Compara mètodes

	Relevance Tree Analysis	Gompertz Substitution Forecasting
Camp	Futures Foresight Studies	Futures Foresight Studies
Família	Process / pipeline	Process / pipeline
Any d'origen	1966	1971
Autor original	Defense and aerospace planners (Honeywell's PATTERN program); systematized in futures research by Gordon, Helmer and colleagues	Benjamin Gompertz (curve); growth-curve technology forecasters (Lenz, Martino) and the Fisher-Pry tradition
Tipus	Normative hierarchical decomposition pipeline for R&D priority-setting	Growth-curve diffusion pipeline for technology adoption and substitution
Font seminal	Glenn, J. C., & Gordon, T. J. (Eds.). (2009). Futures Research Methodology, Version 3.0. The Millennium Project. ISBN: 9780981894119	Fisher, J. C., & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75-88.
Àlies	Relevance Tree Method, Relevance Number Analysis, Normative Relevance Tree, PATTERN-Style Relevance Trees	Gompertz Diffusion Forecasting, Gompertz Growth-Curve Forecasting, Asymmetric S-Curve Technology Forecasting, Gompertz Adoption Model
Relacionats	3	3
Resum	Relevance tree analysis is a normative forecasting method that decomposes a high-level objective into a hierarchy of sub-objectives, functions, and contributing technologies, and then assigns relevance numbers that quantify how much each branch contributes to its parent. By normalizing these numbers so that the children of every node sum to one and multiplying them down each path, the method produces an overall relevance score for every technology or task at the leaves, which ranks them by their importance to the top objective. Unlike exploratory forecasting, which projects what the future will be, relevance trees work backward from a desired goal — they are 'normative,' starting from where you want to go and identifying what must be developed to get there. Originating in defense and aerospace planning and codified in Glenn and Gordon's Futures Research Methodology, the technique remains a standard tool for research-and-development priority-setting and mission analysis.	Gompertz substitution forecasting projects the adoption, diffusion, or substitution of a technology by fitting the asymmetric Gompertz growth curve to historical data and extrapolating it toward a saturation ceiling. Like the symmetric logistic used in the Fisher-Pry substitution model, the Gompertz curve captures the characteristic S-shape of technological change — slow initial uptake, rapid mid-life growth, and tapering as the market saturates — but unlike the logistic it is asymmetric, reaching its fastest growth early, at roughly 37 percent of the ceiling rather than at the midpoint. This makes it a natural choice when a new technology accelerates quickly and then approaches its limit gradually. Within the futures and foresight toolkit catalogued by Glenn and Gordon, growth-curve forecasting of this kind is a core quantitative method for anticipating when a technology will mature and when a successor is likely to displace it.