An ANN Approach to Demand Forecasting in Retail Trade in Bangladesh

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 25 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.