The models developed in this study are intended to form part of a product and technology lifecycle management toolset, enabling dissimilar technology options (potentially at different stages of development) to be evaluated versus performance expectations and anticipated market responses. Hypothetical product and/or technology ‘roadmaps’ could then be tested against market responses, in order to inform the systematic targeting of funding to bring technology performance to the required levels at or ahead of the expected time, consequently guiding technology substitution.
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.

Thesis reader profiles and possible interests

Purpose

6 month report:
The purpose of this project is to develop a framework methodology that will enable the evolving values and beliefs of global ATS stakeholders to be determined from their dynamic environmental relationships (including influences due to regional level socio-political characteristics outside of the direct ATS), as well as capturing stakeholders ability to take intuitive action not necessarily prescribed by previous learning (often represented by disruptive innovations within the ATS).
Current revised purpose:

Objectives

3rd year report:
The objectives of the individual research project have been adapted from those outlined in the previous progress reports; specific objectives for the individual research project are provided here only, taking into account agreed research partner contributions:
a)           Development of disruptive case studies to identify and understand impact of scientific & technological effects perceived by different community groups on global technology adoption trends: This will be a primary objective of the individual Eng-D research project
b)           Development of a static technology adoption model (i.e. stakeholders have fixed behavioural laws applied) for conceptual level strategic analysis: Individual research project focus here will be on translating and implementing stakeholder behavioural patterns into micro-level behavioural models that produce observed aggregate-level technology diffusion behaviours
c)           Development of a dynamic technology adoption model (i.e. including stakeholder learning behaviours and intuition) for conceptual level strategic analysis: Individual research project focus here will be on the modelling of intuitive and presumptive behaviours in stakeholders based on future scientific and technological constraints, to identify any influences on aggregate-level substitution of disruptive technologies
d)           Implementation of disruptive case study representation(s) in SoS, and examination of predicted evolution of technology adoption trends, stakeholders values, beliefs, and strategies: This will be a primary objective of the individual Eng-D research project
A breakdown of the basic project structure is provided in Appendix 1 of the first year progress report that illustrates the tasks allocated to each partner in the wider research consortium, and the key themes behind each area of investigation.

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).