Compare methods

	Trend Impact Analysis	Fisher-Pry Substitution Model
Field	Futures Foresight Studies	Futures Foresight Studies
Family	Process / pipeline	Process / pipeline
Year of origin	1972	1971
Originator	Theodore J. Gordon (The Futures Group / Millennium Project)	John C. Fisher & Robert H. Pry (General Electric)
Type	Probabilistic trend-extrapolation pipeline perturbed by future events	Logistic-growth forecasting pipeline for technological substitution
Seminal source	Gordon, T. J., & Hayward, H. (1968). Initial experiments with the cross-impact matrix method of forecasting. Futures, 1(2), 100-116.	Fisher, J. C., & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75-88.
Aliases	TIA, Trend-Impact Forecasting, Probabilistic Trend Perturbation, Event-Adjusted Trend Extrapolation	Fisher-Pry Model, Technological Substitution Model, Logistic Substitution Forecasting, Fisher-Pry Curve
Related	3	2
Summary	Trend impact analysis (TIA) is a forecasting method that marries quantitative extrapolation with expert judgment about disruptive future events. Developed by Theodore Gordon and colleagues at The Futures Group in the early 1970s and later codified in the Millennium Project's Futures Research Methodology, it starts from a 'surprise-free' baseline produced by fitting and projecting a historical time series. It then asks which unprecedented events — events with no historical analog that ordinary extrapolation cannot anticipate — could deflect that trend, and with what probability, magnitude, and timing. Through Monte Carlo simulation those probabilistic impacts perturb the baseline, yielding not a single line but a probability envelope that shows how the trend might bend if the unexpected occurs.	The Fisher-Pry Substitution Model, introduced by John Fisher and Robert Pry of General Electric in 1971, is a foundational technique for forecasting technological substitution — the process by which a new technology displaces an older one. Its empirical premise, supported by dozens of historical cases from synthetic to natural materials and from one manufacturing process to another, is that the fractional market share captured by the new technology follows a logistic (S-shaped) growth curve. The model's elegance lies in a transformation: when the takeover ratio f/(1-f), the ratio of the new technology's share to the old's, is plotted on a logarithmic scale against time, the substitution traces a straight line. This linearization makes it easy to fit, interpret, and extrapolate substitutions from sparse early data, which is why the Fisher-Pry curve remains a workhorse of technological forecasting.