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Individual and group dynamics in purchasing activity

Author

Listed:
  • Gao, Lei
  • Guo, Jin-Li
  • Fan, Chao
  • Liu, Xue-Jiao

Abstract

As a major part of the daily operation in an enterprise, purchasing frequency is in constant change. Recent approaches on the human dynamics can provide some new insights into the economic behavior of companies in the supply chain. This paper captures the attributes of creation times of purchase orders to an individual vendor, as well as to all vendors, and further investigates whether they have some kind of dynamics by applying logarithmic binning to the construction of distribution plots. It’s found that the former displays a power-law distribution with approximate exponent 2.0, while the latter is fitted by a mixture distribution with both power-law and exponential characteristics. Obviously, two distinctive characteristics are presented for the interval time distribution from the perspective of individual dynamics and group dynamics. Actually, this mixing feature can be attributed to the fitting deviations as they are negligible for individual dynamics, but those of different vendors are cumulated and then lead to an exponential factor for group dynamics. To better describe the mechanism generating the heterogeneity of the purchase order assignment process from the objective company to all its vendors, a model driven by product life cycle is introduced, and then the analytical distribution and the simulation result are obtained, which are in good agreement with the empirical data.

Suggested Citation

  • Gao, Lei & Guo, Jin-Li & Fan, Chao & Liu, Xue-Jiao, 2013. "Individual and group dynamics in purchasing activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(2), pages 343-349.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:2:p:343-349
    DOI: 10.1016/j.physa.2012.07.047
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    References listed on IDEAS

    as
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    Cited by:

    1. Yong, Nuo & Ni, Shunjiang & Shen, Shifei & Ji, Xuewei, 2016. "An understanding of human dynamics in urban subway traffic from the Maximum Entropy Principle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 222-227.
    2. Gao, Cai & Yan, Chao & Zhang, Zili & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "An amoeboid algorithm for solving linear transportation problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 179-186.

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