Enhancing forecast accuracy for lumpy demand using hybrid machine learning model

Enhancing forecast accuracy for lumpy demand using hybrid machine learning model

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...

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Bibliographic Details
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

Internet

https://hdl.handle.net/10356/182354

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