Anomaly Detection with Machine Learning Technique to Support Smart Logistics

Accurate planning and cost-effective management on product delivery are among key factors leading to the success of most manufacturing sectors in the current era of the fourth industrial revolution, or Industry 4.0. In this work, we focus our study on the development of a model based on machine learning technique to support smart logistics by detecting anomaly events on the big stream of electronic orders obtained from ubiquitous customers. We use the data-driven approach to build the order-anomaly model and present the built model as the classification and regression tree. Our model in a tree formalism is to be used as an automatic detector for unusual events inherent in the customers’ order stream. The anomaly events should invoke special care in the smart logistics environment that delivery tasks are performed in an automatic manner. Early detection of anomaly ordering events is expected to improve accuracy on delivery planning.

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