MRO Inventory Demand Forecast Using Support Vector Machine – A Case Study-Reference-Cited by-同舟云学术

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MRO Inventory Demand Forecast Using Support Vector Machine – A Case Study

Published:2022 Issue: Volume: Page:221-233
ISSN:2194-1009
Container-title:Springer Proceedings in Mathematics & Statistics
language:
Short-container-title:

Author:

de Paula Vidal Guilherme Henrique^ORCID,Caiado Rodrigo Goyannes Gusmão^ORCID,Scavarda Luiz Felipe^ORCID,Santos Renan Silva^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-14763-0_18

Reference43 articles.

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2. Caiado, R.G.G., Scavarda, L.F., Azevedo, B.D., Nascimento, D.L.d.M., Quelhas, O.L.G.: Challenges and benefits of sustainable industry 4.0 for operations and supply chain management—a framework headed toward the 2030 agenda. Sustainability. 14, 1–26 (2022). https://doi.org/10.3390/su14020830

3. Melnyk, S.A., Lummus, R.R., Vokurka, R.J., Burns, L.J., Sandor, J.: Mapping the future of supply chain management: a Delphi study. Int. J. Prod. Res. 47, 4629–4653 (2009). https://doi.org/10.1080/00207540802014700

4. Lancioni, R.A.: New developments in supply chain management for the millennium. Ind. Mark. Manag. 29, 1–6 (2000). https://doi.org/10.1016/S0019-8501(99)00106-6

5. Sustrova, T.: An artificial neural network model for a wholesale company’s order-cycle management. Int. J. Eng. Bus. Manag. 8, 1–6 (2016). https://doi.org/10.5772/63727

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1. Prediction of external corrosion rate in Oil and Gas platforms using ensemble learning;Brazilian Journal of Operations & Production Management;2023-09-22

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