Introduction

Impact Statement

The introduction of new technologies into heavily regulated industries such as aerospace is often a very complex, time-consuming and expensive challenge that requires significant levels of research and development in order to ensure a successful technology substitution. This challenge is exacerbated when new technology options represent a fundamental shift away from well-established principles, as the risk and uncertainties involved increase significantly. This is currently the case in the anticipated transition from conventional turbojet aircraft architectures to all new electric configurations, and equally for the adoption of technologies enabling mass manufacturing and customisation processes in aerospace production lines. At the same time, the opportunities associated with these disruptive innovations may be sufficient to warrant decision-makers adopting new technological paradigms. In some cases, new technological paradigms arise even while existing technological paradigms are still undergoing further developments, and have not yet reached the peak of their performance. This further complicates the decision for enterprises, as switching to a new technological baseline that may or may not out-perform the old one presents great commercial risk. In this regard it is beneficial to be able to identify early on whether a new technological paradigm is likely to have scope for development beyond that of the current dominant technology, and commercially, when the tipping point might occur where the new paradigm would become the industry ‘mainstream’ technology option.
The growth opportunities presented by the aerospace sector are also significant, and present another form of risk: Airbus Global Market Forecast identifies the need for almost 20,000 new single aisle aircraft alone by 2030 [1]. This is derived from replacement of existing aircraft but also new aircraft to support traffic growth in both established and emerging markets. During the same timeframe, fleet growth is also expected in the twin-aisle and large aircraft markets, bringing the expected value of market growth to be more than $4 trillion [1].
The size and complexity of the market means that success in securing these new aircraft sales will depend on being able to deliver a product that meets the needs of a wide range of ATS stakeholders, each with different strategic goals. Based on the significant growth trends expected, these forecasts illustrate how there is much to gain in the aviation industry, but equally, much to lose for incumbent firms. As a result, within technology and product development decision-making processes significant emphasis is often now put on risk management practices in order to minimise a firm’s exposure to both technology and market volatility. In the case of disruptive innovations it may not be immediately apparent what the risks and benefits of this new technology could be without having a comparable example that already exists to gauge it against. Typically risk is quantified as the financial investment, time, and effort required for the development of the new technology that is lost if the project is not successful, whilst the benefits will most likely be defined via existing 'legacy' performance metrics (at least initially). In aviation, weight and fuel burn have conventionally been the principal design drivers for technology performance assessment, but in recent years alternative metrics that reflect a direct impact on product/service attractiveness to customers (such as the aircraft’s impact on airport capacity constraints) have started to become increasingly important. Whereas the metrics used to characterise the benefits of a disruptive technology may themselves evolve over time, the performance expectations that drive eventual adoption of disruptive technologies are often rooted in known scientific principles. Consequently, studies of historical adoption patterns of disruptive technologies driven by scientific, rather than commercial, expectations may provide a better insight into how future disruptive innovations will evolve over time in these cases, along with observed market responses. Disruptive innovations also present a greater magnitude of benefit and/or risks in comparison to incremental development strategies, arising from the gamble of being the ‘first-mover’ in a new technological field. On the one hand, the sunk cost of prototyping, development, and industrialisation learning curves associated with pursuing a disruptive technology will no doubt be steep, but the rewards for being the first to capture the market can equally be of great significance. Conversely, adopting a low cost, low technical risk, incremental development strategy can actually turn out in some cases to be higher risk than adopting a new technological paradigm if the incremental technologies developed are easily replicated, or even leap-frogged, by competitors. In the worst case, companies may commit extensive resources to technologies and strategies that may be obsolete by the time they come to market. Ultimately, the increasingly complex nature of the Air Transportation System (ATS) 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. 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].
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].
The models developed in this study are intended to form part of a product/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/ahead of the expected time, consequently guiding technology substitution.
To date, analysis of existing literature and technology forecasting techniques, coupled with statistical and functional data analysis of historical patent data, has structured:
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 technological substitution to arise
3)    an agent-based/system dynamics simulation framework for assessing the impact of disruptive innovations
Combined, these elements provide a means to predict market receptivity and support technology strategy and innovation management. 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/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/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.