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A hybrid modeling approach for demand forecasting: a case study of raw material forecasting in the restaurant chain business

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dc.contributor.advisor Vatcharaporn Esichaikul (Chairperson) en_US
dc.contributor.author Thirut Supawongvisarn en_US
dc.contributor.other Guha, Sumanta (Member) en_US
dc.contributor.other Dailey, Matthew N. (Member) en_US
dc.date.accessioned 2015-01-12T10:43:50Z
dc.date.available 2015-01-12T10:43:50Z
dc.date.issued 2010-08 en_US
dc.identifier.other AIT RSPR no.IM-10-05 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/477
dc.description Submitted in partial fulfillment of the requirements for the degree of Masters of Science in Information Management. en_US
dc.description.abstract Demand forecasting plays an important role in many kinds of business. A restaurant chain business, the business that has a relatively large supply chain process involving several fresh raw materials needs an accurate demand forecasting. Therefore, this paper is aimed at developing the demand forecasting of raw material based on a hybrid model that combines the most two commonly used time series forecasting models between Autoregressive Integrated Moving Average (ARIMA) for a linear component and Artificial Neural Networks (ANNs) for a nonlinear relationships. The idea of a hybrid model is simply derived from using the strength feature of these two different models to capture linearity and nonlinearity of data and to improve the forecasting accuracy. The Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the model performance. The development of hybrid model with three different real data sets is indicated that a hybrid model is a promising approach to improve forecasting accuracy as it gives better forecasting result than ARIMA and ANN models. en_US
dc.description.sponsorship RTG Fellowship en_US
dc.language.iso eng en_US
dc.publisher Asian Institute of Technology en_US
dc.subject Time series forecasting en_US
dc.subject Prediction system en_US
dc.subject Autoregressive Integrated Moving Average (ARIMA) en_US
dc.subject Artificial Neural Networks (ANNs) en_US
dc.subject Supply chain management en_US
dc.subject Demand forecasting en_US
dc.subject Artificial neural network en_US
dc.subject Hybrid model en_US
dc.subject Restaurant chain business en_US
dc.subject.lcsh Others en_US
dc.title A hybrid modeling approach for demand forecasting: a case study of raw material forecasting in the restaurant chain business en_US
dc.type Research Report en_US
dc.rights.holder Copyright (C) 2010 by Asian Institute of Technology. en_US

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