Robust and scalable procurement forecast in logistics
Автор: Ulyanov Sergey V., Filipyev Andrey V.
Журнал: Сетевое научное издание «Системный анализ в науке и образовании» @journal-sanse
Статья в выпуске: 3, 2022 года.
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This article aims to reveal that machine learning algorithms can be applied in a variety of commercial companies in order to improve developing intelligent systems. The major task which would be discussed in the developing robust forecast model and lauching it on scalable data platform. Besides algorithms and software, the problems of changing processes would be considered and possible solutions suggested.
Machine learning, intelligence logistic systems, scalable data platform architecure, decision making systems, artificial intelligence
Короткий адрес: https://sciup.org/14126378
IDR: 14126378
Список литературы Robust and scalable procurement forecast in logistics
- Wisner J. D., Tan K. C, Leong G. K. Principles of supply chain management: a balanced approach. Mason: Thomson South-Western, 2011.
- Kenneth G. An ARIMA Supply Chain Model. Management Science. 2005. Vol.51, No 2. Pp.305-310. https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.1040.0308.
- Forecasting of demand using ARIMA model. International Journal of Engineering Business Manage-ment. Vol. 10. Pp. 1–9. https://journals.sagepub.com/doi/10.1177/1847979018808673.
- Box George E. P, Jenkins G. Time series analysis, forecasting and control. San Francisco: Holden-Day, 1970.
- Hanke J. E., Reitsch A. G. Business forecasting. 5th ed. Englewood Cliffs. 1995.
- Taylor S. J., Letham B. Forecasting at scale. 2017. PeerJ Preprints 5:e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2.
- Hastie T., Tibshirani R. Generalized additive models: some applications. Journal of the American Statis-tical Association. 1987. Vol. 82, No 398. Pp. 371–386.
- Gardner E. S. Exponential smoothing: the state of the art. Journal of Forecasting. 1985. Vol. 4, Pp. 1–28.
- Armbrust M., et al. Delta lake: high-performance ACID table storage over cloud object stores. Proceed-ings of the VLDB Endowment Vol. 13, No 12. 2020. Pp. 3411-3424. DOI: 10.14778/3415478.3415560
- Priebe T., Neumaier S., Markus S. Finding Your Way Through the Jungle of Big Data Architectures. 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. DOI: 10.1109/BigData52589.2021.9671862.
- Khan Mohammad Arafat Ali, et al. Nowcasting the Financial Time Series with Streaming Data Analyt-ics under Apache Spark.2022. arXiv preprint arXiv:2202.11820.
- Jit Kee Chin, Mikael Hagstroem, Ari Libarikian, and Khaled Rifai. Advanced analytics: Nine insights from the C-suite. McKinsey & Company. 2018. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ advanced-analytics-nine-insights-from-the-c-suite.
