Supervisor
Vikas Tomer
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This thesis explores the impact of machine learning (ML) on supply chain planning, particularly in demand forecasting, supply planning, and inventory optimisation. By analysing literature on supply chain management, data flow, and the intersection of ML and competitive advantage, the author contextualises the research within a globalised market's demands. Case studies, interviews with industry professionals, and raw data collection provide empirical support for evaluating the research objectives and documenting the integration of ML in supply chain processes.
The findings reveal that optimised ML models, particularly those using model stacking (autoregressors, GRUs, and Random Forests), significantly outperform traditional demand forecasting methods, achieving a 70% MAPE improvement over a 45% benchmark. The integration of advanced techniques like XGBoost further optimised supply and inventory planning. The research concludes that leveraging ML not only enhances forecast accuracy but also strengthens supply chain competitiveness through superior planning outputs.
By critically relating empirical data to literature insights, the author demonstrates that ML-driven approaches enhance supply chain management in a Central European wholesale clothing business. This research validates the transformative potential of advanced data analytics for achieving a competitive edge in the supply chain.
Date of Award
2024
Full Publication Date
2024
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Cully, Laurence, "Supply Chain Optimisation with Machine Learning and Neural Networks: Applications to Demand Planning, Supply Planning, and Inventory Planning." (2024). ICT. 69.
https://arc.cct.ie/ict/69