This research looked to find a structural model to forecast future global sales of DRAM 1Mb. Determining demand for a new high tech product that is replacing an older technology presents challenges to manufacturers and price setters. Understanding the market and where it is headed are necessary for decisions regarding manufacturing investments. The concept of the product lifecycle and the categories of adopters were explored to help understand where the market stood and what changes might be coming in the future.
The structural model was created with aggregating the global DRAM shipments with the average memory content per electronic device. Data was obtained through inSpectrum, the IMF and the World Bank. Quarterly and monthly data for the period of January, 2006 to June, 2010 was used for the analysis. The goal was to see if price and income were the mitigating factors to predict demand of global DRAM. Through multiple regression analysis it was found that income held the strongest relationship to demand. Price was not a significant variable in the three computer device markets. Alternative indicators of price could be further studied to test for validity.

Aizcorbe, A. (2005). Moore's law, competition, and Intel's productivity in the mid-1990s. American Economic Review, 95(2), 305-308.
Aizcorbe, A., & Kortum, S. (2004). Moore's law and the semiconductor industry: A vintage model.
Armstrong, J. S. (2006). Demand forecasting: Evidence-based methods. Update.
Dezhi, C., & Richard, L.-H. (2011). Modes of technological leapfrogging: Five case studies from China. J. Eng. Technol. Manag., 28(1-2), 93-108.
Field, M. K., & Pagoulatos, E. (1997). The cyclical behavior of price elasticity of demand. Southern Economic Journal, 64(1), 118-129.
Friedman, M. (1962). Price theory. New Brunswick: Transaction Publishers.
Hamel, G. (1997). Killer strategies that make shareholders rich. Fortune, 70-84.
Hawkins, E. R. (1957). Methods of estimating demand. Journal of Marketing, 21, 428-438.
Kang, J. (2010). A Study of the DRAM industry. Unpublished Masters Thesis, Massachusetts Institute of Technology, Massachusetts.
Karlgaard, R. (2003). CEOs talk tech. Forbes, 172, 33.
Kim, C.-W., & Lee, K. (2003). Innovation, technological regimes and organizational selection in industry evolution: a 'history friendly model' of the DRAM industry. Ind Corp Change, 12(6), 1195-1221.
Levary, R. R., & Han, D. (1995). Choosing a technological forecasting method. Industrial Management, 37(1), 14.
McGuigan, J. R., Moyer, R. C., & Harris, F. H. B. (2008). Economics for managers: Thomson/ South-Western.
Moore, G. (2002). Crossing the chasm, marketing and selling technology to mainstream customers. New York: Harper Business.
Patterson, D., Anderson, T., Cardwell, N., & Fromm, R. (1997). A case of intelligent DRAM: IRAM. University of California.
Pindyck, R. S., & Rubinfeld, D. L. (1991). Econometric models & economic forecasts. New York: McGraw-Hill
Rogers, E. (1983). Diffusion of innovations. New York: Free Press.
Shin, J.-S., & Jang, S.-W. (2005). Creating first-mover advantages: The case of Samsung electronics. Unpublished Masters Thesis, University of Singapore, Singapore.
Tharakan, P. K. M. (1995). The Japan-EC DRAMs anti-dumping undertaking: Was it justified? What purpose did it serve? : Center for Developmment Studies, University of Antwerp-(UFSIA).
Varian, H. R. (2001). High-technology industries and market structure.
Victor, N. M., & Ausubel, J. H. (2002). DRAMs as model organisms for study of technological evolution. Technological Forecasting and Social Change, 69(3), 243-262.
Wingard, D. E. (2009). Addressing interleaved multichannel memory challenges. EDN, 54(16), 32-36.