In this regard this study adapts existing literature and technology forecasting techniques, coupled with statistical and functional data analysis of historical patent data, in order to structure:
1) the formulation of a functional linear regression model that indicates the likely mode of adoption from key technology development indicators
2) the conditions required for presumptive and reactive technological substitutions to arise
3) a system dynamics simulation framework for assessing the impact of technological substitutions
Combined, these elements provide a means to predict market receptivity and support technology strategy and innovation management. Equally, this enables both quantitative (i.e. data-driven) measures of scientific development to be considered alongside more qualitative sociotechnical influences as part of wider technology development and market adoption processes. The capability to identify and test the sensitivity of the mode of substitution for a given technology will reduce uncertainty in decision-making processes by providing a clearer view of the risk of obsolescence of technology options and designs at the earliest conceptual stages. This in turn would enable a firm to identify the transition points where new products or upgrades should be phased in, based on the translation of expected performance characteristics into projected market share, with increased confidence. A more focused technology roadmap can then be implemented that offers a reduced time-to-market, whilst allowing product and service strategies to be developed that are more robust to rapidly shifting technological, market, and environmental conditions. This approach also enables a shift away from purely product-based development strategies, as being able to compare dissimilar technologies allows promising general-purpose technologies to be identified earlier on, which are ‘product-agnostic’ (e.g. technologies that are likely to be of value irrespective of which product they are applied to).
References:
[1] A. G. M. Forecast, “Forecast 20122031,” Full Book, retrieved, vol. 28, 2012.
[2] R. Lear, “Undesirable effects in design to manufacture,” Airbus S.A.S., Report, 2007.
[3] J. Roskam, “Airplane design, part i to viii. design,” Analysis and Research Corporation (DARcorporation), 2005.
[4] J. R. Carter III, “A business case for modeling and simulation,” DTIC Document, Report, 2001.
[5] Swenson, M., M. Languell, and J. Golden. "Modeling and simulation: The return on investment in materials science." IDC White Paper (2004): 1-24.
Introduction to technology forecasting
Forecasting techniques often used to determine strategies in large organisations by providing guide to future opportunities, risks, challenges, & areas of uncertainty
From 'Gauging credibility of simulated disruptions':
A common challenge faced by many disruptive technologies, innovations, and business models when first introduced into commercial markets is the assessment of the projected viability of the product or service being offered in uncertain future conditions. To this extent forecasts are often generated of projected market outcomes, increasingly based on computer-generated simulations of the world, in order to provide some guidance on the implications of predicted changes.
Forecasts are used in many different aspects of life: from predictions of changing weather patterns, to projections of a nation’s financial outlook, or to provide update warnings of traffic congestion to in-car satellite navigation systems as holiday-makers converge on popular destinations. Equally, computer-generated forecasts are increasingly used to represent the possible outcomes of disruptive changes and events that cannot be easily or safely reproduced through conventional experimentation procedures (such as simulating responses to natural disasters, and large-scale social disruptions).