Abstract
In the age of big data, it is a challenging task for ERP systems to maintain data timeliness over changing data sources. Purchase data is an important dynamic data and its timeliness directly affects the accuracy of inventory data and purchase plans. According to the characteristics of Markov decision process, we design a dynamic programming algorithm to obtain the optimal purchase data updating policy. Its effectiveness is tested by comparing with traditional fixed interval policies with real-life enterprise data. The comparison results show the proposed updating policy outperforms the fixed interval policies and can be applied to enterprises when updating ERP systems.
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Acknowledgments
This work was supported by the National Natural Science Foundation under Grant No. 71428003,71471144, 71071126.
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Zong, W., Wu, F., Jiang, Z., Qu, Y. (2015). A Study on Optimal Policy for Purchase Data Updating in ERP Systems. In: Zhang, C., et al. Data Science. ICDS 2015. Lecture Notes in Computer Science(), vol 9208. Springer, Cham. https://doi.org/10.1007/978-3-319-24474-7_3
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DOI: https://doi.org/10.1007/978-3-319-24474-7_3
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