Supply Chain Data Analytics for Predicting Delivery Risks Using Machine Learning
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-1019-9_16
Reference13 articles.
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3. Kusrini E et al (2020) Risk mitigation on product distribution and delay delivery: a case study in an Indonesian manufacturing company. In: IOP conference series: materials science and engineering, vol 722, pp 012015. https://doi.org/10.1088/1757-899x/722/1/012015
4. Wang G, Gunasekaran A, Ngai EWT et al (2016) Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int J Prod Econ 176:98–110. https://doi.org/10.1016/j.ijpe.2016.03.014
5. Baryannis G, Validi S, Dani S et al (2018) Supply chain risk management and artificial intelligence: state of the art and future research directions. Int J Prod Res 57:2179–2202. https://doi.org/10.1080/00207543.2018.1530476
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2. Enhancing Supply Chain Resilience: RIME-Clustering and Ensemble Deep Learning Strategies for Late Delivery Risk Prediction;Logforum;2024-03-30
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