A two stage fuzzy piecewise logistic model for penetration forecasting

• Use historical data and the data investigated by the Scenario and Delphi methods.
• Propose a two stage fuzzy piecewise logistic growth model for sale forecasting.
• Forecast the market shares of the optimistic, pessimistic and most possible scenarios.
• Demonstrate two cases in the Television and Telecommunication industries of the global market.
• Outperform the technology substitution model or the Norton and Bass diffusion model according to MAE, MSE and MAPE.

It is undeniably crucial for a firm to be able to make a forecast regarding the sales volume of new products. However, the current economic environments invariably have uncertain factors and rapid fluctuations where decision makers must draw conclusions from minimal data. Previous studies combine scenario analysis and technology substitution models to forecast the market share of multigenerational technologies. However, a technology substitution model based on a logistic curve will not always fit the S curve well. Therefore, based on historical data and the data forecast by both the Scenario and Delphi methods, a two stage fuzzy piecewise logistic growth model with multiple objective programming is proposed herein. The piecewise concept is adopted in order to reflect the market impact of a new product such that it can be possible to determine the effective length of sales forecasting intervals even when handling a large variation in data or small size data. In order to demonstrate the model's performance, two cases in the Television and Telecommunication industries are treated using the proposed method and the technology substitution model or the Norton and Bass diffusion model. A comparison of the results shows that the proposed model outperforms the technology substitution model and the Norton and Bass diffusion model.