Demand Management using Autoregressive-Time Series Modeling in Mobile Value-Added Services

Document Type : Original Article

Authors

1 Ph.D student, Amir Kabir University of Technology.

2 Professor, Amir Kabir University of Technology.

Abstract

     Emerging of Value-Added Services (VAS) as a modern supply sector in the field of mobile networks requires some elements such as content providers, intermediate companies, as well as operators, which called service supply chain. Formation of such service supply chain produces some challenges consist of management and modeling of demand trend, customer behavior and Bullwhip Effect. This paper aims to perform a precise evaluation on trend of demand in the mobile VAS area and also the Bullwhip Effect. Considering Conditional Autoregressive effects on demand trend, it has been recommended to use of ARCH class models in time series analysis. The results of this paper show that ARMA (1,1)/EGARCH (1,1) model is more powerful than GJR and GARCH models in reducing the Bullwhip Effect of this special time series demand.

Keywords

References

1. Abbasi, M., Estakhrian, A. R. (2017). Big data applied programming for supply chain management in service and production sectors. Information technology and computer science national conferences, Islamic Azad Univesity, Sepidan, Iran.
2. Al-Debei, M. M., & Al-Lozi, E. (2014). Explaining and predicting the adoption intention of mobile data services: A value-based approach. Computers in Human Behavior, 35, 326-338.
3. Bing, L. I. U., & TANG, S. L. (2006). Revenue-Sharing Analysis in the Mobile Value-Added Services. The Journal of China Universities of Posts and Telecommunications, 13(4), 102-105.
4. Bollerslev, T., Engle, R. F., & Nelson, D. B. (1995). ARCH models, in ‘Handbook of Econometrics’, Vol. 4.
5. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
6. Buchmeister, B., Friscic, D., & Palcic, I. (2014). Bullwhip effect study in a constrained supply chain. Procedia Engineering, 69, 63-71.
7. Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management science, 46(3), 436-443.
8. Chen, F., Ryan, J. K., & Simchi‐Levi, D. (2000). The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics (NRL), 47(4), 269-286.
9. Dai, J., Peng, S., & Li, S. (2017). Mitigation of Bullwhip Effect in Supply Chain Inventory Management Model. Procedia engineering, 174, 1229-1234.
10. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987-1007.
11. International Telecommunication Union (ITU). Measuring the Information Society Report.http://www.itu.int/en/ITU-D/Statistics/ Pages/publications/mis2016, Accessed on Mar 6, 2017.
12. Nemtajela, N., & Mbohwa, C. (2017). Relationship between inventory management and uncertain demand for fast moving consumer goods organisations. Procedia Manufacturing, 8, 699-706.
13. Ohyver, M., & Pudjihastuti, H. (2018). Arima Model for Forecasting the Price of Medium Quality Rice to Anticipate Price Fluctuations. Procedia Computer Science, 135, 707-711.
14. Ouyang, Y., & Li, X. (2010). The bullwhip effect in supply chain networks. European Journal of Operational Research, 201(3), 799-810.
15. Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
16. Qin, M., Tang, C. H. H., Jang, S. S., & Lehto, X. (2017). Mobile app introduction and shareholder returns. Journal of Hospitality and Tourism Management, 31, 173-180.
17. Rahimi, SH.H, Sharifi, M. & Shahriari, M.R. (2017). Designing a Supply Chain Resilience Model (Case Study: Iran Welfare Organization). Journal of Industrial Management Perspective, 27,127-150 (In Persian).
18. Rezaei, P., Adel Azar, A., Taghavi, A. & Moghbel, A. (2014). Proposing service only supply chain performance evaluation by fuzzy cognitive mapping approach (Case study: Insurance industry). Journal of Industrial Management Perspective, 16, 73-95 (In Persian).
19. Sirikasemsuk, K., & Luong, H. T. (2017). Measure of bullwhip effect in supply chains with first-order bivariate vector autoregression time-series demand model. Computers & Operations Research, 78, 59-79.
20. Talebi, D., Airon, F. (2015). Identifying supply chain risks and supplier selection by network analysis processing (Case Study: Automobile Industries). Journal of Industrial Management Perspective, 17, 31-43 (In Persian).
21. Wang, K. (2015). Determinants of mobile value-added service continuance: The mediating role of service experience. Information & Management, 52(3), 261-274.
22. Xu, J., & Yang, T. J. (2015). Telecom value-added Service Supply Chain Implanting Advertising Elements with two Service Providers. Journal of Interdisciplinary Mathematics, 18(3), 197-205.
23. Wang, Y., Wallace, S. W., Shen, B., & Choi, T. M. (2015). Service supply chain management: A review of operational models. European Journal of Operational Research, 247(3), 685-698.
24. Zhao, L., Lu, Y., Zhang, L., & Chau, P. Y. (2012). Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision support systems, 52(3), 645-656.
Journal of Industrial Management Perspective
Volume 8, Issue 2 - Serial Number 30
September 2018
Pages 9-30

Files

Share

How to cite

Statistics