Ultimately, the increasingly complex nature of the Air Transportation System, and other Large Technological Systems (which include technical, economic, cultural, and organisational technology adoption influences), means data-driven models may be helpful in providing further understanding of the impact technologies will have on future market evolution. More specifically, the ability to recognise the mode of adoption early on in the development of a new technology based on historical patterns would provide a clearer view of the long-term commercial potential of that technology. In this regard, advances in data-driven feature recognition and behavioural modelling techniques over the past couple of decades may provide a means to make sense of some of the complex sociotechnical influences behind technological substitutions in LTS. As such, this study attempts to bridge the gap between technology performance expectations, technology development patterns, and market uptake in order to improve the robustness, and consequently likelihood of success, of technology development decisions whilst still in the conceptual stages of design.
The perils of a rapidly changing technology roadmap can be illustrated by considering analysis conducted internally within Airbus examining the impact of Undesirable Effects in Design to Manufacture (a survey across the UK business). This study revealed that consistently more time was wasted in re-planning of work than actually completing the necessary tasks using Airbus processes [2], and that much of this re-planning stemmed from the oscillation between conflicting business directions. This survey identified over 300 different Undesirable Effects (UDEs) in Design to Manufacture from 691 reported observations, and through a cause and effect analysis, identified 22 primary UDEs responsible for this situation (crucially these results and proposed solutions were ratified by members from all parts of the UK business). For wing development programs the most significant standalone undesirable event in design is perceived to cost upwards of £3 million to rectify. In addition the Airbus UDE survey identified that delays associated with immature tools and processes can account for over a year’s worth of additional effort, whilst time wasted gathering and reformatting data and reworking of components can cause delays of more than 5 and 6 months respectively. Instances of component re-work in particular are perceived to account for over £2 million of additional costs during this 6 month period. Finally, the survey also identified that the impact of not working to originally agreed plans can account for an additional 50% of required effort in compensation.
Perception of cost is as important as actual cost for decision-making purposes, as in most cases it is the perception of forecasted costs that is used as the basis of decision-making, rather than already known costs. The results of this analysis suggested that 5% of the UDEs observed cost more than £1,000,000, 6% cost more than £100,000, 12% cost more than £10,000, 16% cost more than £1,000, and 6% cost more than £100. Taking these minimum category values and expected frequency of UDE occurrences, this would consequently equate to over £250 million per year perceived as being spent on UDEs in UK wing design.
Previous historic studies have estimated that 65% of aircraft lifecycle costs (LCC) are effectively ’locked-in’ during the conceptual design stages, with 85% of LCC being ‘locked-in’ by the end of the preliminary design stage [3]. This may suggest that 65% percent of these annual UDE costs (i.e. over £160 million) are in some way connected to decisions taken during the conceptual design phase. This is particularly significant when you consider that many aircraft have lifespans of 30 to 40 years (or more) and will undergo numerous modifications during their operational life [4]. Consequently, this highlights the need to develop stable views on technology development trajectories at the earliest stage of design to minimise, to as large an extent as possible, these recurring costs arising later on from re-configuration of products, designs, tools, and processes.
Beyond this, in terms of the direct return on investment (ROI) of this study, the case for modelling and simulation is often hard to quantify (as the alternative cases of not undertaking modelling and simulation are not usually measured), however literature exists that goes some way to providing an insight. Pharmaceutical and material science firms, which face similar development programme overheads and timescales to aviation due to the equivalent level of certification and regulation in place, are quoted as receiving between $3 and $9 ROI for every $1 invested in modelling and simulation  [4, 5]. It is therefore believed that the models developed in this study, if not the end product in themselves, will still provide a benefit to decision-making that will enable similar improved efficiencies.

Research purpose

With this in mind, the overall purpose of this study can be stated as the development of a modelling framework to identify and test the sensitivity of the mode of substitution for emerging technologies in Large Technological Systems, such as the ATS. 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.

Research objectives

In order to meet this purpose, specific research objectives are outlined here that this study aims to fulfill, whilst the study hypotheses, research questions, boundaries, desired outcomes, and research strategy are explored in more detail in chapter 3:
  1. Identification of technology substitution patterns and characteristics in Large Technological Systems in relation to scientific & technological development efforts and other sociotechnical influences
  2. Identification of historical technology substitution case studies to determine the impact of different patterns of scientific & technological development efforts on global technology adoption trends
  3. Development of a technology classification model based on features extracted from historical datasets
  4. Construction and validation of a dynamic technology adoption modelling framework for use in conceptual level design based on identified substitution patterns and case studies
Consequently, the end focus of this study is on developing a modelling framework that is able use data-driven technological development patterns to reproduce observed substitution behaviours for a range of historical case studies and infer future trends.

Research outcomes

The main study hypothesis speculates that technology substitutions may follow one of two principal substitution modes, driven by performance expectations or relative scientific and technological development efforts, and that it will be possible to recognise these modes through the emergence of patterns in available technology datasets. 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.