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Retracted: A hybrid artificial intelligence sales‐forecasting system in the convenience store industry
Author(s) -
Lee WanI,
Shih BihYaw,
Chen ChenYuan
Publication year - 2011
Publication title -
human factors and ergonomics in manufacturing and service industries
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.408
H-Index - 39
eISSN - 1520-6564
pISSN - 1090-8471
DOI - 10.1002/hfm.20272
Subject(s) - artificial neural network , computer science , sales forecasting , artificial intelligence , food industry , fuzzy logic , set (abstract data type) , operations research , machine learning , engineering , marketing , business , political science , law , programming language
Recently, there has been increasing interest in computer‐aided ergonomics and its applications, such as in the fields of intelligent robots, intelligent mobiles, intelligent stores, and so on. The operation of convenience stores (CVS) in Taiwan is facing a crossover revolution by providing multiple services, including daily fresh foods, a café, ticketing, and a grocery. Therefore, forecasting the daily sales of fresh foods is getting more and more complex due to the influence of both internal and external factors. Eventually, a reliable sales‐forecasting system will play an important role in improving business strategies and increasing competitive advantages. The purpose of this study is the development of an enhanced hybrid sales‐forecasting model of fresh foods, called ECFM (Enhanced Cluster and Forecast Model), for CVSs by combining a self‐organization map (SOM) neural network and radial basis function (RBF) neural networks. The model is evaluated for a six‐month sales data set of daily fresh foods at a chained CVS in Taiwan. Meanwhile, the performance of the proposed model is compared with that of fuzzy neural network (FNN) and cluster and forecast model (CFM). The result reveals that the proposed model is not only amenable but can also promise the fresh food sales forecasting for CVSs. © 2011 Wiley Periodicals, Inc.