engleski

Extensions of Logistic Growth Model for the Forecasting of Product Life Cycle Segments

Proper forecast of product market diffusion enables optimal planning of resources, investments, revenue, marketing and sales. Quantitative forecasting methods for this purpose rely on sigmoidal growth models such as logistic growth and Bass model, which are acceptable for the first adoption interval of product life cycle (PLC). Modelling of other PLC segments requires complex models that need large set of input data that limits their application for the forecasting purposes. This paper presents extensions of the logistic growth model that combine the principle of sigmoidal growth and the concept of interpolation splines. In addition, adaptation of the logistic model is shown to be congruent with Bass model. Applications of developed models for the forecasting of PLC segments are analysed and examined, together with possible ways of interaction between different products. Developed models and interaction types enable forecasting of entire PLC with minimum set of input data, or assessment of qualitative forecasting results.

Product life cycle, logistic growth model, Bass model, forecasting

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