[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123921-en":3,"doc-seo-123921-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123921,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning Approaches for Aftermarket Demand Forecasting - Tackling Intermittent Time Series Challenges","This thesis addresses the challenge of accurate demand prediction for aviation aftermarket maintenance and spare parts management, with emphasis on intermittent parts whose demand occurs irregularly. Such intermittency makes forecasting and stock-level decisions difficult, and conventional methods often produce inaccurate results, slowing inventory turnover and increasing warehousing costs. A comprehensive set of techniques is evaluated, spanning statistical models, machine learning, and deep learning. Using real company data, models including exponential smoothing and Croston, SVR, Random Forest, K-nearest neighbour, and deep models such as LSTM, GRU, and CNN are implemented with customized error metrics for intermittent demand forecasting. Results show deep learning models, especially Gated CNN and LSTM, achieve the most accurate forecasts on average, supporting cost reduction and service reliability while offering insights for other intermittent-demand industries.","Machine Learning Approaches for Aftermarket Demand Forecasting: Tackling Intermittent Time  \nSeries Challenges  \nSarvesh Kumar Rajavelloo  \nA Thesis  \nin  \nThe Department  \nof  \nSupply Chain & Business Technology Management  \nPresented in Partial Fulfillment of the Requirements  \nfor the Degree of Master of Supply Chain Management at  \nConcordia University  \nMontréal, Quebec, Canada  \nDecember 2023  \n© Sarvesh Kumar Rajavelloo, 2023  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared,  \nBy: Sarvesh Kumar Rajavelloo  \nEntitled: Machine Learning Approaches for Aftermarket Demand Forecasting: Tackling Intermittent Time Series Challenges  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Supply Chain Management  \ncomplies with the regulations of the University and meets the accepted standards with respect to originality and quality.  \nSigned by the final Examining Committee:  \n  Chair  \nDr. Navneet Vidyarthi  \n  Examiner  \nDr. Navneet Vidyarthi  \n  Examiner  \nDr. Xiaodan Pan  \nSupervisor  \nDr. Chaher Alzaman  \nApproved by:  \nDr. Satyaveer S. Chauhan, Graduate Program Director  \nDr. Anne-Marie Croteau, Dean of Faculty  \nDate:    \nDecember 6th 2023  \nABSTRACT  \nMachine Learning Approaches for Aftermarket Demand Forecasting: Tackling Intermittent Time  \nSeries Challenges  \nSarvesh Kumar Rajavelloo  \nThis thesis addresses the significant challenge of achieving precise demand prediction within the aviation aftermarket maintenance and spare parts management sector, particularly concerning intermittent parts. These components, characterized by irregular demand occurrences, present a formidable challenge due to the difficulty in accurately estimating their demand and setting appropriate stock levels. Historical approaches, relying on conventional demand forecasting techniques, often yielded inaccurate forecasts, resulting in slow inventory turnover and increased warehousing costs. To address this challenge, a broad spectrum of techniques was examined, ranging from traditional statistical models to modern machine learning and deep learning methods falling under the broader domain of artificial intelligence. Deep learning has garnered substantial attention in time series analysis for its exceptional forecasting performance. Real-world data from an aviation company was used to implement various forecasting models, including traditional methods like the exponential smoothing, and Croston, as well as machine learning models like SVR, Random Forest, and K-nearest neighbour. Deep learning techniques, including LSTM, GRU, and CNN, were prominently featured, with customized error metrics tailored to intermittent demand forecasting. The findings highlight that, on average, deep learning models, especially Gated CNN and LSTM, outperform other models and offer highly accurate forecasts for intermittent demand. This study serves as a reference point for choosing the most effective forecasting method to support inventory planning in the aviation aftermarket, reducing costs, and enhancing service reliability. Moreover, its relevance extends to various industries dealing with intermittent demand, offering valuable insights for improved demand forecasting.  \nKeywords: Demand forecasting, Aviation aftermarket parts management, Intermittent time series, Deep learning  \nACKNOWLEDGEMENT  \nI would like to sincerely thank everyone who contributed to the realization of this thesis. My sincere gratitude goes out to Dr. Chaher Alzaman, who served as my thesis supervisor and provided invaluable guidance, support, and encouragement throughout the research process. His counselling and expertise have been crucial in forming this thesis. This academic and personal journey has been profoundly transformative, and it owes its success to the support, direction, and motivation I received from numerous individuals and organizations.  \nI would also like to extend my gratitude to my mother and ","cbCaioMETtPLsh0R","https://ap.wps.com/l/cbCaioMETtPLsh0R","pdf",2091544,1,68,"English","en",105,"# CHAPTER 1: Introduction\n## 1.1 Research problem\n## 1.2 Thesis structure\n# CHAPTER 2: Literature review\n## 2.1 Classification of demand\n## 2.2 Time series forecasting\n## 2.3 Intermittent demand forecasting\n## 2.4 Intermittent demand forecasting with advanced methods","[{\"question\":\"What problem does the thesis focus on in aviation aftermarket demand forecasting?\",\"answer\":\"It focuses on achieving precise demand prediction for aviation aftermarket maintenance and spare parts, particularly intermittent parts with irregular demand occurrences.\"},{\"question\":\"Why are intermittent parts especially difficult to forecast?\",\"answer\":\"Irregular demand makes it hard to estimate future demand accurately and to determine appropriate stock levels.\"},{\"question\":\"Which forecasting approaches and model types are compared in the study?\",\"answer\":\"The study evaluates traditional statistical methods (including exponential smoothing and Croston), machine learning models (such as SVR, Random Forest, and K-nearest neighbour), and deep learning models (including LSTM, GRU, and CNN) with customized error metrics.\"}]","Machine Learning Approaches for Aftermarket Demand Forecasting - 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