Degree
Master of Science in Data Science
Department
Department of Computer Science
Faculty/ School
School of Mathematics and Computer Science (SMCS)
Date of Submission
Fall 2024
Supervisor
Dr. Tariq Mahmood, Professor and Program Coordinator MS(CS) and MS(DS) Programs, School of Mathematics and Computer Science (SMCS)
Keywords
Demand Forecasting, Supply Chain, Inventory Management, Sales Forecasting
Abstract
Efficient demand forecasting is essential for profitability and sustainability in the retail food industry. This project focuses on developing a predictive sales model for a Pakistan-based retail food brand to overcome the limitations of manual forecasting methods, which often result in waste from overstocking, lost sales due to stockouts, and increased operational costs. Using sales data from five high-performing branches, a multiple linear regression model enhanced with dynamic subset feature selection was developed to predict daily donut sales with precision. The model achieved up to an 82% reduction in forecasting errors (RMSE) compared to traditional methods, demonstrating its ability to capture complex sales patterns that manual approaches often overlook. To ensure practicality and scalability, a web-based application was created, offering branch managers automated data integration, forecasting sales and actionable insights.
Document Type
Restricted Access
Submission Type
Research Project
Recommended Citation
Inam, N. (2024). Demand Forecasting for Retail Efficiency: Minimizing Waste and Maximizing Profitability (Unpublished graduate research project). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/research-projects-msds/51
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