Leveraging Python and Machine Learning for Anomaly Detection in Order Tracking Systems

Abstract

Order tracking systems are now an inherent part of supply chain management, guaranteeing the unhampered flow of goods, real-time monitoring of shipments, and improved customer satisfaction. Nevertheless, such systems are frequently faced with impassable hurdles, including delayed shipments, fraud, inconsistencies in data, and mismatches in delivery. Conventional rule-based detection approaches are less flexible and scalable to process huge volumes of complicated data in real time, and it is challenging to detect concealed anomalies in logistics networks. The integration of Python and machine learning has emerged as a breakthrough approach to anomaly detection in order tracking systems. Through the utilization of historical data, sensor data, and transaction logs, machine learning al- gorithms identify anomalous patterns in shipment data. Unlike conventional approaches, AI-driven anomaly detection utilizes supervised and unsupervised learning models to predict, classify, and prevent anomalies before they impact logistics processes. Supervised learning algorithms like decision trees and support vector machines (SVM) are efficient in identifying pre-defined anomalies, whereas unsupervised learning algorithms like k- means clustering and autoencoders are proficient in identifying unknown patterns. Python has a comprehensive library and framework base, such as TensorFlow, Scikit-learn, Pandas, and NumPy, which enables effective data preprocessing, feature engi- neering, model training, and anomaly detection. As AI continues to be at the core of supply chain operations, companies are using Python-based applications to enable better real-time decision- making, fraud detection, and logistics optimization. The research explores the utilization of machine learning in Python for the detection of anomalies in order tracking systems. By analyzing actual datasets and testing different algorithms, the research aims to determine the optimal methods for shipment anomaly detection. Additionally, challenges related to data quality, com- putational overhead, and model interpretability will be discussed, along with possibilities for future enhancement in AI-enabled order tracking systems.