Theoretical Overview of Quantum Machine Learning Applications in Enhancing Demand Forecasting for Supply Chain Management

Author:

Mandal Kamaluddin1,Chatterjee Tathagata2ORCID,Sarkar Abhijit3,Ghosh Joyita1,Gupta Subir1ORCID

Affiliation:

1. Haldia Institute of Technology, India

2. Narula Institute of Technology, India

3. Global Institute of Management and Technology, India

Abstract

This chapter explores the potential of quantum machine learning (QML) to enhance demand forecasting in supply chain management. With the increasing complexity and data volumes in modern supply chains, traditional forecasting methods often fall short. QML leverages the principles of quantum mechanics, such as superposition and entanglement, to process data at unprecedented speeds and complexity. The authors discuss theoretical frameworks, focusing on quantum algorithms like quantum support vector machines and quantum neural networks, which can handle high-dimensional data more efficiently than classical counterparts. The chapter evaluates the feasibility, challenges, and prospects of implementing QML in real-world supply chain scenarios, suggesting that QML could significantly improve forecasting accuracy and operational efficiency, ultimately transforming supply chain management practices.

Publisher

IGI Global

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