FORECASTING OF SOME KEY INDICATORS OF THE RFI AND RFP PROCESSES OF THE BULGARIAN MOBILE TELECOMMUNICATION OPERATORS

Avgustin Milanov

Abstract

The present paper regards the opportunities of forecasting of some key indicators in the “Request for Information” (RFI) and “Request for Proposal” (RFP) processes in the supply chain at the Bulgarian mobile telecommunication operators. The presented hereby forecasting is based on the use of the Holt-Winters method for exponential smoothing in the presence of additive and multiplicative seasonality and is made or indicators: “number of contracts”, “number of contracts with savings” and “number of the issued purchase orders”. The lowest “Stationary R square”, “R square” and MAPE (Mean Absolute Percentage of Error) values are used as measurement of accuracy and for selection of the best fit models that are applied. It is also important to point out that the measurement is being done for the so-called “bottle necks” or “narrow places” in the RFI and RFP processes. The purpose of this bottle-neck forecasting is to provide timely point for “Go/Not Go” decisions point for these very same process and thus to result in an improved risk management in the form of risk aversion and risk minimization.

Keywords: forecasting, Holt-Winters exponential smoothing, supply chain management, risk management, RFI and RFP process, telecommunication operators

JEL Codes: L93, O18, F47

DOI: 10.37708/el.swu.v2i2.6

CITE AS: 

References

1. Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. Retrieved from http://documents.irevues.inist.fr/handle/2042/28540
2. Chatfield, C., & Yar, M. (1988). Holt-Winters forecasting: Some practical issues. The Statistician, 37(2), 129-140. https://doi.org/10.2307/2348687
3. DeLurgio, S. A. (1998). Forecasting Principles and Applications. Pennsylvania State University: Irwin/McGraw-Hill.
4. Dimitrov, P. (2010). Short-run forecasting of tourism arrivals in separate sectors of Bulgarian tourism industry. In International Scientific Conference “Alternatives for Development of the Modern Tourism. 378-389.
5. Dimitrov, P. (2011). Long-run market situation forecasting in tourism.  Journal of Science & Research. 1, 23-33. Retrieved from August 31, 2020 from http://www.bkonk.bg/images/Body_broi_02_final.pdf
6. Dimitrov, P. (2012). Long-term forecasting of the spa and wellness subsector of the Bulgarian tourism industry. Tourism & Management Studies, 7, 140-148. Retrieved from https://www.academia.edu/1475952/Long_run_Forecasting_of_Spa_and_Wellness_Subsector_of_the_Bulgarian_Tourism_Industry
7. Dimitrov, P. (2013). Long-run forecasting of the number of the ecotourism arrivals in the municipality of Stambolovo, Bulgaria. Tourism & Management Studies, 9(1), 41-47.
8. Dimitrov, P. (2020). Експоненциално прогнозиране на туристическите пазари [Exponential Forecasting of the tourism markets]. Sofia, Bulgaria: Avangard-Prima Publishing House.
9. Dimitrov, P. M., Daleva, D. T., & Stoyanova, M. (2016). Forecasting the number of Sport Tourism Arrivals in Southwest Bulgaria. Journal of Media Critiques [JMC], 2(8), 173-182. Retrieved from https://www.ceeol.com/search/article-detail?id=470162
10. Dimitrov, P. M., Daleva, D., & Stoyanova, M. (2017). Forecasting of the Volume of the SPA and Wellness Tourism Receipts in the South-West Bulgaria. Journal of Spatial and Organizational Dynamics, 5(2), 83-99. Retrieved from https://www.jsod-cieo.net/journal/index.php/jsod/article/view/88
11. Dimitrov, P., Kalinova, M., Gantchev, G., & Nikolov, C. (2015). Exponential forecasting of the monthly volume of the tourism receipts in Bulgaria. Tourism & Management Studies 11(1), 104 110.
12. Dimitrov. P, Krasteva, R., Dimitrov, B., & Parvanov, P. (2018).  Bulgarian tourism and the problem of poverty in Bulgaria. Tourism & Management Studies, 14(2), 45-52.
13. Dimitrov, P, Krasteva, R., & Mirchova, S. (2014). Forecasting of the number of passengers serviced at the Sofia Airport. Contemporary Issues in Tourism & Management Studies. University of the Algarve. 71-88. Retrieved from https://www.ualg.pt/en/node/113050
14. Dimitrov, P., & Stoyanova, M. (2015). Long-run forecasting of the spa and wellness tourism development in Bulgaria, Varna. 82-91. Retrieved from http://unicat.nalis.bg/Record/IUV.000032739
15. Dimitrov, P., & Stoyanova, M. (2016). Long-run forecasting of the number of international tourism arrivals in Bulgaria. International Scientific Conference “Cultural Corridor Western Transbalkan Road: Cultural Tourism Without Bounderies”. South-West University “Neofit Rilski”, 125-131.
16. Gardner, E. S. (1985). Exponential Smoothing: the state of the art. Journal of Forecasting, 4(1), 1-28. https://doi.org/10.1002/for.3980040103
17. Gardner, E. S. (1987). Chapter 11: Smoothing methods for short-term planning and control. In S. Makridakis and S. C. Wheelright (Ed.), The Handbook of forecasting – A Manager’s Guide, (2nd Ed.) (pp. 174-175). New York: John Wiley & Sons. https://doi.org/10.1016/0169-2070(89)90075-7
18. Gardner, E.S., & McKenzie, E. (1985). Forecasting trends in time series. Management Science, 31(10), 1237-1246. https://doi.org/10.1287/mnsc.31.10.1237
19. Gardner, E.S., & McKenzie, E. (1988). Model identification in exponential smoothing. Journal of the Operational Research Society, 39(9), 863-867. https://doi.org/10.1057/jors.1988.146
20. Hamilton, J .D. (1994). Time Series Analysis. Princeton, NJ: Princeton University Press.
21. Holt, C. C. (1957). Forecasting trends and seasonals by exponentially weighted averages. Carnegie institute of technology. Pittsburgh ONR memorandum.
22. Hyndman, R., Koehler, A. B., Snyder, R., & Grose. S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439-454. https://doi.org/10.1016/S0169-2070(01)00110-8
23. Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.
24. Hyndman, R. J. (2014). Initializing the Holt-Winters method. Hyndsight – A blog by R. J. Hyndeman. Retrieved from http://robjhyndman.com/hyndsight/hw-initialization/
25. Ivanov, M. (2007). A try and conclusions from the forecasting of the business processes with the help of time series (a MS PowerPoint presentation in Bulgarian language). Retrieved June 07, 2016 from http://www.nbu.bg/PUBLIC/IMAGES/File/departments/informatics/Izsledvania/Martin_Ivanov_prolet_2007.pdf
26. Ledolter, J., & Abraham, B. (1984). Some comments on the initialization of exponential smoothing. Journal of Forecasting, 3(1), 79-84. https://doi.org/10.1002/for.3980030109
27. Mishev, G., & Goev, V. (2012). Statistical Analysis of Time Series. Sofia, Avangard-Prima Publishing House.
28. Pegles, C. C. (1969). Exponential forecasting: some new variations. Management Science, 15(5), 311-315.
29. Sirakov, S. (1996). Конюнктура и прогнозиране на международните пазари [Conjuncture and Forecasting of International Markets]. Sofia, Bulgaria: Stoilov Publishing House.
30. Taylor, J. W. (2003). Exponential Smoothing with a damped multiplicative trend. International Journal of Forecasting, 19(4), 715-725. https://doi.org/10.1016/S0169-2070(03)00003-7
31. Tashman, L. J., & Kruk, J. M. (1996). The use of protocols to select exponential smoothing procedures: a reconsideration of forecasting competitions. International Journal of Forecasting, 12(2), 235-253. https://doi.org/10.1016/0169-2070(95)00645-1
32. Tsay, R. S. (2005). Analysis of Financial Time Series. New York: John Wiley & Sons.
33. Winters, P.R. (1960). Forecasting sales by exponentially weighted moving averages, Management Science, 6(3), 324–342. https://doi.org/10.1287/mnsc.6.3.324
34. Williams, D. W., & Miller, D. (1999). Level-adjusted exponential smoothing for modeling planned discontinuities. International Journal of Forecasting, 15(3), 273-289. https://doi.org/10.1016/S0169-2070(98)00083-1