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- W4313444444 abstract "The monitoring of the delivery performance and accurately predicting any delays in deliveries could be one of the key performance indicators for an enterprise to improve customer retention and ensure service satisfaction. The delivery performance assessment system can help to monitor the current delivery performance of sales orders and stock transport orders. However, for enterprises that are keen to predict the delays in advance for planned deliveries, the delivery risk prediction model can be helpful. This work aims to develop a predictive model for the delivery risk in a supply chain. For the computational study, the work uses historical supply chain data publicly available in a data repository. With the predictive models, enterprises can proactively respond to the situations and avoid delays wherever possible. This paper applies machine learning algorithms to build a predictive model for identifying late delivery risks. Further, a comparative analysis is done with specific performance metrics such as receiver operator characteristics, precision, recall, and F1-score to identify the best predictive model for the delivery risk prediction problem." @default.
- W4313444444 created "2023-01-06" @default.
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- W4313444444 date "2023-01-01" @default.
- W4313444444 modified "2023-10-16" @default.
- W4313444444 title "Development of Predictive Models for Order Delivery Risk in a Supply Chain: A Machine Learning Approach" @default.
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- W4313444444 doi "https://doi.org/10.1007/978-981-19-6945-4_43" @default.
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