Increasing Supply Chain Effectiveness: Forecasting Models for Order Quantity Prediction

Akilli, Cem (2024) Increasing Supply Chain Effectiveness: Forecasting Models for Order Quantity Prediction. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (4MB) | Preview

Abstract

For the retail and whole sale industries, satisfying customer demands while preserving a competitive edge requires effective supply chain management. This management is greatly dependent on the precise demand forecasting as it foresees future requirement of goods. This study explores different modern and classical time series forecasting models like the Long Short-Term Memory networks networks, Autoregressive Integrated Moving Average in order to better a prediction accuracy. This study was motivated by the necessity in inventory management, production scheduling, and market entry choices of demand forecasting. Mistakes in demand forecasting can cause substantial operational inefficiencies and financial losses. In order to begin answering these questions, this study evaluates how well the cyclical models forecast US regional sales data utilizing historical information. It is an evaluation of all the models sens, spec and acc to help with which forecasting technique we can go for. These results suggest that while for simple, linear models such as ARIMA traditional models may still be effective LSTM could provide greater accuracy in complex and non-linear ones. This summarises the importance of selecting a forecasting model based on various data characteristics and forecasting needs. The study’s findings offer supply chain professionals insightful information that will help them make better decisions, maximize inventory levels, and boost overall operational effectiveness. This research advances our understanding of forecasting model efficiency, which benefits supply chain management both theoretically and practically.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Onwuegbuche, Faithful UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Retail Industry H Social Sciences > HD Industries. Land use. Labor > Business Logistics > Supply Chain Management
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	20 Jun 2025 11:40
Last Modified:	20 Jun 2025 11:40
URI:	https://norma.ncirl.ie/id/eprint/7978

Actions (login required)

View Item