Azeem AhmedFollow

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

Ms. Abeera Tariq, Lecturer, Department of Computer Sciences

Keywords

Inventory management, utility projects, forecasting models

Abstract

This project focuses on the development of an Inventory Management System aimed at optimizing material demand forecasting and consumption planning for utility projects. The current system faces challenges such as delays in raising material requisitions and inefficiencies in managing inventory due to extended lead times and limited visibility into consumption trends. Leveraging two years of historical data, this study used machine learning techniques to predict material requirements with improved accuracy. The methodology includes extensive exploratory data analysis, data preprocessing, and the application of forecasting models to identify patterns and trends in material consumption. A user-friendly web interface, built using Django, facilitates seamless interaction with the system, enabling users to input material codes and retrieve real-time insights into inventory status and future requirements. The results demonstrate the effectiveness of the proposed system in reduced lead times, minimized stockouts, and improved inventory efficiency. By integrating data-driven decision-making into inventory management, this project provides a scalable solution to meet the dynamic needs of utility projects. Future work involves incorporating real-time data integration and expanding the model to include cost analysis for a more comprehensive approach to inventory management.

Document Type

Restricted Access

Submission Type

Research Project

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