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Quantile forecasting and data-driven inventory management under nonstationary demand
- Source :
- Operations Research Letters. 47:465-472
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- In this paper, a single-step framework for predicting quantiles of time series is presented. Subsequently, we propose that this technique can be adopted as a data-driven approach to determine stock levels in the environment of newsvendor problem and its multi-period extension. Theoretical and empirical findings suggest that our method is effective at modeling both weakly stationary and some nonstationary time series. On both simulated and real-world datasets, the proposed approach outperforms existing statistical methods and yields good newsvendor solutions.
- Subjects :
- 021103 operations research
Computer science
Applied Mathematics
0211 other engineering and technologies
02 engineering and technology
Management Science and Operations Research
Newsvendor model
01 natural sciences
Industrial and Manufacturing Engineering
Data-driven
010104 statistics & probability
Inventory management
Econometrics
0101 mathematics
Software
Stock (geology)
Quantile
Subjects
Details
- ISSN :
- 01676377
- Volume :
- 47
- Database :
- OpenAIRE
- Journal :
- Operations Research Letters
- Accession number :
- edsair.doi...........071b6e7a6c05c81200b74c5873c8df9a