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Elahi, M., Khakbaz, A. (2018). The Forecasting of Supply Chain Demand Using Time Series Techniques in Conditions the No Information Sharing; Case Study: Fioreh Big Market. Electronic and Cyber Defense, 20(60), 46-59.
Maryam Elahi; Amir Khakbaz. "The Forecasting of Supply Chain Demand Using Time Series Techniques in Conditions the No Information Sharing; Case Study: Fioreh Big Market". Electronic and Cyber Defense, 20, 60, 2018, 46-59.
Elahi, M., Khakbaz, A. (2018). 'The Forecasting of Supply Chain Demand Using Time Series Techniques in Conditions the No Information Sharing; Case Study: Fioreh Big Market', Electronic and Cyber Defense, 20(60), pp. 46-59.
Elahi, M., Khakbaz, A. The Forecasting of Supply Chain Demand Using Time Series Techniques in Conditions the No Information Sharing; Case Study: Fioreh Big Market. Electronic and Cyber Defense, 2018; 20(60): 46-59.

The Forecasting of Supply Chain Demand Using Time Series Techniques in Conditions the No Information Sharing; Case Study: Fioreh Big Market

مدیریت زنجیره تأمین
Article 4, Volume 20, Issue 60, May 2019, Pages 46-59    PDF (1.98 M)
Document Type: Conceptual Paper
Authors
Maryam Elahi* 1; Amir Khakbaz2
1M.Sc. Student in Industrial Engineering, college engineering, Private University of North Amol
2Assistant Professor of Industrial Engineering, Private University of North Amol, college engineering
Receive Date: 07 January 2019,  Revise Date: 20 January 2019,  Accept Date: 20 February 2019 
Abstract
The food industry is among the industries where information sharing with the supply chain partners has been taken into consideration. However, this information is not shared due to issues such as high costs of investment in information technology, lack of trust from managers, and fear of data leaks out. The purpose of this study is to provide a solution for cases in which there are no desire to share information in the chain. In this study, we evaluate a DDI strategy by use of different forecasting methods such as SMA, WMA, SES and DES, in the Fiorella Amol large store. The accuracy and of these methods are determined using MSE, MAD, STD, Bias, MAPE methods. The obtained results show that, based on the methods MAD, MSE, STD, and Bias, the WMA forecasting method has better efficiency than the SMA does. In addition, based on the MAPE error calculation method, the SES forecasting method is the best one. In the other words, the present study provides an opportunity to use the WMA method instead of the SMA method in the future studies.
Keywords
Supply Chain Management; Downstream Demand Inference; Weighted Moving Average; Seasonal Exponential Smoothing; Double Exposure Smoothing
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