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Comparison of sales forecasting models for an innovative agro-industrial product: Bass model versus logistic function

Author

Listed:
  • Orakanya Kanjanatarakul

    (Rajamangala University of Technology Lanna Chiang Mai)

  • Komsan Suriya

    (Chiang Mai University)

Abstract
This paper compares the accuracy of sales forecasting between Bass model (Bass, 1969) and Logistic function (Stoneman, 2010). It uses several ways to estimate the models; least squares with quadratic interpolation, least squares with quasi-Newton, maximum likelihood with quadratic interpolation and maximum likelihood with quasi-Newton. It applies the technique to an innovative agro-industrial product, feta cheese from buffalo milk. Then it compares the performance of the models by Mean Absolute Percentage Error (MAPE) of the out-of-sample test. It matches Bass model and Logistic function which are estimated by the same method and compare their performances. Moreover, it compares the best Bass model with the best Logistic function regardless of the estimation method. The results reveal that, in most pairs, Logistic function is superior than Bass model when the model uses the data between 7 to 24 months which MAPE of Logistic function are improved tremendously. However, the performance of the best Logistic function is insignificantly different to that of Bass model.

Suggested Citation

  • Orakanya Kanjanatarakul & Komsan Suriya, 2012. "Comparison of sales forecasting models for an innovative agro-industrial product: Bass model versus logistic function," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(4), pages 89-106, December.
    • Handle: RePEc:chi:journl:v:1:y:2012:i:4:p:89-106
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    Citations

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    Cited by:

    1. José F. C. Castro & Davidson C. Marques & Luciano Tavares & Nicolau K. L. Dantas & Amanda L. Fernandes & Ji Tuo & Luiz H. A. de Medeiros & Pedro Rosas, 2022. "Energy and Demand Forecasting Based on Logistic Growth Method for Electric Vehicle Fast Charging Station Planning with PV Solar System," Energies, MDPI, vol. 15(17), pages 1-21, August.
    2. Sudtasan, Tatcha & Mitomo, Hitoshi, 2017. "Comparison of Diffusion Models for Forecasting the Growth of Broadband Markets in Thailand," 14th ITS Asia-Pacific Regional Conference, Kyoto 2017: Mapping ICT into Transformation for the Next Information Society 168541, International Telecommunications Society (ITS).
    3. Komsan Suriya & Orakanya Kanjanatarakul, 2013. "Forecasting the sales of an innovative agro-industrial product with limited information: A case of feta cheese from buffalo milk in Thailand," EcoMod2013 5422, EcoMod.

    More about this item

    Keywords

    Innovative product; agro-industrial product; sales forecasts; Bass model; Logistic function.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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