Abstract—For a small retail trading chain, demand
forecasting is the main driver to effectiveness and efficiency.
However, as large number of varied models and products are
marketed through a super market, several attributes affect
forecasting. Because of these affecting parameters, nonlinearity
arises. As a result, traditional forecasting approaches can not
provide good estimation of demand. A fuzzy neural network
analysis can provide better solution in this case. This research
first analyzed the trend and seasonality patterns of a selected
product in a retail trading chain in Bangladesh. Then demand
was forecasted using traditional Holt-Winter’s model. The
same was done again using artificial neural network (ANN)
with fuzzy uncertainty. Finally, the errors, measured in terms
of MAPE, were compared for finding the best fitting
forecasting approach. The research found that the error levels
in Holt-Winter’s approach are higher than those obtained
through fuzzy ANN approach. This is because of influence of
several factors on demand function in retail trading system. It
was also observed that as forecasting period becomes smaller,
the ANN approach provides more accuracy in forecast.
Index Terms—Nonlinear demand estimation, retail trading
chain, Holt-Winter’s approach, neural network analysis.
Dr. M. Ahsan Akhtar Hasin is a Professor in the Department of,
Industrial and Production Engineering (IPE) at Bangladesh University of
Engineering and Technology (BUET), Dhaka-1000, Bangladesh. Email:
aahasin@ipe.buet.ac.bd.
Mr. Shuvo Ghosh is an Assistant Professor in the Department of,
Industrial and Production Engineering (IPE) at Bangladesh University of
Engineering and Technology (BUET), Dhaka-1000, Bangladesh.
Dr. Mahmud Akhtar Shareef is an Associate Professor in the School of
Business at North South University, Dhaka, Bangladesh.
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Cite:M. Ahsan Akhtar Hasin, Shuvo Ghosh, and Mahmud A. Shareef, "An ANN Approach to Demand Forecasting in Retail Trade in Bangladesh," International Journal of Trade, Economics and Finance vol.2, no.2, pp. 154-160, 2011.