Name
Title | ||
|---|---|---|
A simulation-based support tool for data-driven decision making: Operational testing for dependence modeling |
Abstract | ||
|---|---|---|
Dependencies occur naturally between input processes of many manufacturing and service applications. When the dependence parameters are known with certainty, the failure to factor the dependencies into decisions is well known to waste significant resources in system management. Our focus is on the case of unknown dependence parameters that must be estimated from finite amounts of historical input data. In this case, the estimates of the unknown dependence parameters are random variables and simulations are designed to account for the dependence parameter uncertainty to better support the data-driven decision making. The premise of our paper is that there are certain cases in which the assumption of an independent input process to minimize the expected cost of input parameter uncertainty becomes preferable to accounting for the dependence parameter uncertainty in the simulation. Therefore, a fundamental question to answer before capturing the dependence parameter uncertainty in a stochastic system simulation is whether there is sufficient statistical evidence to represent the dependence, despite the uncertainty around its estimate, in the presence of limited data. We seek an answer for this question within a data-driven inventory-management context by considering an intermittent demand process with correlated demand size and number of interdemand periods. We propose two new finite-sample hypothesis tests to serve as the decision support tools determining when to ignore the correlation and when to account for the correlation together with the uncertainty around its estimate. We show that a statistical test accounting for the expected cost of correlation parameter uncertainty tends to reject the independence assumption less frequently than a statistical test which only considers the sampling distribution of the correlation-parameter estimator. The use of these tests is illustrated with examples and insights are provided into operational testing for dependence modeling. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WSC.2014.7019950 | Winter Simulation Conference |
Keywords | Field | DocType |
statistical distributions,finite-sample hypothesis tests,input process,dependence parameters,decision support tools,inventory management,data-driven inventory-management,statistical testing,dependence parameter uncertainty,simulation-based support tool,interdemand period number,service applications,random variables,decision making,intermittent demand process,statistical test,operational testing,system management,random simulations,data-driven decision making,independent input process,correlation-parameter estimator,correlation parameter uncertainty,expected cost minimization,dependence modeling,unknown dependence parameter estimation,input parameter uncertainty,manufacturing applications,sampling methods,historical input data,stochastic system simulation,correlated demand size,sampling distribution | Econometrics,Sampling distribution,Computer science,Decision support system,Sensitivity analysis,Uncertainty analysis,Statistical assumption,Statistical hypothesis testing,Estimator,Operational acceptance testing | Conference |
ISSN | ISBN | Citations |
0891-7736 | 978-1-4673-9741-4 | 2 |
PageRank | References | Authors |
0.39 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bahar Biller | 1 | 452 | 72.34 |
| Alp Akcay | 2 | 26 | 6.39 |
| Canan G. Corlu | 3 | 30 | 6.12 |
| Sridhar Tayur | 4 | 475 | 52.25 |