长鞭效应危害的主要因素的研究
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摘要
90年代以来,作为实施供应链管理的重要障碍，长鞭效应受到人们越来越多的注意。如何弱化它，成为供应链管理中，十分重要的问题。因此,应该加强长鞭效应相关理论的研究，弱化其危害,为现代管理方式的运用积极创造条件。
关于长鞭效应产生原因的研究比较多,许多学者提出了各自的观点,总体上认为主要是由于需求预测、批量订货、价格波动和短缺博弈这四个因素共同作用造成的。但也存在需要继续研究的地方：首先，关于这四个因素的研究还不是很深入,特别是当这四个因素中的某些因素被抑制后,长鞭效应及其危害的改变,还没有细致的研究。其次，这些文章只用文字对这几个因素进行描述，没有对各种因素进行量化分析。并且，也没有说明这几个因素中哪些因素对造成长鞭效应危害的作用更大，定性的分析不多且缺乏证明，定量的分析则几乎没有。另外，用于讨论长鞭效应的模型也比较简单。本文因此在这些方面进行了一定的研究，希望对相关理论的研究有所贡献，对供应链中的企业克服长鞭效应有所帮助。主要运用的研究工具包括：供应链理论、管理学理论、数据分析、图表分析、程序运算等。
“啤酒对策实验”是由MIT实验室开发出来的一套模拟系统。文章把该系统抽象为函数，并增加了市场当期真实需求水平权重值，真实需要的定货量，短缺博弈程度参数等一系列参数、变量，得到了一个框架。该框架由零售商的到货量、销售量、库存量、定货量，批发商的到货量、发货量、库存量、定货量，生产商的产量、发货量、库存、未发货量等所构成。
文章探讨了评价长鞭效应及其危害的标准，从函数表达式和具体数据（通过程序运算得到结果数据）两个方面证明了长鞭效应和长鞭效应的危害并不同步，而评价长鞭效应不如评价长鞭效应的危害更有意义。于是，提出以订货量方差在内的多个参数综合作为评价长鞭效应危害的标准，侧重对危害及其弱化的分析。
接着，在假定生产商采用传统生产模式——刚性生产的条件下，分别对需求预测因素、缺货博弈因素、批量订货因素这三个因素进行了25%、50%、
75%程度的抑制。并运用这个标准评价了，它们对弱化供应链中的长鞭效应危害的效果。
文章最初提出的函数框架，是在“啤酒对策实验”（80年代）的基础上建立的。该模拟系统是以刚性生产作为生产商的唯一生产方式。但是，随着当今买方市场的到来，企业更强调柔性生产。于是，文章讨论了在供应链中的生产商采用柔性生产方式后，对供应链中的长鞭效应危害的弱化效果。
然后，文章采用讨论刚性生产下的长鞭效应危害的方法，分析了在供应链中采用柔性生产的情况下，分别抑制这三个因素对弱化长鞭效应危害的效果，证明出在一定条件下，批量订货因素才是造成供应链中长鞭效应危害的主要因素等一系列结论。
最后，文章引入了几种不同的分配机制，并且放大了对企业规模的假设条件。根据分配级别和订货数量的不同，把零售商分为九种类型，从定量的角度，着重探讨了分别采用确定型分配机制、比例分配机制、线性分配机制和均匀分配机制，在供应链中的零售商、批发商及生产商环节对长鞭效应危害的影响，并得出了一系列结论。
Since 1990s,Bullwhip Effect, which is a main obstacle in supply chain take more and more, people's attention. It is a very important problem in supply chain management that how to overcome Bullwhip Effect. So we should strengthen the research about Bullwhip Effect and overcome its mischief to create a positive condition for the using of modern management method.
Now there is too much research about the cause of Bullwhip Effect. Many scholars bring forward their own standpoints. In conclusion, it is affected by the following four factors: demand estimate, batch order, short gambling, and price fluctuation. But, these researches about how to restrain the four factors are not deep enough. Especially, when certain factors are restrained in supply chain there are not meticulous research about Bullwhip Effect and its mischief. The other limitation about these articles is only having some describe in words and without necessary quantity analysis. And they didn't explain which factors are more important in causing Bullwhip Effect. There are a little quantity analysis and lack of necessary proof. The quantity analysis is almost nothing. So in this text I have some research in these aspects. I hope I can do some contribution in the related theory and give some help in overcoming Bullwhip Effect. The main tools are used including: supply chain theory, management theory, data analysis, figure analysis, counting by procedures and etc.
This text abstracts the "beer countermeasure experiment" to functions and adds a series of parameters and variables to it including the value about really demand of native market, the really order and the parameter about degree of short-game. It draws into soft product to decide the turnout of the manufacturer and get a model. The model includes the Arrival, Sell, Order, and Stock of the dealer, the Arrival, Consignment, Order, Stock and Non-consignment of the wholesaler, the Turnout, Consignment, Stock and Non-consignment of the manufacturer.
In this text I studied the standard of evaluating Bullwhip Effect and its mischief. I proved that of Bullwhip Effect and the mischief Bullwhip Effect is not synchronization from function and data. It's more meaningful to evaluate the mischief of Bullwhip Effect than the Bullwhip Effect. So I bring forward a synthetically standard of evaluating Bullwhip Effect by order variation and other parameters. And I emphasize the analysis to the mischief and how to weaken it.
Accordingly, in this text I suppose that the manufacturer take a traditional model, which is in the condition of rigidity, and I restrain 25% of demand estimate, 50% of short gambling, 75% of batch order individually. At the same time, I evaluate the effect in weakening the impact of Bullwhip Effect by using this standard.
I bring forward a function frame originally, which is based on the famous beer countermeasure experiment. At that time (1980s), rigidity producing measure is widely used, so the simulation system takes rigidity production as unique measure. But, consider coming of marketing economic, enterprise emphasis the pliability production. Consequently, in this test I discuss the condition of restrain the mischief of Bullwhip Effect when manufacturers take pliability measure.
Then, I analyze the effects of restrain the three factors individually when manufacturers take pliability measure. And I have proved that in certain condition batch order is the main factor of causing Bullwhip Effect and draw some other conclusions.
At last, his article draws some different distribution mechanisms into the functions frame used for the research of Bullwhip Effect in SCM and enlarges the conditions of hypotheses about the scale of enterprise. According to the different assign grade and amount of order, this thesis divides the retailers into nine types and quantitatively research the affects of Certain- assign mechanism, Proportion- assign mechanism, Linear- assign mechanism, and even- assign mechanism, in the link of retail, wholesale and predictor. At last, it
关于长鞭效应产生原因的研究比较多,许多学者提出了各自的观点,总体上认为主要是由于需求预测、批量订货、价格波动和短缺博弈这四个因素共同作用造成的。但也存在需要继续研究的地方：首先，关于这四个因素的研究还不是很深入,特别是当这四个因素中的某些因素被抑制后,长鞭效应及其危害的改变,还没有细致的研究。其次，这些文章只用文字对这几个因素进行描述，没有对各种因素进行量化分析。并且，也没有说明这几个因素中哪些因素对造成长鞭效应危害的作用更大，定性的分析不多且缺乏证明，定量的分析则几乎没有。另外，用于讨论长鞭效应的模型也比较简单。本文因此在这些方面进行了一定的研究，希望对相关理论的研究有所贡献，对供应链中的企业克服长鞭效应有所帮助。主要运用的研究工具包括：供应链理论、管理学理论、数据分析、图表分析、程序运算等。
“啤酒对策实验”是由MIT实验室开发出来的一套模拟系统。文章把该系统抽象为函数，并增加了市场当期真实需求水平权重值，真实需要的定货量，短缺博弈程度参数等一系列参数、变量，得到了一个框架。该框架由零售商的到货量、销售量、库存量、定货量，批发商的到货量、发货量、库存量、定货量，生产商的产量、发货量、库存、未发货量等所构成。
文章探讨了评价长鞭效应及其危害的标准，从函数表达式和具体数据（通过程序运算得到结果数据）两个方面证明了长鞭效应和长鞭效应的危害并不同步，而评价长鞭效应不如评价长鞭效应的危害更有意义。于是，提出以订货量方差在内的多个参数综合作为评价长鞭效应危害的标准，侧重对危害及其弱化的分析。
接着，在假定生产商采用传统生产模式——刚性生产的条件下，分别对需求预测因素、缺货博弈因素、批量订货因素这三个因素进行了25%、50%、
75%程度的抑制。并运用这个标准评价了，它们对弱化供应链中的长鞭效应危害的效果。
文章最初提出的函数框架，是在“啤酒对策实验”（80年代）的基础上建立的。该模拟系统是以刚性生产作为生产商的唯一生产方式。但是，随着当今买方市场的到来，企业更强调柔性生产。于是，文章讨论了在供应链中的生产商采用柔性生产方式后，对供应链中的长鞭效应危害的弱化效果。
然后，文章采用讨论刚性生产下的长鞭效应危害的方法，分析了在供应链中采用柔性生产的情况下，分别抑制这三个因素对弱化长鞭效应危害的效果，证明出在一定条件下，批量订货因素才是造成供应链中长鞭效应危害的主要因素等一系列结论。
最后，文章引入了几种不同的分配机制，并且放大了对企业规模的假设条件。根据分配级别和订货数量的不同，把零售商分为九种类型，从定量的角度，着重探讨了分别采用确定型分配机制、比例分配机制、线性分配机制和均匀分配机制，在供应链中的零售商、批发商及生产商环节对长鞭效应危害的影响，并得出了一系列结论。
Since 1990s,Bullwhip Effect, which is a main obstacle in supply chain take more and more, people's attention. It is a very important problem in supply chain management that how to overcome Bullwhip Effect. So we should strengthen the research about Bullwhip Effect and overcome its mischief to create a positive condition for the using of modern management method.
Now there is too much research about the cause of Bullwhip Effect. Many scholars bring forward their own standpoints. In conclusion, it is affected by the following four factors: demand estimate, batch order, short gambling, and price fluctuation. But, these researches about how to restrain the four factors are not deep enough. Especially, when certain factors are restrained in supply chain there are not meticulous research about Bullwhip Effect and its mischief. The other limitation about these articles is only having some describe in words and without necessary quantity analysis. And they didn't explain which factors are more important in causing Bullwhip Effect. There are a little quantity analysis and lack of necessary proof. The quantity analysis is almost nothing. So in this text I have some research in these aspects. I hope I can do some contribution in the related theory and give some help in overcoming Bullwhip Effect. The main tools are used including: supply chain theory, management theory, data analysis, figure analysis, counting by procedures and etc.
This text abstracts the "beer countermeasure experiment" to functions and adds a series of parameters and variables to it including the value about really demand of native market, the really order and the parameter about degree of short-game. It draws into soft product to decide the turnout of the manufacturer and get a model. The model includes the Arrival, Sell, Order, and Stock of the dealer, the Arrival, Consignment, Order, Stock and Non-consignment of the wholesaler, the Turnout, Consignment, Stock and Non-consignment of the manufacturer.
In this text I studied the standard of evaluating Bullwhip Effect and its mischief. I proved that of Bullwhip Effect and the mischief Bullwhip Effect is not synchronization from function and data. It's more meaningful to evaluate the mischief of Bullwhip Effect than the Bullwhip Effect. So I bring forward a synthetically standard of evaluating Bullwhip Effect by order variation and other parameters. And I emphasize the analysis to the mischief and how to weaken it.
Accordingly, in this text I suppose that the manufacturer take a traditional model, which is in the condition of rigidity, and I restrain 25% of demand estimate, 50% of short gambling, 75% of batch order individually. At the same time, I evaluate the effect in weakening the impact of Bullwhip Effect by using this standard.
I bring forward a function frame originally, which is based on the famous beer countermeasure experiment. At that time (1980s), rigidity producing measure is widely used, so the simulation system takes rigidity production as unique measure. But, consider coming of marketing economic, enterprise emphasis the pliability production. Consequently, in this test I discuss the condition of restrain the mischief of Bullwhip Effect when manufacturers take pliability measure.
Then, I analyze the effects of restrain the three factors individually when manufacturers take pliability measure. And I have proved that in certain condition batch order is the main factor of causing Bullwhip Effect and draw some other conclusions.
At last, his article draws some different distribution mechanisms into the functions frame used for the research of Bullwhip Effect in SCM and enlarges the conditions of hypotheses about the scale of enterprise. According to the different assign grade and amount of order, this thesis divides the retailers into nine types and quantitatively research the affects of Certain- assign mechanism, Proportion- assign mechanism, Linear- assign mechanism, and even- assign mechanism, in the link of retail, wholesale and predictor. At last, it
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