A Multi-technology Forecasting Tool

Heuristic models like Moore’s Law have served the technology sector for many years, but a paper by Gareth James and Gerard Tellis, professors at the USC Marshall School of Business and their co-authors Ashish Sood, at Emory and Ji Zhu at the University of Michigan have developed a new model for thinking about technological developments that can be applied to several technologies. The authors looked at 26 technologies in six markets from lighting to automobile batteries and found their model, Step and Wait (SAW), to be more accurate and effective in all six markets than competing models.

The sweet spot is in knowing which technology to back based on predicting when a new technology is going to have a jump in performance. What Tellis and his colleagues came up with, are average performance improvements for the industry in terms of “steps” and wait times. The challenge for strategists is to invest in various technologies to beat these averages. (Taken from the press release)

The authors say Moore’s Law, Kryder’s Law, etc. are overly generalized models drawn from a broad perspective. While they may provide a quick view at the potential evolution of a given technology, they do not effectively serve managers and investors in predicting the near term development of the technology.

As an example, consider the competition between LCD and CRT monitors (see Figure 1b). Sony kept investing in CRT even after LCD first crossed CRT in performance in 1996. Instead of considering LCD, Sony introduced the FD Trinitron/WEGA series, a flat version of the CRT. CRT crossed LCD for a few years, but ultimately lost decisively to LCD in 2001. In contrast, by backing LCD, Samsung grew to be the world’s largest manufacturer of LCD, while the former leader Sony had to seek a joint venture with Samsung in 2006 to manufacture LCD. Prediction of the next step size and wait time using SAW could have helped Sony’s managers make a timely investment in LCD technology.

display mon

The authors also say the older models depend too highly on either extrapolation or environmental scanning whereas SAW utilizes both. It is more flexible than other models as well by factoring for periods of little change followed by large steps or small periods with little changes approximating a smooth curve.

To download the whole paper and see why SAW is so effective: Predicting the Path of Technological Innovation: SAW Versus Moore, Bass, Gompertz, and Kryder.

 

 

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