The Research on Distribution of Lead-Time Demand on the Basis of Multivariate Higher-Order Markov Chain

Authors

  • Jiahui Xu SILC Business School, Shanghai University, China
  • Pingyu Liu SILC Business School, Shanghai University, China
  • Xuemin Xu SILC Business School, Shanghai University, China
  • Zhenni Huang SILC Business School, Shanghai University, China
  • Wenshuang Zhao SILC Business School, Shanghai University, China
  • Kwok Leung Tam School of Mathematics and Statistics, UNSW, Australia
  • Aiping Jiang SILC Business School, Shanghai University, China

DOI:

https://doi.org/10.5755/j01.ee.32.4.27571

Keywords:

Forecasting, Lead-time demand, Inventory management, Supply chain management, Multivariate higher-order Markov chain

Abstract

Inventory management is an important part of supply chain management: inventory shortages could result in reduced delivery speeds and response speeds while excess inventory could lead to increased inventory and operating costs. Therefore, finding ways to efficiently control inventory has become an issue companies are most concerned about. Choosing a proper inventory management method based on the lead-time demand distribution fitted from historical data has become the key criteria to solve this issue. However, it is difficult to determine the lead-time distribution based on the limited amount of historical data directly. Thus, the method this report introduces uses a multivariate higher-order Markov chain to reconstruct historical data in order to expand the amount of data used to fit the lead-time distribution of demand, which is significant for inventory management.

Author Biographies

Jiahui Xu, SILC Business School, Shanghai University, China

Jiahui Xu is a native of Taizhou, Zhejiang. Now he is an undergraduate in SHU‐UTS SILC Business School, Shanghai. Shanghai. His main research interests are demand forecasting and inventory management. ORCID: 0000-0001-6772-3957

Pingyu Liu, SILC Business School, Shanghai University, China

Pingyu Liu is a native of Shanghai. Now she is an undergraduate in SHU‐UTS SILC Business School, Shanghai University. Her main research interests relate to demand forecasting and inventory management

Xuemin Xu, SILC Business School, Shanghai University, China

Xuemin Xu is a native of Xingtai, Hebei. She graduated from Shanghai University. Now she is a lecturer in the SHU-UTS SILC Business School, Shanghai University, PRC. She was a visiting scholar at Carlton University in the United States, Sydney University of Technology in Australia and Angles College in Australia. Her main research interests are related to demand forecasting and inventory management.

Zhenni Huang, SILC Business School, Shanghai University, China

Zhenni Huang is a native of Shanghai. She is a postgraduate in SHU‐UTS SILC Business School (Master’s Degree in Management Science and Engineering), Shanghai University. Her main research interests are demand forecasting and inventory management

Wenshuang Zhao, SILC Business School, Shanghai University, China

Wenshuang Zhao is a native of Shandong. He is a postgraduate in SHU‐UTS SILC Business School (Bachelor’s Degree in International Economy and Trade), Shanghai University. His main research interests is optimization.

Kwok Leung Tam, School of Mathematics and Statistics, UNSW, Australia

Kwok Leung Tam is a native of Hongkong; Dr. He graduated from the Department of Statistics, Columbia University. He is a teacher in the School of Mathematics and Statistics, UNSW, Sydney NSW, Australia. His main research interests are related to demand forecasting.

Aiping Jiang, SILC Business School, Shanghai University, China

Aiping Jiang (corresponding author) is a native of Longkou, Shandong, a professor. She graduated from the Department of Applied Mathematics, Tongji University. She is an associate professor in the SHU‐UTS SILC Business School, Shanghai University. She is also a distinguished research fellow at the Demand Chain Management Laboratory of Modern Logistics Research Center, Shanghai University. Her research interests include demand forecasting and inventory management etc.. She takes charge of the national natural science fund project (71871133). ORCID iD: 0000-0002-4822-9627

Additional Files

Published

2021-10-28

Issue

Section

Articles