Multivariate statistical simulation
Multivariate statistical simulation
The Value of Information Sharing in a Two-Level Supply Chain
Management Science
Reducing parameter uncertainty for stochastic systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The stochastic root-finding problem: Overview, solutions, and open questions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Improved Inventory Targets in the Presence of Limited Historical Demand Data
Manufacturing & Service Operations Management
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
A practical inventory control policy using operational statistics
Operations Research Letters
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
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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.