Demand forecasting based on natural computing approaches applied to the foodstuff retail segment
作者： Claudimar Pereira da Veiga , Cássia Rita Pereira da Veiga , Weslly Puchalski , Leandro dos Santos Coelho , Ubiratã Tortato
DOI: 10.1016/J.JRETCONSER.2016.03.008
关键词:
摘要: Abstract The purpose of this paper is to compare the accuracy demand forecasting between two classical linear models (Autoregressive and Integrated Moving Average -ARIMA Holt-Winter) nonlinear based on natural computing approaches (Wavelets Neural Networks - WNN Takagi-Sugeno Fuzzy System TS), all applied aggregated retail sales three groups perishable food products from 2005 2013. Moreover, evaluates impact satisfaction rate overall economic performance business operations. most accurate model, WNN, had a 98.27% for Group A, 98.83% B 98.80% C. estimated loss revenue R$1329.14 million/year with minimum 166 tons/year, which means that results are 37.67% more efficient than TS, 57.49% higher ARIMA 76.79% HW. This presents main contributions: (i) it examines question not evaluated in literature foodstuff segment generates better practical results, (ii) proposes single model could be different product serves organization as whole good relationship cost benefit process (iii) like previous studies, proves plays an important role can generate competitive advantage incorporated into its strategy.
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