INTEGRATING TECHNOLOGICAL OBSOLESCENCE IN QUANTITATIVE FORECASTING OF MEMORY DEVICE FAILURE COST
survivor curve over its entire life cycle. This approach has been demonstrated to be inadequate as it has understated
the true impact of technological obsolescence. Nowadays there are so many devices whose utility is very sensitive
to technological innovation. For these products, relying on the traditional historical mortality approach simply leads
to a gross error in the estimation of life cycle values such as depreciation in their utility or their life cycle cost. This
paper contains three distinctive parts. In the first part, the paper presents analogical derivations of various growth
curves and the rationale which leads to the establishment of a statistical methodology for determining the retirement
rate due to wear and tear, failure by accidents, and/or simply by age related deterioration. Another significant
development presented in this first part is the derivation of the obsolescence chart for a given product. The paper
goes on to describe a method to combine the impact of both traditional mortality influence and technological
influence on the life cycle of the property. The second part of the paper is used to demonstrate the results of
the proposed combined quantitative forecasting methodology via a case study which involves the substitution
of mechanical memory devices by solid state (flash) memory devices. This paper discusses the importance of
combining the effects of mortality and technological obsolescence when conducting an economic forecast.
Obeng, Gershom, 2013, Design and Implementation of Software for Technological Forecasting and Modeling Technological Obsolescence. Master of Science. Old Dominion University.
Barreca, Stephen, 2000, Technology Life
Cycles and Technology Obsolescence [online]. 1. BCRI Incorporated. Available from:
Ernst, H. The Use of Patent Data for Technological
Forecasting: The Diffusion of CNC-Technology in the
Machine Tool Industry. Small Business Economics [online]. 1997. [Accessed 19 August 2015]. Vol. 9, no. 4, p.
-381. DOI 10.1023/A:1007921808138. Available from:
Modis, Theodore. A Scientific Approach to Managing
Competition. The Industrial Physicist [online]. 2003.
P. 22-23. [Accessed 19 August 2015]. Available from:
Kucharavy, Dmitry and De Guio, Roland. Logistic Substitution Model and Technological Forecasting. In: TRIZ Future 2008 - Synthesis in Innovation. 2008. [Accessed 19 August2015]. p. 65-73.
Beach, Brian. How long do disk drives last?. Backblaze
Blog — The Life of a Cloud Backup Company
[online]. 2013. [Accessed 21 May 2015]. Available
HowStuffWorks,. Computer Memory Image Gallery - How-StuffWorks. [online]. 2015. [Accessed 21 May 2015]. Availablefrom: https://computer.howstuffworks.com/computermemory-pictures.htm#page=12.
Yinug, Falan. The Rise of the Flash Memory Market:
Its Impact on Firm Behavior and Global Semiconductor
Trade Patterns. United States International
Trade Commission Journal of International Commerce
and Economics [online]. 2007. P. 5. Available from:
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