Driving inventory system simulations with limited demand data: Insights from the newsvendor problem
| Autor: | Sridhar Tayur, Bahar Biller, Canan G. Corlu |
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| Rok vydání: | 2018 |
| Předmět: |
Estimation
050210 logistics & transportation 021103 operations research Operations research Computer science media_common.quotation_subject 05 social sciences 0211 other engineering and technologies ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Inventory system Newsvendor model ComputingMilieux_GENERAL Modeling and Simulation Service (economics) Service level 0502 economics and business Quality (business) Software media_common |
| Zdroj: | Journal of Simulation. 13:152-162 |
| ISSN: | 1747-7786 1747-7778 |
| DOI: | 10.1080/17477778.2018.1488935 |
| Popis: | Stochastic inventory system simulation is often the tool of choice by industry practitioners who struggle with the evaluation of the quality of proposed inventory targets using service levels. However, driving simulations with unknown input distribution parameters has its own challenges. In this paper, we focus on the newsvendor problem and quantify the amount of demand parameter uncertainty – the uncertainty around the unknown demand distribution parameters which are estimated from the limited historical demand data – in the confidence interval of the mean service level. We use this quantification to understand how the variance of the mean service level, due to the amount of the demand parameter uncertainty in the simulation output process, changes with the choice of Type-1 and Type-2 service-level criteria, the historical data length, the ratio of the unit shortage cost to the unit holding cost, and the distributional shape of the demand’s density function. |
| Databáze: | OpenAIRE |
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