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Logistic forecasting model based on the product life cycle recognition_49336

By Bill Martinez,2014-11-02 09:45
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Logistic forecasting model based on the product life cycle recognition_49336

    Logistic forecasting model based on the product life cycle recognition

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     [Abstract] In this paper, golden section method and engineering software MATLAB function in polyfit Logistic forecasting model for solving the three parameters, and the

    introduction of long maturity to determine the coefficient of ?? ?? ??, improved Logistic equation, so that it covers the entire product range forecast life cycle. Finally, a numerical example, show that the methods to identify the effectiveness

    of the product life cycle. [Keywords:] Logistic model; product life cycle; recognition 1 Introduction Product life cycle refers to the products from entering the market to the whole process of elimination from the market, like the biological life process as the product will experience the birth, growth, maturity and decline process. Based on product sales and profits, product life cycle can be divided into introduction, growth, maturity and decline of four stages. a distinctive feature of the various stages of the

    rationalization of the enterprise provided the basis for decision-making. Product life cycle theory after 50 years of development has been more mature, for each phase of the Strategy has been well established. However, the theory in

    practice, there are still many problems, one of which is the life cycle by not quite Ends clearly, in the actual implementation process is not easy to confirm. only to determine more accurately the stage products, enterprises can select the appropriate corresponding strategies. take into account consistent with the theory of biological growth cycle Logistic curve and the curve of product life cycle theory have great similarities in form, we construct the life cycle of a product Logistic equation, the stage of product simulation and

    identification, in order to use products for the enterprise to facilitate the life cycle theory. 2 Model and Solution 2.1 Mathematical model Logistic forecasting model is a bio-

    mathematician PFVerhulst Netherlands in 1838 to control

    population growth projections and export, both to be used in

    animal and plant growth and development of Research or reproduction processes, are also widely used in studies of socio-economic phenomena. The model forms ?? Yt space generally show different forms of S ?? curve, corresponding to the initial slow growth, acceleration, deceleration and stabilization in four phases, reflected in the life course, can use it to identify the product life cycle. its integral form: switch Download Center free paper affixed to the curve of the first special point http:// ?? t ?? 1 = 13.7692 ??, that the product from 1965 to mid-September 1978 period are in

    period, sales growth slow and very few sales. it through a special point of that into the growth stage. growing mid-

    point, that is the end of April 1983, the curve inflection point, the point that the product into the rapid growth phase. By 1998, sales growth is slowing, and gradually moving towards the peak, the product matured. in accordance with the growing

    maturity length is 3 times the length of the assumptions, we can see products from July 2015 to enter recession. References: [1] Chen Jianzhong, Peng Yan Fu. Channel decisions in the product life cycle model using the modified Gompertz prediction [J]. Fortnightly under the Entrepreneur World (Theory), 2008 (11). [2] Yan Hua Yuan. On the Comparison of Logistic model parameter estimation [J]. Journal of Heilongjiang Institute of Technology, 2008 (6). [3] Hangzhou Metropolis. Prediction of population and resources, carrying capacity parameter Logistic model estimates from the regression [J]. Natural Resources, 2009 (6). [4] Hong-

    nan. Logistic curve parameters of a best estimate approach [J]. Biomathematics, 1994 (3). [5] Cao Ying, Wang Yu

    time. Fuzes designed for use in ballistic artillery Logistic curve empirical formula [J]. Detection & Control, 2008 (4). [6] Wang Xiaobo. Logistic by nonlinear least square fitting curve and its proliferation of computer Technology in the

    application [J]. Shanghai University (Natural Science), 1995 (4). Links to Research Papers Download Center http://

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