrong but are struggling to find the source of the problem. It also seeks out anomalies on its own and can identify them before you realize there is a problem.
Machine learning in logistics can search through data in systems like TMS, OMS, and WMS. It seeks and finds data glitches, errors, and other problems that are not fitting standard patterns and interfere with proper business processes and customer satisfaction. Companies can use this information to identify and solve problems, thus streamlining logistics processes.
As supply chains become more complex and nuanced, it's crucial to have a powerful tool that uses true unsupervised machine learning to uncover instances of changes happening that will impact your transportation spend or your end customer experience.
Intelligent Audit's proprietary machine learning algorithms can do what no person or team of people could ever accomplish at a significant scale and speed. Backed by our team of experts who can explain the data, you pave the way for a more efficient, cost-effective supply chain.
Let's go through 7 use cases Intelligent Audit has already uncovered in anomaly detection powered by machine learning.
It's one thing to read about a concept and another to see how it works in action. The following use cases help to show how machine learning in the logistics industry works in real life to detect anomalies. Through these cases, you'll better understand the types of anomalies this technology can find in logistics.
Anomalies in storage and shipping can interfere with the supply chain, costs, or customer experience. Machine learning in the logistics industry provides a way to detect these anomalies better than employees can do on their own. The list of seven use cases demonstrates how effective machine learning can identify various anomalies so companies can understand and address them. Machine learning helps to quickly prevent or address mistakes to keep logistics operating as smoothly as possible. Contact us today to learn more about how Intelligent Audit's proprietary machine learning and anomaly detection can provide you with peace of mind.
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