Demand Prediction, Predictive Shipping, and Product Allocation for Large-Scale E-Commerce

Posted: 27 Nov 2018

See all articles by Xiaocheng Li

Xiaocheng Li

Stanford University, School of Engineering, Management Science & Engineering, Students

Yufeng Zheng

Shanghai Jiao Tong University, Department of Industrial Engineering and Management

Zhenpeng Zhou

Stanford University, School of Humanities & Sciences, Department of Chemistry

Zeyu Zheng

University of California, Berkeley - Department of Industrial Engineering and Operations Research

Date Written: November 1, 2018

Abstract

In this paper, we first extensively analyze the transactional level data made available by Alibaba and its logistics arm Cainiao, including detailed information on transaction orders, inventory and logistics for 7,103 different products and 130 warehouses. Based on the data analysis, we focus on demand prediction, data-driven shipping mechanism, and product allocation across warehouses: (i) We develop a multiple-product demand prediction system that identifies unique features presented in the data, which improves prediction accuracy significantly compared to using standard machine learning models. A new clustering-based regularization method is developed with the aid of representation learning to augment data and prevent overfitting. (ii) We propose and analyze in theory a novel shipping mechanism - Predictive Shipping, which utilizes demand prediction to arrange shipping before orders are placed. The observed vast vacancy in regional warehouses are utilized to support this mechanism. (iii) We formulate a large-scale product allocation problem across warehouses and study the cost sensitivity of a change in allocation distributions. Numerical experiments with both real data and synthetic data are conducted to demonstrate our findings.

Suggested Citation

Li, Xiaocheng and Zheng, Yufeng and Zhou, Zhenpeng and Zheng, Zeyu, Demand Prediction, Predictive Shipping, and Product Allocation for Large-Scale E-Commerce (November 1, 2018). Available at SSRN: https://ssrn.com/abstract=3277125

Xiaocheng Li

Stanford University, School of Engineering, Management Science & Engineering, Students ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Yufeng Zheng

Shanghai Jiao Tong University, Department of Industrial Engineering and Management ( email )

Zhenpeng Zhou

Stanford University, School of Humanities & Sciences, Department of Chemistry ( email )

Stanford, CA 94305
United States

Zeyu Zheng (Contact Author)

University of California, Berkeley - Department of Industrial Engineering and Operations Research ( email )

4141 Etcheverry Hall
Berkeley, CA 94720-1777
United States

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