Dear faculty members and fellow students,

 

You are cordially invited to attend my thesis defense.

 

Title: Advanced Data-Driven Methodologies for Enhanced Demand Forecasting in Supply Chain Management

 

Committee:

Dr. Benoit Montreuil (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

Dr. David Goldsman, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

Dr. Yao Xie, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

Dr. Shihao Yang, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

Dr. Bruno Agard, Department of Mathematical and Industrial Engineering, Institut National Polytechnique de Grenoble

 

Date and Time: Friday, August 9, 2024, 9:30 AM (EST)

Meeting Link and Location: Microsoft Teams. Link Join the meeting now

Meeting ID: 252 475 031 352

Passcode: wtWAUo

 

 

Abstract

In the complex and dynamic realm of supply chain management, the accuracy of demand forecasting is paramount for achieving operational efficiency, reducing costs, and enhancing customer satisfaction. This dissertation introduces a series of innovative, data-driven methodologies that significantly enhance the precision of demand forecasts across various facets of the supply chain. By integrating cutting-edge analytics and forecasting techniques, this research addresses critical challenges encountered by logistics and retail sectors, especially in light of recent global disruptions such as the COVID-19 pandemic.

 

Chapter 1 presents a novel dynamic forecasting method specifically designed for predicting parcel arrivals at logistics hubs. As e-commerce continues its rapid expansion, logistics hubs are under increasing pressure to manage incoming parcel volumes efficiently. This chapter elucidates the development and implementation of an ensemble forecasting model that leverages both historical data and real-time parcel tracking information to predict short-term parcel arrival rates. This innovative approach enables logistics operators to optimize resource allocation, enhance throughput, and mitigate operational uncertainties.

 

Chapter 2, expands the scope of retail demand forecasting by incorporating a spatial-temporal analytical framework. Recognizing that both geographic and temporal factors significantly influence demand, this chapter unveils a clustering-based ensemble model for forecasting. It integrates augmented fuzzy c-means clustering with advanced time series and multivariate forecasting techniques to generate highly accurate demand predictions that consider both spatial correlations and temporal patterns. This comprehensive approach not only enhances forecast accuracy but also furnishes actionable insights for optimizing supply chain operations within the retail sector.

 

Chapter 3, explores the significant challenge of estimating true consumer demand during out-of-stock events—a frequent and impactful issue in retail operations. Conventional demand forecasting methods often fail to capture the true essence of consumer desire, particularly when products are unavailable. This chapter introduces a sophisticated model that distinguishes between original demand, substitution demand, and deferral demand, thus providing a more accurate reflection of consumer intent and behavior. This detailed perspective on demand dynamics allows retailers to fine-tune inventory and marketing strategies, thereby improving customer satisfaction and minimizing financial waste.

 

Best regards,

Xinyue Pan