Utilizing Machine Learning to Enhance Optimal Inventory Management - Case: Aviation Industry Spart Parts

Aug 4, 2026

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This thesis tackles inventory management challenges in global aerospace platforms, with a case focus on GA Telesis, an aircraft maintenance and component services provider. Item classification strategies are explored using historical usage, cost, and engine models, combining ABC/XYZ analyses with machine learning for demand forecasting. An action research methodology is used with ARIMA, SVM, linear regression, and gradient boosting, evaluated via ME, RMSE, MAE, and MAPE on IFS procurement data, supported by interviews with key personnel. Results indicate ARIMA delivers the strongest forecasting performance for the aviation sector.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages59

Reading Time2 h 29 min

AccessFree to download

1 INTRODUCTION

1.1 Background

1.2 Objectives and Research Questions

1.3 Structure of the thesis

2 RELATED WORK

2.1 Inventory optimization in the aviation industry

2.2 Demand forecasting

3 METHODOLOGY

3.1 Pareto principle

3.2 Inventory management techniques

3.3 Evaluation criteria

4 EXPERIMENTS

4.1 Technology stack and tools

4.2 Case Description

4.3 Proposed solution

4.4 Master dataset creation

4.5 Description of experiments

5 DISCUSSION AND CONCLUSION

5.1 Research Question Responses

UTILIZING MACHINE LEARNING TO ENHANCE OPTIMAL INVENTORY MANAGEMENT-CASE: AVIATION INDUSTRY SPART PARTS Lappeenranta-Lahti University of Technology LUT Master’s Program in Computational Engineering, Master’s Thesis 2024 Nghia Nguyen Examiners: Professor Tapio Helin Assistant Professor Jyrki Savolainen ABSTRACT Lappeenranta-Lahti University of Technology LUT School of Engineering Science Computational Engineering Nghia Nguyen Utilizing Machine Learning to Enhance Optimal Inventory Management - Case: Aviation Industry Spart Parts Master’s thesis 2024 59 pages, 19 figures, 5 tables, 6 appendices Examiners: Professor Tapio Helin and Assistant Professor Jyrki Savolainen Keywords: Inventory management, ERP system, ABC analysis, XYZ analysis, material management, demand forecasting, time series forecasting This thesis addresses inventory management challenges faced by global aerospace platforms. The special focus is given to a case company, GA Telesis which is a full-service aircraft maintenance and component services provider. Strategies for optimizing inventory levels are explored, including classifying items based on historical usage, cost and assembled engine models to identify critical items and utilizing ABC/XYZ analyses alongside machine learning for inventory management. The quantitative study methodology employs an action research approach with machine learning models of Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), Linear Regression (LR), Gradient Boosting (GB) and statistical techniques, including Mean Error (ME), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) to predict future demand based on numerical data sourced from the IFS system’s procurement records of the case company. Additionally, interviews with key personnel provide insights into real-world challenges faced by the company, allowing the study to propose solutions specifically tailored to address these issues. The key finding of this study is that the ARIMA model demonstrated superior performance in demand forecasting, corroborating with existing literature and validating its effectiveness in the aviation sector. ACKNOWLEDGEMENTS The journey of crafting this thesis has been an invaluable opportunity to deepen my expertise in the logistics domain, particularly within the aviation market. I feel excitement and relief as my time at LUT University draws closer. These past two years have been challenging yet incredibly rewarding, filled with cherished memories and invaluable experiences. The friendships made during these years will certainly last. I would like to take this opportunity to express my gratitude to those who helped me conduct research and write this thesis. Firstly, I would like to thank my supervisor, Jyrki Savolainen, for guiding me through this entire process and for his valuable feedback, patience, and support throughout this research and the writing of this thesis. His insights and words of encouragement have often inspired me and renewed my hopes for completing my graduate education. Secondly, I would like to thank the personnel in the case company, especially Galina Alkvist and Juuso Aho, for giving me this opportunity to complete my master’s thesis for the case company and for your constant support and encouragement. Thanks also to Sami Kouhia, Sam Rista, Kalle Arvila, Oscar Moctezuma, Tommi Riuttala, Iina Rinne and Alex Nirvinen, who participated in the study; your contributions have been valuable to this thesis. Last but not least, I want to express my unique gratitude tomy family, friends and Tran Ngoc Dan Thanh, whose unwavering belief in me has been a constant source of support. Lappeenranta, March 12, 2024 Nghia Nguyen 4 LIST OF ABBREVIATIONS ANN Artificial Neural Network ARIMA Autoregressive Integrated Moving Average BOM Bill Of Material DLNN Deep Learning Neural Networks EOQ Economic Order Quantity

FAQs

What inventory management problem does the thesis address, and which company is used as the case?

The study addresses inventory management challenges in global aerospace platforms, focusing on GA Telesis, an aircraft maintenance and component services provider.

How are demand forecasting models evaluated in this research?

Models are evaluated using metrics including Mean Error (ME), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) on procurement data from the IFS system.

Which model shows the best performance for demand forecasting in the aviation sector?

The ARIMA model demonstrates superior performance in forecasting demand, aligning with existing literature and validating its effectiveness for the aviation case.

Aug 4, 2026

Upload by Oliver