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A Study on Modeling and Forecasting of Mobile Phone Sales Trends

이동통신 단말기 판매 추이에 대한 모형 및 수요예측에 관한 연구

  • Kim, Min-Jeong (Department of Consumer Economics, Sookmyung Women's University)
  • 김민정 (숙명여자대학교 소비자경제학과)
  • Received : 2016.03.31
  • Accepted : 2016.06.02
  • Published : 2016.06.30

Abstract

Among high-tech products, the mobile phone has experienced a rapid rate of innovation and a shortening of its product life cycle. The shortened product life cycle poses major challenges to those involved in the creation of forecasting methods fundamental to strategic management and planning systems. This study examined whether the best model applies to the entire diffusion life span of a mobile phone. Mobile phone sales data from a specific mobile service provider in Korea from March of 2013 to August of 2014 were analyzed to compare the performance of two S-shaped diffusion models and two non-linear regression models, the Gompertz, logistic, Michaelis-Menten, and logarithmic models. The experimental results indicated that the logistic model outperforms the other three models over the fitted region of the diffusion. For forecasting, the logistic model outperformed the Gompertz model for the period prior to diffusion saturation, whereas the Gompertz model was superior after saturation approaches. This analysis may help those estimate the potential mobile phone market size and perform inventory and order management of mobile phones.

하이테크 제품 중에서 이동통신 단말기는 빠른 속도로 혁신이 이루어지고 있으며 이에 따라 제품수명주기도 짧아지고 있다. 이렇게 짧아진 제품수명주기를 정확히 예측하기 위해서는 정확한 수요예측방법론의 선택이 중요하며 이는 전략적 경영계획 수립에 가장 기본적인 요소라고 할 수 있다. 본 연구의 목적은 이동통신 단말기의 전체 확산 수명에 적용될 수 있는 최적의 모형을 제시하는 것이다. 우리는 2013년 3월부터 2014년 8월까지 국내 특정 이동통신 서비스 사업자의 이동통신 단말기 판매 데이터를 활용하여 이동통신 단말기의 판매추이 및 수요예측을 위한 최적의 모형을 제시하고자 한다. 본 연구에서는 네 가지 모형의 성능을 비교분석하였는데 두 가지 S자형 확산모형인 Gompertz와 logistic 모형, 두 가지 비선형 회귀모형인 Michaelis-Menten과 logarithmic 모형을 비교한다. 모형 적합도에 따르면 logistic 모형이 모형일치성에 있어서 다른 세 개의 모형보다 성능이 우수한 것으로 발견되었으며 수요예측모델로는 확산이 정체하기 전까지는 logistic 모형이 우수하며 포화단계에 근접할수록 Gompertz 모형이 적합한 것으로 나타났다. 이러한 분석결과는 이동통신 단말기 시장 규모를 추정하거나 이동통신 단말기의 재고 및 주문관리를 하는데 있어서 유용한 자료로 활용될 수 있을 것이다.

Keywords

References

  1. S. D. Wu, B. Aytac, R. T. Berger, C. A. Armbruster, "Managing Short Life-Cycle Technology Products for Agere Systems", Interfaces, Vol. 36, Issue 3, pp. 234-247, 2006. DOI: http://dx.doi.org/10.1287/inte.1050.0195
  2. T. Teng, "Cell phone models change frequently", NEWSVINE, 2009 [cited 2009 Feb 24], Available From: http://www.newsvine.com/_news/2009/02/24/2471152-cell-phone-models-change-frequently(accessed Feb, 24, 2016)
  3. A. Botelho, L. C. Pinto, "The diffusion of cellular phones in Portugal", Telecommunications Policy, Vol. 28, Issues 5-6, pp. 427-437, 2004. DOI: http://dx.doi.org/10.1016/j.telpol.2003.11.006
  4. L. F. Gamboa, J. Otero, "An estimation of the pattern of diffusion of mobile phones: The case of Colombia", Telecommunications Policy, Vol. 33, Issues 10-11, pp. 611-620, 2009. DOI: http://dx.doi.org/10.1016/j.telpol.2009.08.004
  5. C. Michalakelis, D. Varoutas, T. Sphicopoulos, "Diffusion models of mobile telephony in Greece", Telecommunications Policy, Vol. 32, Issues 3-4, pp. 234-245, 2008. DOI: http://dx.doi.org/10.1016/j.telpol.2008.01.004
  6. F. Wu, W. Chu, "Diffusion models of mobile telephony", Journal of Business Research, Vol. 63, Issue 5, pp. 497-501, 2010. DOI: http://dx.doi.org/10.1016/j.jbusres.2009.04.008
  7. K. Sandrasegaran, K. Pillay, P. Tsang, "Forecasting the Growth of GSM networks in Australia using Regression Analysis", Proc. of 3rd Workshop on Internet, Telecommunications and Signal Processing, pp. 299-305, 2004.
  8. L. Wu, K. Sandrasegaran, "Forecasting Asia Pacific Mobile Market Trends using Regression Analysis", Proc. of 6th International Conference on the Management of Mobile Business, pp. 1-6, 2007. DOI: http://dx.doi.org/10.1109/icmb.2007.30
  9. T. Levitt, "Exploit the product life cycle", Harvard Business Review, Vol. 43, pp. 81-94, 1965.
  10. QuickMBA, "The product life cycle", Available From: http://www.quickmba.com/marketing/product/lifecycle/ (accessed March, 14, 2016)
  11. P. McBurney, S. Parsons, J. Green, "Forecasting market demand for new telecommunications services: an introduction", Telematics and Informatics, Vol. 19, Issue 3, pp. 225-249, 2002. DOI: http://dx.doi.org/10.1016/S0736-5853(01)00004-1
  12. E. M. Rogers, Diffusion of Innovations(5th Edition). Free Press, 2003.
  13. F. M. Bass, "A new product growth for model consumer durables", Management Science, Vol. 15, No. 5, pp. 215-227, 1969. DOI: http://dx.doi.org/10.1287/mnsc.15.5.215
  14. V. Mahajan, E. Muller, F. M. Bass, "New product diffusion models in marketing: A review and directions for research", Journal of Marketing, Vol. 54, No. 1, pp. 1-26, 1990. DOI: http://dx.doi.org/10.2307/1252170
  15. D. G. Bonett, "New product sales forecasting using a growth curve model", Journal of Applied Business Research, Vol. 3, No. 2, pp. 119-123, 1987. DOI: http://dx.doi.org/10.19030/jabr.v3i2.6540
  16. N. Meade, "Forecasting using growth curves-an adaptive approach", Journal of the Operational Research Society, Vol. 36, No. 12, pp. 1103-1115, 1985. DOI: http://dx.doi.org/10.2307/2582342
  17. H. Gruber, F. Verboven, "The diffusion of mobile telecommunications services in the European Union", European Economic Review, Vol. 45, Issue 3, pp. 577-588, 2001. DOI: http://dx.doi.org/10.1016/S0014-2921(00)00068-4
  18. P. Rouvinen, "Diffusion of digital mobile telephony: Are developing countries different?", Telecommunications Policy, Vol. 30, Issue 1, pp. 46-63, 2006. DOI: http://dx.doi.org/10.1016/j.telpol.2005.06.014
  19. G. Intepe, T. Koc, "The use of S curves in technology forecasting and its application on 3D TV technology", International Scholarly and Scientific Research & Innovation, Vol. 6, No. 11, pp. 2491-2495, 2012.
  20. P. Pflaumer, "Forecasting the U.S. Population with the Gompertz Growth Curve", Proc. of Joint Statistical Meetings, pp. 4967-4981, 2012.
  21. C. V. Trappey, H. Wu, "An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles", Advanced Engineering Informatics, Vol. 22, Issue 4, pp. 421-430, 2008. DOI: http://dx.doi.org/10.1016/j.aei.2008.05.007
  22. J. G. De Gooijer, R. J. Hyndman, "25 years of time series forecasting", International Journal of Forecasting, Vol. 22, Issue 3, pp. 443-473, 2006. DOI: http://dx.doi.org/10.1016/j.ijforecast.2006.01.001
  23. S. Park, J. Oh, "Regression models based on cumulative data for forecasting of new product", Journal of the Korean Data & Information Science Society, Vol. 20, No. 1, pp. 117-124, 2009.
  24. V. Bianco, O. Manca, S. Nardini, "Electricity consumption forecasting in Italy using linear regression models", Energy, Vol. 34, Issue 9, pp. 1413-1421, 2009. DOI: http://dx.doi.org/10.1016/j.energy.2009.06.034
  25. J. Schwartz, A. Marcus, "Mortality and air pollution in London: a time series analysis", American Journal of Epidemiology, Vol. 131, Issue 1, pp. 185-194, 1990. https://doi.org/10.1093/oxfordjournals.aje.a115473
  26. Musicmetric, "Time series views: daily and cumulative", Available from: http://knowledgebase.musicmetric.com/tutorials/basic-app-usage/daily-and-cumulative-views/(accessed Feb, 20, 2016)
  27. P. H. Franses, D. v. Dijk, Non-linear time series models in empirical finance(1st Edition). Cambridge University Press, 2000. DOI: http://dx.doi.org/10.1017/CBO9780511754067
  28. R. R. Levary, D. Han, "Choosing a technological forecasting method", Industrial Management, Vol. 37, No. 1, pp. 14-18, 1995.
  29. H. Jaakkola, M. Gabbouj, Y. Neuvo, "Fundamentals of technology diffusion and mobile phone case study", Circuits Systems and Signal Processing, Vol. 17, No. 3, pp. 421-448, 1998. DOI: http://dx.doi.org/10.1007/BF01202301
  30. Wikipedia, "Michaelis-Menten kinetics", Available from: http://en.wikipedia.org/wiki/Michaelis-Menten_kinetics (accessed Feb, 4, 2016)
  31. A. M. Brown, "A step-by-step guide to non-linear regression analysis of experimental data using a Microsoft Excel spreadsheet", Computer Methods and Programs in Biomedicine, Vol. 65, Issue 3, pp. 191-200, 2001. DOI: http://dx.doi.org/10.1016/S0169-2607(00)00124-3
  32. A. Goldman, "Short product life cycles: implications for the marketing activities of small high-technology companies", R&D Management, Vol. 12, Issue 2, pp. 81-90, 1982. DOI: http://dx.doi.org/10.1111/j.1467-9310.1982.tb00487.x
  33. N. Meade, T. Islam, "Technological Forecasting-Model Selection, Model Stability, and Combining Models", Management Science, Vol. 44, No. 8, pp. 1115-1130, 1998. DOI: http://dx.doi.org/10.1287/mnsc.44.8.1115
  34. J. P. Martino, Technological forecasting for decision making(3rd Edition). McGraw-Hill, 1992.
  35. J. Tidd, Gaining momentum: Managing the Diffusion of Innovations. Imperial College Press, 2010. DOI: http://dx.doi.org/10.1142/p625