A Probabilistic Estimation of Perfect Order Parameters
| Autor: | Vladislav Lukinskiy, Darya Bazhina, Valery Lukinskiy, Boris Sokolov |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems ISBN: 9783030858735 APMS (1) |
| DOI: | 10.1007/978-3-030-85874-2_47 |
| Popis: | In the digital economy, information systems have a significant impact on supply chain management. However, there is a need for further development of theoretical knowledge and mathematical models, including methods for managing risk in complex supply networks to best serve customer orders. In the supply chain operations reference (SCOR) model, reliability is assessed by calculating perfect order parameters. The component/process reliability is calculated as the product of the weighted averages of the perfect order parameters, and possible combinations of failure features are not taken into account. This paper presents an approach to probabilistic estimation of perfect order parameters based on the general theorem on the repetition of experiments, and proposes to use a binomial distribution to approximate the values obtained. The obtained results make it possible to assess the efficiency of possible measures (increasing the insurance stock, replacing the carrier, etc.) to improve the reliability of perfect order fulfilment. |
| Databáze: | OpenAIRE |
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