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商业订单行为中的标度律分析、建模与仿真

已有 3685 次阅读 2012-10-17 18:51 |个人分类:成果交流|系统分类:论文交流| 幂律, 建模, 人类动力学, 指数, 订单

 
我们认为对人类行为动力学的分析应该从个体、团体(组织)、群体三个方面进行,目前的研究由于数据采集困难等原因,常见于个体和群体,而对团体的研究较少。
我们最近的一篇文章恰好弥补了这样的缺失,以某全球500强企业的采购订单为研究对象,来考察某个组织的行为,看其与个体或者群体行为有何分别,统计量仍然为人类动力学研究中的关键量——时间间隔分布,具体从个体(区分不同客户的订单)和群体(不区分客户整体考虑)两个层面进行研究。
研究发现,当不区分各个供应商时,个体行为标度律表现为较好的幂律分布,指数约为2.0;当将供应商混合考虑时,群体行为表现为幂律和指数混合的分布形式。对后者, 我们建立了一个以产品生命周期驱动的模型,并给出数值解析和仿真,结果显示可以很好的刻画这种幂律与指数混合的分布律。
 

Individual and group dynamics in purchasing activity

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

Physica A, Volume 392, Issue 2, 15 January 2013, Pages 343–349

 

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.

 

Keywords

Human dynamics; Supply chain; Power-law distribution; Mixture distribution; Individual dynamics; Group dynamics

 

  • http://www.sciencedirect.com/science/article/pii/S0378437112007169
  • Corresponding author. E-mail address: phd5816@163.com  (J.-L. Guo).

     


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