Paul Collopy

Aerospace System Value Models: A Survey and Observations

Nomenclature ai = attribute i of a prospect A, B, etc. = prospects γ = ratio of risk tolerance to total assets, when this ratio is constant pj = the probability of prospect j p = the lottery probability at which the preference between A and the lottery switches r = discount rate (per year) ρ = risk tolerance (

ECONOMIC-BASED DISTRIBUTED OPTIMAL DESIGN

) R = the set of real numbers S = total assets (

ADVERSE IMPACT OF EXTENSIVE ATTRIBUTE REQUIREMENTS ON THE DESIGN OF COMPLEX SYSTEMS

) t = time (years into the future, that is, future year minus present year) σ = standard deviation of the probability distribution of a random number θ = risk premium (

Application of value-driven design to commercial aeroengine systems

) u = utility v = value w = worth (

A Research Agenda for the Coming Renaissance in Systems Engineering

)

Surplus value in propulsion system design optimization

Spacecraft * and launch systems are examples of complex products which require a careful balance between competing concerns, such as performance, weight, and reliability, to serve their mission. Complexity requires design by large engineering organizations, so this balance must be achieved across many teams of people working on various components. This paper uses optimization theory to derive a method for distributed optimal design. Each component design team is provided with a separate optimization problem such that, as each team finds the best design solution to their problem, the teams together design the best system. To date, distributed optimal design has been difficult because complex system design spaces have extremely high dimensions over which design objectives are poorly correlated. Instead, the paper proposes that design objectives be expressed as functions in property spaces, which have few dimensions and are much smoother than design spaces. Property spaces are generated from design spaces by traditional engineering analysis processes. Economic analysis of all parties to the spacecraft launch and operation is used to construct a top-level value function on the system property space. This function is linearly decomposed into value functions for component property spaces. This provides the needed objective functions for distributed optimal design.

The Distinct and Interrelated Roles of Value-Driven Design, Multidisciplinary Design Optimization, and Decision Analysis

Extensive attributes are defined as attributes of a complex system where the value of the attribute at the system level is a function of the values at the component level. Examples are weight and cost. Assigning system requirements to extensive attributes and allocating these requirements to components causes a significant loss in each attribute, ultimately resulting in cost growth of tens of percent and major schedule delays in the detailed design phase. The alternative is to provide design guidance in the form of objective functions for extensive attributes, and flow the objective functions down from the system level to the component level.

VALUE MODELING FOR TECHNOLOGY EVALUATION

Value-Driven Design provides a framework to enhance the systems engineering processes for the design of large systems. By employing economics in decision making, Value-Driven Design enables rational decision making in terms of the optimum business and technical solution at every level of engineering design. This paper explains the application of ValueDriven Design to the aero-engine system through two case studies, which were conducted through workshops under the Rolls-Royce plc Advanced Cost Modeling Methodologies project. The Surplus Value Theory was utilized to provide a metric that can trade-off component designs with changes in continuous and discrete design variables. Illustrative results are presented to demonstrate how the methodology and modeling approach can be used to evaluate designs and select the value-enhancing solution.

The elephant in the mist: What we don't know about the design, development, test and management of complex systems

NASA and the National Science Foundation conducted three workshop during 2010 and 2011 addressing basic research needs in sy stems engineering. The workshop are described and the research needs are summarized. Essentially, these workshops pressed for a shift from a focus on proce ss to a focus on product, largely through the lens of decision. How does each engine ering choice, whether it is made by a lead systems engineer or made by a design team deep within a large engineering organization, affect the resulting prod uct? If the point were to make an excellent product, how would systems engineering be different than it is today?

Value-Driven Design

An economic model is presented which relates engineering performance characteristics to product profitability for conceptual and preliminary design of propulsion systems and aircraft. The model represents the competitive marketplace rigorously, which sets it apart from the prevailing state of the art. The model is shown to produce significantly different design results than current approaches. INTRODUCTION Today the design, certification and tooling costs to introduce a new aircraft engine can exceed one billion dollars. To make such an investment responsibly, design engineers must be focused on creating a product that will succeed in the marketplace, providing a good value for prospective customers [Collopy, 1997]. However, today’s design teams lack the economic tools to translate among engineering parameters, market needs, and costs. Therefore, new propulsion systems are based on goals such as “reduce aircraft total operating cost by 10%” [Aviation Week & Space Technology, 1996] that neglect airline revenue and engine manufacturing costs. Attempts to incorporate economic values, such as Altman [1994], although laudable, do not incorporate rigorous, complete representation of market operation and therefore do not generally lead to optimal designs. This paper presents an economic model that relates aircraft performance and manufacturing cost to aircraft, airline, and engine profitability. The model incorporates a rigorous representation of the aircraft market. It is shown that the total profit of the airframer, engine manufacturer, and purchasing airline, referred to as the surplus value, is set by the aircraft and engine design; the market functions only to divide the profit among the parties. Furthermore, it will be shown that the optimal design for airframe and engine is that design which maximizes surplus value. This same design maximizes profit for the airline, the airframer and the engine manufacturer—a true win-win solution. An example is provided in which the economic model is employed in a simplified design study to optimize bypass ratio. The results using the model are significantly different than with prior techniques. WHY AIRLINES PURCHASE AIRCRAFT Aircraft purchases are complex decisions. Salesmanship, maintenance agreements, manufacturing offsets and international politics all come into play. But virtually all purchases rest on a fundamentally rational financial base: the intent of the airline to make money by operating the aircraft. A simple economic model of the aircraft purchase decision can be made by assuming that operating profit is the airline’s only objective in purchasing an aircraft. The influence of this model on propulsion system design is instructive. Revenue and Operating Cost Airline operating profit is the difference between revenue and operating cost. The revenue that an aircraft can earn for an airline depends on the payload and range capability of the aircraft, in conjunction with airline’s route structure and the passenger and freight demand on those routes. Note in Figure 1 that payload and range are not separate attributes—the usual measure is in fact a function of maximum payload which depends on range. Economic probabilistic models can be used to estimate the