A neural approach to product demand forecasting

Nafisa Mahbub, Sanjoy Kumar Paul and Abdullahil Azeem

International Journal of Industrial and Systems Engineering, 2013, vol. 15, issue 1, 1-18

Abstract: This paper develops an artificial neural network (ANN) model to forecast the optimum demand as a function of time of the year, festival period, promotional programmes, holidays, number of advertisements, cost of advertisements, number of workers and availability. The model selects a feed-forward back-propagation ANN with 13 hidden neurons in one hidden layer as the optimum network. The model is validated with a furniture product data of a renowned furniture company. The model has also been compared with a statistical linear model named Brown's double smoothing model which is normally used by furniture companies. It is observed that ANN model performs much better than the linear model. Overall, the proposed model can be applied for forecasting optimum demand level of furniture products in any furniture company within a competitive business environment.

Keywords: product demand forecasting; artificial neural networks; ANNs; Brown's double smoothing model; furniture products; linear modelling. (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:15:y:2013:i:1:p:1-18

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