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Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data

Author

Listed:
  • R. J. Kuo

    (National Taiwan University of Science and Technology)

  • Y. S. Tseng

    (American Megatrends Inc.)

  • Zhen-Yao Chen

    (DE LIN Institute of Technology)

Abstract

Sales forecasting plays a very important role in business operation. Many researches generally employ statistical methods, such as regression or auto-regressive integrated moving average model, to forecast the product sales. However, they only can consider the quantitative data. Some exogenous qualitative variables have more influence on forecasting result. Thus, this study attempts to propose a integrated forecasting system which is able to consider both quantitative and qualitative factors to achieve a more comprehensive result. Basically, fuzzy neural network is first employed to capture the expert knowledge regarding some qualitative factors. Then, it is combined with the time series data using an artificial immune system based back-propagation neural network. A laptop sales data set provided by a distributor in Taiwan is applied to verify the proposed approach. The computational result indicates that the proposed approach is superior to other forecasting methods. It can be used to decrease the inventory costs and enhance the customer satisfaction.

Suggested Citation

  • R. J. Kuo & Y. S. Tseng & Zhen-Yao Chen, 2016. "Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1191-1207, December.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:6:d:10.1007_s10845-014-0944-1
    DOI: 10.1007/s10845-014-0944-1
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    References listed on IDEAS

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    1. Lu, Chi-Jie & Wang, Yen-Wen, 2010. "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, Elsevier, vol. 128(2), pages 603-613, December.
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    3. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
    4. P. Du Jardin & E. Séverin, 2012. "Forecasting financial failure using a Kohonen map: a comparative study to improve bankruptcy model over time," Post-Print hal-00801853, HAL.
    5. Kuo, R. J., 2001. "A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm," European Journal of Operational Research, Elsevier, vol. 129(3), pages 496-517, March.
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    Cited by:

    1. Fatih Yiğit & Şakir Esnaf, 2021. "A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1517-1528, August.
    2. Adnan Aktepe & Emre Yanık & Süleyman Ersöz, 2021. "Demand forecasting application with regression and artificial intelligence methods in a construction machinery company," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1587-1604, August.

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