Anomaly Detection Algorithms in Supply Chain Analytics

Anomaly Detection Algorithms in Supply Chain Analytics

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Key Takeaways

⇨ Anamoly detection algorithms can be applied to a variety of scenarios, including credit card fraud, cyber security and asset management and maintenance.

⇨ These are the areas it is already actively being used but the opportunities to leverage these algorithms are plenty.

⇨ For SAP users specifically, having a library of algorithms available within SAP HANA allows seamless integration of algorithms with your data.

Anomaly detection algorithms do exactly what the name suggests – detect anomalies in the data. The simplest example is your body temperature. If you had a sensor measuring your body temperature, and the date is being fed to an anomaly detector in the form of time series data (like every one hour), if the temperature value exceeds 99 degrees Fahrenheit, the anamoly detector will flag the temperature as mild fever. Obviously, this example is very simple but the gist of these algorithms is the same. These algorithms look at data to identify the data points that are inconsistent with the rest of the data and are an outlier or anomaly. In this article, we discuss how this algorithm can be leveraged for supply chain analytics in SAP technology ecosystem.

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