A Nearly Optimal Order Policy to Reduce Bullwhip Effect

Abstract

An important supply chain research problem is the bullwhip effect caused by information distortion and variation amplification along a supply chain, which can lead to tremendous inefficiencies, such as excessive inventory investment and lost revenues. Motivated by engineering process control methods, this paper proposes a class of order-up-to policies and develops a nearly optimal policy to reduce the bullwhip effect. The proposed policy can significantly reduce the order variance while keeping the expected cost nearly optimal. According to our numerical studies, the order variance of the nearly optimal policy can be reduced by more than 50% while the expected cost is only slightly greater than that of the optimal policy derived in Lee et al. [12].

Key Words:

• Engineering process control
• information distortion
• order-up-to policy

Additional information

Notes on contributors

H. Liu

Hancong Liu He is a Research Fellow of Chemical and Materials Engineering at University of Alberta. He received his Ph.D. in Industrial Engineering and Engineering Management from Hong Kong University of Science and Technology. He got both BSc and MSc in Mathematics from Northeastern University, China. He was a visiting lecturer in Wisconsin University at Milwaukee. He is a member of CORS and CSChE. His research interests include experimental design, process control and monitoring, supply chain management, and outlier detection.

K.L. Tsui

Kwok-Leung Tsui He is Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology. He has a B.Sc. in Chemistry and an M.Ph. in Mathematics both from the Chinese University of Hong Kong, and a Ph.D. in Statistics from the University of Wisconsin at Madison. He had worked in the Quality Assurance Center of AT&T Bell Laboratories before joining Georgia Tech in 1990. Dr. Tsui was a recipient of the 1992 NSF Young Investigator Award. He was the (elected) President and Vice President of the American Statistical Association Atlanta Chapter in 1992–1993. Dr. Tsui was the Chair of the INFORMS Section in Quality, Statistics, and Reliability (QSR) in 2000 and the program chair of the QSR cluster sessions in 1999 and 2000. He is currently the Chair of the INFORMS Section in Data Mining (DM) and was the program chair of the DM cluster sessions in 2002 and 2003. Dr. Tsui is also a US representative in the ISO Technical Committee on Statistical Methods (TC 69). Dr. Tsui researches, teaches, and consults on statistical methods for quality and supply chain management. His current research interest includes data mining, supply chain management, robust design and Taguchi method, experimental design, statistical process control, and design and modeling of computer experiments.

F. Tsung

Fugee Tsung

He is an Associate Professor of Industrial Engineering and Engineering Management at HKUST. He received his Ph.D. in Industrial and Operations Engineering from the University of Michigan, Ann Arbor. He worked at Ford Motor and Rockwell International and did his post-doctoral research with Chrysler. He is the Chair of the Quality, Statistics, and Reliability (QSR) Section of INFORMS, and is currently on the editorial boards for the International Journal of Reliability, Quality and Safety Engineering (IJRQSE) and the International Journal of Six Sigma and Competitive Advantage (IJSSCA). He is the recipient of the Best Paper Award for the IIE Transactions focus issues on Quality and Reliability in 2003. His research interests include quality engineering and management, process control and monitoring, and Six Sigma implementation.