Value-driven design (VDD) is a systems engineering strategy based on microeconomics which enables multidisciplinary design optimization. Value-driven design is being developed by the American Institute of Aeronautics and Astronautics, through a program committee of government, industry and academic representatives. [1] In parallel, the U.S. Defense Advanced Research Projects Agency has promulgated an identical strategy, calling it value-centric design, on the F6 Program. At this point, the terms value-driven design and value-centric design are interchangeable. The essence of these strategies is that design choices are made to maximize system value rather than to meet performance requirements.
This is also similar to the value-driven approach of agile software development where a project's stakeholders prioritise their high-level needs (or system features) based on the perceived business value each would deliver. [2]
Value-driven design is controversial because performance requirements are a central element of systems engineering. [3] However, value-driven design supporters claim that it can improve the development of large aerospace systems by reducing or eliminating cost overruns [4] which are a major problem, according to independent auditors. [5]
Value-driven design creates an environment that enables and encourages design optimization by providing designers with an objective function and eliminating those constraints which have been expressed as performance requirements. The objective function inputs all the important attributes of the system being designed, and outputs a score. The higher the score, the better the design. [6] Describing an early version of what is now called value-driven design, George Hazelrigg said, "The purpose of this framework is to enable the assessment of a value for every design option so that options can be rationally compared and a choice taken." [7] At the whole system level, the objective function which performs this assessment of value is called a "value model." [8] The value model distinguishes value-driven design from Multi-Attribute Utility Theory applied to design. [9] Whereas in Multi-Attribute Utility Theory, an objective function is constructed from stakeholder assessments, [10] value-driven design employs economic analysis to build a value model. [11] The basis for the value model is often an expression of profit for a business, but economic value models have also been developed for other organizations, such as government. [8]
To design a system, engineers first take system attributes that would traditionally be assigned performance requirements, like the range and fuel consumption of an aircraft, and build a system value model that uses all these attributes as inputs. Next, the conceptual design is optimized to maximize the output of the value model. Then, when the system is decomposed into components, an objective function for each component is derived from the system value model through a sensitivity analysis. [6]
A workshop exercise implementing value-driven design for a GPS satellite was conducted in 2006, and may serve as an example of the process. [12]
The dichotomy between designing to performance requirements versus objective functions was raised by Herbert Simon in an essay called "The Science of Design" in 1969. [13] Simon played both sides, saying that, ideally, engineered systems should be optimized according to an objective function, but realistically this is often too hard, so that attributes would need to be satisficed, which amounted to setting performance requirements. But he included optimization techniques in his recommended curriculum for engineers, and endorsed "utility theory and statistical decision theory as a logical framework for rational choice among given alternatives".
Utility theory was given most of its current mathematical formulation by von Neumann and Morgenstern, [14] but it was the economist Kenneth Arrow who proved the Expected Utility Theorem most broadly, which says in essence that, given a choice among a set of alternatives, one should choose the alternative that provides the greatest probabilistic expectation of utility, where utility is value adjusted for risk aversion. [15]
Ralph Keeney and Howard Raiffa extended utility theory in support of decision making, [10] and Keeney developed the idea of a value model to encapsulate the calculation of utility. [16] Keeney and Raiffa also used "attributes" to describe the inputs to an evaluation process or value model.
George Hazelrigg put engineering design, business plan analysis, and decision theory together for the first time in a framework in a paper written in 1995, which was published in 1998. [7] Meanwhile, Paul Collopy independently developed a similar framework in 1997, and Harry Cook developed the S-Model for incorporating product price and demand into a profit-based objective function for design decisions. [17]
The MIT Engineering Systems Division produced a series of papers from 2000 on, many co-authored by Daniel Hastings, in which many utility formulations were used to address various forms of uncertainty in making engineering design decisions. Saleh et al. [18] is a good example of this work.
The term value-driven design was coined by James Sturges at Lockheed Martin while he was organizing a workshop that would become the Value-Driven Design Program Committee at the American Institute of Aeronautics and Astronautics (AIAA) in 2006. [19] Meanwhile, value centric design was coined independently by Owen Brown and Paul Eremenko of DARPA in the Phase 1 Broad Agency Announcement for the DARPA F6 satellite design program in 2007. [20] Castagne et al. [21] provides an example where value-driven design was used to design fuselage panels for a regional jet.
Implementation of value-driven design on large government systems, such as NASA or European Space Agency spacecraft or weapon systems, will require a government acquisition system that directs or incentivizes the contractor to employ a value model. [22] Such a system is proposed in some detail in an essay by Michael Lippitz, Sean O'Keefe, and John White. [23] They suggest that "A program office can offer a contract in which price is a function of value", where the function is derived from a value model. The price function is structured so that, in optimizing the product design in accordance with the value model, the contractor will maximize its own profit. They call this system Value Based Acquisition.
Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.
Value engineering (VE) is a systematic analysis of the functions of various components and materials to lower the cost of goods, products and services with a tolerable loss of performance or functionality. Value, as defined, is the ratio of function to cost. Value can therefore be manipulated by either improving the function or reducing the cost. It is a primary tenet of value engineering that basic functions be preserved and not be reduced as a consequence of pursuing value improvements. The term "value management" is sometimes used as a synonym of "value engineering", and both promote the planning and delivery of projects with improved performance
In mathematical optimization and decision theory, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its opposite, in which case it is to be maximized. The loss function could include terms from several levels of the hierarchy.
In product development and process optimization, a requirement is a singular documented physical or functional need that a particular design, product or process aims to satisfy. It is commonly used in a formal sense in engineering design, including for example in systems engineering, software engineering, or enterprise engineering. It is a broad concept that could speak to any necessary function, attribute, capability, characteristic, or quality of a system for it to have value and utility to a customer, organization, internal user, or other stakeholder. Requirements can come with different levels of specificity; for example, a requirement specification or requirement "spec" refers to an explicit, highly objective/clear requirement to be satisfied by a material, design, product, or service.
Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO), and Multidisciplinary Design Analysis and Optimization (MDAO).
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System of systems is a collection of task-oriented or dedicated systems that pool their resources and capabilities together to create a new, more complex system which offers more functionality and performance than simply the sum of the constituent systems. Currently, systems of systems is a critical research discipline for which frames of reference, thought processes, quantitative analysis, tools, and design methods are incomplete. The methodology for defining, abstracting, modeling, and analyzing system of systems problems is typically referred to as system of systems engineering.
Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making. Conflicting criteria are typical in evaluating options: cost or price is usually one of the main criteria, and some measure of quality is typically another criterion, easily in conflict with the cost. In purchasing a car, cost, comfort, safety, and fuel economy may be some of the main criteria we consider – it is unusual that the cheapest car is the most comfortable and the safest one. In portfolio management, managers are interested in getting high returns while simultaneously reducing risks; however, the stocks that have the potential of bringing high returns typically carry high risk of losing money. In a service industry, customer satisfaction and the cost of providing service are fundamental conflicting criteria.
Decision analysis (DA) is the discipline comprising the philosophy, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing important aspects of a decision; for prescribing a recommended course of action by applying the maximum expected-utility axiom to a well-formed representation of the decision; and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker, and other corporate and non-corporate stakeholders.
Howard Raiffa was an American academic who was the Frank P. Ramsey Professor (Emeritus) of Managerial Economics, a joint chair held by the Business School and Harvard Kennedy School at Harvard University. He was an influential Bayesian decision theorist and pioneer in the field of decision analysis, with works in statistical decision theory, game theory, behavioral decision theory, risk analysis, and negotiation analysis. He helped found and was the first director of the International Institute for Applied Systems Analysis.
The term demand chain has been used in a business and management context as contrasting terminology alongside, or in place of, "supply chain". Madhani suggests that the demand chain "comprises all the demand processes necessary to understand, create, and stimulate customer demand". Cranfield School of Management academic Martin Christopher has suggested that "ideally the supply chain should become a demand chain", explaining that ideally all product logistics and processing should occur "in response to a known customer requirement".
Multi-objective optimization or Pareto optimization is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.
In decision theory, the evidential reasoning approach (ER) is a generic evidence-based multi-criteria decision analysis (MCDA) approach for dealing with problems having both quantitative and qualitative criteria under various uncertainties including ignorance and randomness. It has been used to support various decision analysis, assessment and evaluation activities such as environmental impact assessment and organizational self-assessment based on a range of quality models.
DIDO is a MATLAB optimal control toolbox for solving general-purpose optimal control problems. It is widely used in academia, industry, and NASA. Hailed as a breakthrough software, DIDO is based on the pseudospectral optimal control theory of Ross and Fahroo. The latest enhancements to DIDO are described in Ross.
A view model or viewpoints framework in systems engineering, software engineering, and enterprise engineering is a framework which defines a coherent set of views to be used in the construction of a system architecture, software architecture, or enterprise architecture. A view is a representation of the whole system from the perspective of a related set of concerns.
SDI Tools is a set of commercial software add-in tools for Microsoft Excel developed and distributed by Statistical Design Institute, LLC., a privately owned company located in Texas, United States.
Management accounting principles (MAP) were developed to serve the core needs of internal management to improve decision support objectives, internal business processes, resource application, customer value, and capacity utilization needed to achieve corporate goals in an optimal manner. Another term often used for management accounting principles for these purposes is managerial costing principles. The two management accounting principles are:
Daniele Mortari is Professor of Aerospace Engineering at Texas A&M University and Chief Scientist for Space for Texas A&M ASTRO Center. Mortari is known for inventing the Flower Constellations and the k-vector range searching technique and the Theory of Functional Connections.
Design optimization is an engineering design methodology using a mathematical formulation of a design problem to support selection of the optimal design among many alternatives. Design optimization involves the following stages:
The value of structural health information is the expected utility gain of a built environment system by information provided by structural health monitoring (SHM). The quantification of the value of structural health information is based on decision analysis adapted to built environment engineering. The value of structural health information can be significant for the risk and integrity management of built environment systems.
