Enhancing forecast accuracy for lumpy demand using hybrid machine learning model

• Main Author: Le, Thi Chau Giang
• Other Authors: Rajesh Piplani
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2025
• Subjects: Engineering; Lumpy demand forecasting; Hybrid model; Machine learning; XGBoost; Croston's model; Supply chain engineering; Sporadic demand; Forecast accuracy; Forecasting algorithm
• Online Access: https://hdl.handle.net/10356/182354
• Institution: Nanyang Technological University

Demand forecasting is a critical aspect of supply chain management, underpinning decision-making processes that span from strategic operations planning to daily workload management. Given its importance, substantial research efforts have been devoted to developing and optimizing forecasting tools to...