AI-Driven Spend Analysis Application: Integrating Purchase Order Classification Proactive Procurement Forecasting & Spend Visibility

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Mehdi Rizvi, Mohd
Issue Date
MSc in Business Analytics
Dublin Business School
This research introduces a groundbreaking advancement in the realm of spend analytics within procurement, presenting an automated solution that integrates cutting-edge machine learning models with Microsoft Power BI. Utilizing Convolutional Neural Networks (CNN) for precise text classification of Purchase Orders (PO) and employing RandomForestClassifier, RandomForestRegressor, XGBClassifier, and XGBRegressor for forecasting both spend and the most procured categories, this methodology constitutes a substantial contribution. The implementation of batch file automation streamlines all process components with a single click. The CNN model enhances efficiency and accuracy by automating the classification of purchase order text, significantly reducing manual efforts in procurement. Simultaneously, the RandomForest and XGBoost models contribute to robust forecasting, delivering proactive insights. The study meticulously details the development, training, and seamless integration of these models within the Power BI environment, offering insights into both challenges and successes. Real-world application and rigorous testing validate the practicality of the solution, demonstrating improved accuracy in text-based purchase order classification and resilient forecasting capabilities. Results indicate a CNN accuracy of 70% for transaction categories, highlighting its adaptability. Forecasting models, particularly XGBoost, exhibit superior accuracy with minimal deviation, achieving a variance of 1.5%. This automated approach transforms spend analysis methodologies. The paper concludes by discussing broader implications, and potential advancements, and suggesting future avenues for refining ensemble machine-learning applications.