More Web Proxy on the site http://driver.im/

research-article

A simulation-based approach to capturing autocorrelated demand parameter uncertainty in inventory management

Authors:

Sridhar TayurAuthors Info & Claims

WSC '12: Proceedings of the Winter Simulation Conference

Article No.: 283, Pages 1 - 12

Published: 09 December 2012 Publication History

Abstract

We consider a repeated newsvendor setting where the parameters of the demand distribution are unknown, and we study the problem of setting inventory targets using only a limited amount of historical demand data. We assume that the demand process is autocorrelated and represented by an Autoregressive-To-Anything time series. We represent the marginal demand distribution with the highly flexible Johnson translation system that captures a wide variety of distributional shapes. Using a simulation-based sampling algorithm, we quantify the expected cost due to parameter uncertainty as a function of the length of the historical demand data, the critical fractile, the parameters of the marginal demand distribution, and the autocorrelation of the demand process. We determine the improved inventory-target estimate accounting for this parameter uncertainty via sample-path optimization.

References

[1]

Akcay, A., B. Biller, and S. Tayur. 2011a. "Improved inventory targets in the presence of limited historical demand data". Manufacturing and Service Operations Management 13 (3): 297--309.

Digital Library

[2]

Akcay, A., B. Biller, and S. Tayur. 2011b. "Inventory-target estimation in the presence of finite autocorrelated demand data". Tepper Working Paper 2011-E3. Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA.

[3]

Barton, R. R., B. L. Nelson, and W. Xie. 2010, December. "A framework for input uncertainty analysis". In Proceedings of the 2010 Winter Simulation Conference, edited by B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, 1189--1198. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

Digital Library

[4]

Biller, B., and C. G. Corlu. 2011. "Accounting for parameter uncertainty in large-scale stochastic simulations with correlated inputs". Operations Research 59 (3): 661--673.

Digital Library

[5]

Cario, M. C., and B. L. Nelson. 1996. "Autoregressive to anything: Time-series input processes for simulation". Operations Research Letters 19 (2): 51--58.

Digital Library

[6]

Chen, H., and B. W. Schmeiser. 2001. "Stochastic root finding via retrospective approximation". IIE Transactions 33 (3): 259--275.

[7]

Chick, S. E. 2001. "Input distribution selection for simulation experiments: Accounting for input uncertainty". Operations Research 49 (5): 744--758.

Digital Library

[8]

Hayes, R. H. 1969. "Statistical estimation problems in inventory control". Management Science 15 (11): 686--701.

Digital Library

[9]

Joe, H. 1997. Multivariate Models and Dependence Concepts. London: Chapman & Hall.

[10]

Johnson, M. E. 1987. Multivariate Statistical Simulation. New York: John Wiley & Sons.

Digital Library

[11]

Johnson, N. L. 1949. "Systems of frequency curves generated by methods of translation". Biometrika 36 (1/2): 149--176.

[12]

Kuhl, M. E., J. S. Ivy, E. K. Lada, N. M. Steiger, M. A. F. Wagner, and J. R. Wilson. 2010. "Univariate input models for stochastic simulation". Journal of Simulation 4 (2): 81--97.

[13]

Lee, H. L., K. C. So, and C. S. Tang. 2000. "The value of information sharing in a two-level supply chain". Management Science 46 (5): 626--643.

Digital Library

[14]

Lewis, P. A. W., E. McKenzie, and D. K. Hugus. 1989. "Gamma processes". Stochastic Models 5 (1): 1--30.

[15]

Liyanage, L. H., and J. G. Shanthikumar. 2005. "A practical inventory policy using operational statistics". Operations Research Letters 33 (4): 341--348.

Digital Library

[16]

Luong, H. T. 2007. "Measure of bullwhip effect in supply chains with autoregressive demand process". European Journal of Operational Research 180 (1): 1086--1097.

[17]

Mallows, C. L. 1967. "Linear processes are nearly Gaussian". Journal of Applied Probability 4 (2): 313--329.

[18]

Ng, S. H., and S. E. Chick. 2006. "Reducing parameter uncertainty for stochastic systems". ACM Transactions on Modeling and Computer Simulation 16 (1): 26--51.

Digital Library

[19]

Pasupathy, R. 2010. "On choosing parameters in retrospective-approximation algorithms for stochastic root finding and simulation optimization". Operations Research 58 (4): 889--901.

Digital Library

[20]

Pasupathy, R., and S. Kim. 2011. "The stochastic root-finding problem: Overview, solutions, and open questions". ACM Transactions on Modeling and Computer Simulation 21 (3): 1--23.

Digital Library

[21]

Robert, C. P., and G. Casella. 1999. Monte Carlo Statistical Methods. New York: Springer-Verlag. Shapiro, A. 2004. "Monte Carlo sampling methods". In Handbooks in Operations Research and Management Science, Stochastic Programming, edited by A. Ruszczynski and A. Shapiro, 353--426. Amsterdam: North-Holland.

[22]

Sklar, A. 1959. "Fonctions de répartition à n dimensions et leurs marges". Publications de l'Institut de Statistique de L'Université de Paris 8 (1): 229--231.

[23]

Wei, W. W. S. 1990. Time Series Analysis: Univariate and Multivariate Methods. New York: Addison Wesley.

[24]

Zouaoui, F., and J. R. Wilson. 2003. "Accounting for parameter uncertainty in simulation input modeling". IIE Transactions 35 (9): 781--792.

[25]

Zouaoui, F., and J. R. Wilson. 2004. "Accounting for input-model and input-parameter uncertainties in simulation". IIE Transactions 36 (11): 1135--1151.

Cited By

Garcia-Herreros PGrossmann ISharda BAgarwal AWassick JBuckley SMiller J(2014)Empirical study of the behavior of capacitated production-inventory systemsProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2694128(2251-2260)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2694128

Index Terms

A simulation-based approach to capturing autocorrelated demand parameter uncertainty in inventory management
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
  2. Modeling and simulation
    1. Simulation evaluation
    2. Simulation types and techniques
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Regression analysis

Recommendations

Demand fulfillment probability under parameter uncertainty
WSC '16: Proceedings of the 2016 Winter Simulation Conference

We study a multi-item inventory system with normally distributed demands in the presence of demand parameter uncertainty - the uncertainty that stems from the estimation of the unknown demand parameters necessitated by limited amounts of historical ...
A simulation-based approach to statistical inventory management
WSC '12: Proceedings of the Winter Simulation Conference

Most of the literature on inventory management assumes that the demand distribution and the values of its parameters are known with certainty. In this research we consider the practical situation where this is not the case and only a limited amount of ...
Inventory management with log-normal demand per unit time

This paper examines optimal policies in a continuous review inventory management system when demand in each time period follows a log-normal distribution. In this scenario, the distribution for demand during the entire lead time period has no known ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WSC '12: Proceedings of the Winter Simulation Conference

December 2012

4271 pages

General Chair:
Oliver Rose
Universität der Bundeswehr München, Germany
,
Program Chair:
Adelinde M. Uhrmacher
University of Rostock, Germany

Sponsors

IIE: Institute of Industrial Engineers
INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
SCS: Shanghai Computer Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

Winter Simulation Conference

Publication History

Published: 09 December 2012

Check for updates

Qualifiers

Research-article

Conference

WSC '12

Sponsor:

IIE
INFORMS-SIM
SCS
SIGSIM

WSC '12: Winter Simulation Conference

December 9 - 12, 2012

Berlin, Germany

Acceptance Rates

WSC '12 Paper Acceptance Rate 189 of 384 submissions, 49%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
54
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Garcia-Herreros PGrossmann ISharda BAgarwal AWassick JBuckley SMiller J(2014)Empirical study of the behavior of capacitated production-inventory systemsProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2694128(2251-2260)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2694128

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents