This article is part of an ongoing series called Machine Learning for Manufacturers, which focuses on the uses for artificial intelligence (AI) in manufacturing. It began with our article introducing machine learning and AI, and continues to spell out the many ways that AI solutions are relevant to manufacturing and supply chain operations.
What is Anomaly Detection?
An anomaly is something unexpected, or, something that is simply not normal.
Anomaly detection is something that many industries engage in for the purpose of cleaning up their operations.
In a word, anomaly detection is the business of finding those unexpected or not-normal things, and deciding whether their existence is preferable or not.
Some anomalies are sudden, and some sneak by for a concerning amount of time, which can lead to lost profits and unfulfilled potential.
More often than not, anomaly detection is done to detect bad anomalies, because, well, there are not really a whole lot of good anomalies out there, and when there are, people tend to just call them lucky breaks.
One such example of an anomaly is fraudulent behavior, such as the intentional misreporting of goods bought or sold.
Back to Normal
In the world of business, a perfect day is one where nothing unexpected, that is also bad for business, happens.
This certainly holds in the manufacturing industry, where even one thing wrong can disrupt the entire supply chain.
More and more manufacturers are turning to AI platforms for anomaly detection, so they can quickly find and eliminate any problems that crop up during operations, before time and money become irretrievably lost.
With AI-powered anomaly detection, you will have 24/7 surveillance of the many areas of your operation.
An AI agent will scan your historical company data to know what is normal for your business, and what is not, and will alert you to any discrepancies found in incoming data.
To learn more about the different kinds of anomalies that AI can detect, read on.
The Eye of AI
What AI does to set up a system for detecting deviations from the norm is to first understand what the norm is for your supply chain operations.
This is typically done through a statistical method, where the likelihood of, for example, you paying a certain amount X for a given raw material, is given a probability.
A model is created that can give the likelihood of a piece of data, such as the cost of a recent purchase of a raw material, and whether that data fits into the statistical model or not.
It is sort of like a test that gets run every time the AI platform detects a new piece of data. If that piece of data fails to pass the test, then you will be alerted, and further action will be awaited.
Some of the most common anomalies to watch out for are sudden price changes that you may not be aware of, reports of lower goods created on the factory line, longer or shorter transportation times, equipment performance, and the like.
With anomaly detection AI, it becomes much easier to comply with legal restrictions while also keeping tabs on your overall performance, making it a worthy investment for
AI-Powered Data Solutions for Your Company
The ongoing supply chain crisis has made the time- and cost-efficient uses of AI in the manufacturing process even more relevant than ever, so do not hesitate to make the investment in AI, which has saved many manufacturers from going under in these turbulent times.
To learn more about AI-based solutions, manufacturing-related or not, reach out to Findability Sciences, a leading AI service provider. From predicting customer churn to installing chatbots in your supply chain, you are sure to find what you need at Findability Sciences.
Read other informative articles in our ongoing Machine Learning for Manufacturers series:
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