Improved Supply Chain Management Performance by Applying Hybrid Forecast Method

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Shiri, Davood | 2014

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 45982 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Hajji, Alireza
Abstract:
In today’s competitive world, using an efficient forecast method is necessity for companies. To now, many forecast methods have been developed, but many of them have not an expected efficiency. In this research, we develop a new hybrid forecast method with application of forecasting retails demand. The hybrid method is the combination of ARIMA method and neural networks. To test the efficiency of the method we use the 96 weeks data of plastic containers demand. We also comprise the hybrid method with other forecast methods including naïve method, ARIMA method and neural network method by applying root mean square error and mean absolute percentage error indexes. In the case of plastic containers demand, the hybrid method outperforms other forecast methods. We also comprise these methods based on total cost by applying least unit cost lot sizing method. The result of this comparison shows that the hybrid method may cause savings in total cost in comparison with other methods. In this case, we infer that applying developed hybrid method may be suitable for use in supply chain management
Keywords:
Supply Chain Management (SCM) ; Forecasting ; Time Series ; Neural Network ; Least Unit Cost Method

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