Supervisor
Dr. Kislay Raj
Programme
HDIP in AI Applications
Subject
Computer Science
Abstract
Mid-sized grocery retailers face a persistent challenge in balancing on-shelf availability with minimizing spoilage of perishable goods. This dissertation addresses this issue by developing a data-driven forecasting and inventory simulation framework within Microsoft Fabric, leveraging scalable data ingestion, Spark-based processing, and advanced machine learning. Using multi-year transactional data enriched with holiday schedules, promotions, and macroeconomic indicators, the study compares classical ARIMA models with XGBoost to capture complex demand patterns. Rigorous hyperparameter tuning in a distributed environment demonstrates that XGBoost outperforms baseline models in terms of MAE and MAPE, particularly during promotion-driven spikes. Inventory simulations based on these forecasts reduce stock-outs by over 15% and cut perishable waste by 10-15%, delivering both economic and environmental benefits. The research underscores the value of ethically guided, reproducible machine learning pipelines for mid-sized retailers, offering a practical path toward smarter inventory management and improved sustainability.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Andrei Ungureanu, D.
(2025) Demand Forecasting and Inventory Optimization in Mid-Sized Grocery Retail Using Machine Learning: A Data-Driven Approach to Minimizing Stock-outs and Waste. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.91