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A Study on Optimal Policy for Purchase Data Updating in ERP Systems

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Data Science (ICDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9208))

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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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Authors and Affiliations

  1. School of Management, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, 710049, People’s Republic of China

    Wei Zong, Feng Wu & Yi Qu

  2. The Key Lab of the Ministry of Education for Process Control & Efficiency Engineering, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, 710049, People’s Republic of China

    Wei Zong, Feng Wu & Yi Qu

  3. Department of Supply Chain and Information Systems, Iowa State University, 2340 Gerdin Business Building, Ames, IA, 50011, USA

    Zhengrui Jiang

Authors
  1. Wei Zong
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  2. Feng Wu
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  3. Zhengrui Jiang
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  4. Yi Qu
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Corresponding author

Correspondence to Feng Wu .

Editor information

Editors and Affiliations

  1. University of Technology, Sydney, Australia

    Chengqi Zhang

  2. Xi'an Jiaotong University, Xi'an Jiaotong, China

    Wei Huang

  3. Chinese Academy of Sciences, Beijing, China

    Yong Shi

  4. University of Illinois Chicago, CHICAGO, Illinois, USA

    Philip S. Yu

  5. Fudan University, Shanghai, China

    Yangyong Zhu

  6. Research Center on Fictitious Econo, Chinese Academy of Sciences, Beijing, China

    Yingjie Tian

  7. University of Technology, Sydney, Australia

    Peng Zhang

  8. Victoria University, Melbourne, Victoria, Australia

    Jing He

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© 2015 Springer International Publishing Switzerland

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24473-0

  • Online ISBN: 978-3-319-24474-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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