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Identifying the Best Design for Uncertain Markets The University of Manchester Research Identifying the Best Design for Uncertain Markets Document Version Accepted author manuscript Link to publication record in Manchester Research Explorer Citation for published version (APA): Desai, A., Hollingsworth, P., & Chinchapatnam, P. (2016). Identifying the Best Design for Uncertain Markets. In 30th Congress of the International Council of the Aeronautical Sciences Published in: 30th Congress of the International Council of the Aeronautical Sciences Citing this paper Please note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscript or Proof version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version. General rights Copyright and moral rights for the publications made accessible in the Research Explorer are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Takedown policy If you believe that this document breaches copyright please refer to the University of Manchester’s Takedown Procedures [http://man.ac.uk/04Y6Bo] or contact [email protected] providing relevant details, so we can investigate your claim. Download date:04. Oct. 2021 IDENTIFYING THE BEST DESIGN FOR UNCERTAIN MARKETS Abdullah Desai*, Peter Hollingsworth*, Phani Chinchapatnam** * University of Manchester, Manchester, England M13 9PL, United Kingdom ** Rolls-Royce plc, Derby, England DE24 8BJ, United Kingdom Keywords: Aviation, Aircraft Design, Value Driven Design Abstract Therefore, industry has showed an interest in This paper demonstrates the development and Value Driven Design (VDD) [4] as an alternative operation of a basic value-driven design or supplementary procedure for preliminary and framework to help identify the best airframe and detailed design. VDD goes beyond the limits of engine options for non-traditional niche markets. SE by replacing the requirements environment, Using data collected by The Bureau of and incorporates a system level value function Transportation Statistics, the size and scope of known as Surplus Value (SV). SV relates aircraft unfulfilled markets can be determined. Using the performance and manufacturing cost to aircraft, methodology, the user can evaluate airline, airline, and engine profitability [3]. airframe and engine combinations for the best A VDD research agenda [5] identifies five value solution. Value model optimisation can main areas of challenges: the system, the take place within each system, subcomponent stakeholders, the value function, finding the best and component model and keeping all things value and identifying the enablers. The research equal how one (or a set of) variable(s) would proposed herein will attempt to address issues affect the overall design solution. Using the associated with the SV method and develop a models, a number of Middle of the Market methodology to include competition within the example cases were investigated, demonstrating supply chain, manufacturers and airlines the principle of the approach. While the modeling portfolio [6]. is incomplete the initial results highlight the challenges of selecting the proper configuration 2 Aim for uncertain, non-traditional markets. Within the initial design stages, information and requirements are uncertain and susceptible to change with design maturity. However, using an 1 Introduction enhanced Value Driven Design approach would Design, certification and tooling costs to provide a method for engineers and designers to introduce any new aircraft with new engines can rank different design options to find the best exceed one billion dollars [1]. For aircraft solution, for the manufacturers themselves and manufactures to make such an investment the operators. responsibly, engineers must focus on creating a This methodology will provide a detailed product that will succeed [2]. Common aircraft relationship of the value split amongst the manufacturer design process utilises Systems operators and manufactures, including the effect Engineering (SE) and Multidisciplinary Design of changing unit profit and/or the manufacturing Optimisation (MDO). cost of either the aircraft or engine. In other Using MDO and SE, design teams lack the words, creating a design space for the product economic tools to translate engineering through relationships between model parameters parameters, market needs, and costs [3]. rather than a flow down of set requirements. 1 DESAI, HOLLINGSWORTH, CHINCHAPATNAM 3.1 Capturing the Potential Market The mechanism for distributing value is The market for a proposed commercial competition, and markets have a powerful effect aviation system ultimately comes from providing on the allocation of profit [7]. Therefore, the a transportation service, either people or cargo. optimisation will enable to find the best split of However, in most cases for commercial aircraft, value to maximise the profit for the airline, the it is possible to view the market as the aircraft airframe and engine manufacturer, and their operators; the airlines. In order to develop a value suppliers. Ultimately minimising the likelihood model for these systems it is necessary to of deadweight loss decision-making. understand the current state of the travel market The competition model will incorporate a and the potential path of the future. In order to number of airline models, supplied by multiple develop this for the case presented in this paper a airframes with a selection of engine options to model was developed using publicly available simulate a simple scenario of competition. Using airline industry data: The U.S. Department of this methodology, the user can investigate and Transportation Form 41 (U.S. DOT Form 41) play out the effects of a competitive environment from the Bureau of Transportation Statistics with uncertainty of market requirements. (BTS) [8], and relevant filings to the Securities and Exchange Commission (SEC). 3 Value Driven Design Process The U.S. DOT Form 41 from BTS is used to Figure 1 demonstrates where potential create important trends and generate forecasts for design parameters are included and how a SV analysis. The data provides key parameters to figure is found. The simulation feeds through the identify the drivers for different types of costs subcomponent and component models, to and identify potential revenues specific to generate a specific aircraft model for a particular aircraft types and airlines. For the purposes of airline/traffic demand. The attributed value from this research the payload and range are the key each model is combined into the product value variables for analysis. The long time series of the model. Optimisation can take place within each data, starting in 1995, provides a great breadth of component to find the best solution within each information to allow investigations over time for model or investigate keeping all things equal how specific routes, identifying aircraft types that one, or a set of, variable(s) would affect the service it and payload demand over time. overall product design. VALUE MODEL​ AIRLINE AIRCRAFT COMPONENT SUB-COMPONENT (Objective Function)​ MODEL MODEL MODEL MODEL Development​ Cost​ Maintenance Capacity Engine Fan Manufacturing Cost​ Taxes​ Training Range Combustor Operations (Utilisation, ​ Fees Weight Gear Turbine revenue per flight, ​ cost per flight)​ ​ Capacity Wing Area Fuselage Compressor Economic Factors​ (Discount rates, ​ Compatibility Length Empennage Nacelle Market Size)​ ATTRIBUTE ATTRIBUTE PRODUCT ATTRIBUTE ATTRIBUTE VALUE​ VALUE​ VALUE​ VALUE​ VALUE​ ​ ​ ​ ​ ​ (e.g Turn (e.g Time to (Surplus​ Value)​ (e.g Noise)​ (e.g Fuel Burn)​ Around Time)​ Construct)​ Design Changes Design Fig. 1. Value Driven Design Process 2 IDENTIFYING THE BEST DESIGN FOR UNCERTAIN MARKETS 3.2 Aircraft Models The ticket price is produced as a function of In order to estimate the value of a new stage length; the total aircraft revenue would be aircraft programme, especially one that does not multiplied by the number of seats available. neatly fit as a replacement, it is necessary to have However, the weight of the total passengers a representation of the potential competing including baggage would typically be under the models. To do this an initial sizing of 24 different maximum payload capacity, therefore the aircraft models was created using NASA’s Flight remainder would be assumed as carrying cargo. Optimization System (FLOPS) [9]. This includes a representation of both current generation and 3.4 Cost Model newer generation aircraft families; e.g. Boeing The cost data within BTS is much like the 737, 757, 767 and 787; Airbus A320, A321, revenue section, which examines the system A330 families [10, 11, 12, 13, 14, 15, 16, 17, 18, performance of each airline and aircraft type. 19, 20, 21]. Each relevant metric has been adjusted for stage- For the known competitors, a simple length to include Direct Operating Costs and regression model is created to find a statistical Indirect Operating Costs. The main drivers of relationship between the payload and range cost include labour, fuel and maintenance. capabilities of the aircraft. While also estimating Using FLOPS to prescribe a mission, the the block time and fuel required for each flight. following data can be used to generate a typical For any new advance technology model a more operating and maintenance cost for
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