Engineering topics
Publications and source records attributed to Dean, Edwin B..
Parametric cost analysis is a mathematical approach to estimating cost. Parametric cost analysis uses non-cost parameters, such as quality characteristics, to estimate the cost to bring forth, sustain, and retire a product. This paper reviews parametric cost analysis and shows how it can be used within the cost deployment process.
The concept of competitiveness is considered as a function of both high quality and low cost, and the techniques which drive a design simultaneously in both directions. In order to examine these techniques, the term 'genopersistation', which is derived from the words 'genesis' and 'persistence' and defined to mean the bringing forth, sustaining, and eventual annihilation of something. For a product, the term includes the conceptual design, design development, test and evaluation, production, deployment, operation, support, evolution, and retirement of product. The techniques are summarized which can be used to simultaneously genopersistate both the high quality and low cost into a product. Attention is given to Taguchi (1986) methods, the response surface methodology (Montgomery, 1984), Quality Function Deployment (Akao, 1990), Hoshin Kanari (Akao, 1991), activity based costing (O'Guin, 1991), concurrent engineering (Carter and Baker, 1991), and multidisciplinary optimization (Evans, 1984).
There is great concern for the competitiveness of the aerospace industry today. This paper examines the concept of competitiveness, introduces the concept of the genopersistation recursion, and summarizes a number of concepts which, when applied, seem to drive a product toward greater competitiveness.
There is great concern for the competitiveness of the aerospace industry today. This paper examines the concept of competitiveness by relating the levels of the genopersistation recursion to cost.
During recent history in the United States, government systems development has been performance driven. As a result, systems within a class have experienced exponentially increasing cost over time in fixed year dollars. Moreover, little emphasis has been placed on reducing cost. This paper defines designing for cost and presents several tools which, if used in the engineering process, offer the promise of reducing cost. Although other potential tools exist for designing for cost, this paper focuses on rules of thumb, quality function deployment, Taguchi methods, concurrent engineering, and activity-based costing. Each of these tools has been demonstrated to reduce cost if used within the engineering process.
During recent history in the United States, government systems development has been performance driven. As a result, systems within a class have experienced exponentially increasing cost over time in fixed year dollars. Moreover, little emphasis has been placed on reducing cost. This paper defines designing for cost and presents several tools which, if used in the engineering process, offer the promise of reducing cost. Although other potential tools exist for designing for cost, this paper focuses on rules of thumb, quality function deployment, Taguchi methods, concurrent engineering, and activity based costing. Each of these tools has been demonstrated to reduce cost if used within the engineering process.
For the purposes of this paper, program refers to a collection of activities or projects which must be performed according to a plan or schedule. The Space Exploration Initiative within the National Aeronautics and Space Administration (NASA) is an example. Dimensionality refers to both the various perspectives of a program and to the components within that perspective. It is, thus, appropriate to think of dimensions of dimensionality. For example, one dimension or perspective of a program is the projects which perform the program. Within the project dimension, the individual projects are the components of that dimensionality. The number of projects defines the spatial dimensionality of the project dimension. Thus, each perspective or dimension has a dimensionality of its own. The structure and associated values of all the various perspectives of a program define the program. A project refers to the collection of activities required to conceive, sell, design, develop, evaluate, produce, operate, support, evolve, and retire a given system. A project thus effects the life cycle of given system. A project is, thus, the system to conceive, sell, design, develop, evaluate, produce, operate, support, evolve, and retire a system. A program, thus, effects the life cycle of the collection of projects required to effect the collection of systems required to implement the program.
The geometric viewpoint identifies the choice of a correlation matrix for the simulation of cost risk with the pairwise choice of data vectors corresponding to the parameters used to obtain cost risk. The correlation coefficient is the cosine of the angle between the data vectors after translation to an origin at the mean and normalization for magnitude. Thus correlation is equivalent to expressing the data in terms of a non orthogonal basis. To understand the many resulting phenomena requires the use of the tensor concept of raising the index to transform the measured and observed covariant components into contravariant components before vector addition can be applied. The geometric viewpoint also demonstrates that correlation and covariance are geometric properties, as opposed to purely statistical properties, of the variates. Thus, variates from different distributions may be correlated, as desired, after selection from independent distributions. By determining the principal components of the correlation matrix, variates with the desired mean, magnitude, and correlation can be generated through linear transforms which include the eigenvalues and the eigenvectors of the correlation matrix. The conversion of the data to a non orthogonal basis uses a compound linear transformation which distorts or stretches the data space. Hence, the correlated data does not have the same properties as the uncorrelated data used to generate it. This phenomena is responsible for seemingly strange observations such as the fact that the marginal distributions of the correlated data can be quite different from the distributions used to generate the data. The joint effect of statistical distributions and correlation remains a fertile area for further research. In terms of application to cost estimating, the geometric approach demonstrates that the estimator must have data and must understand that data in order to properly choose the correlation matrix appropriate for a given estimate. There is a general feeling by employers and managers that the field of cost requires little technical or mathematical background. Contrary to that opinion, this paper demonstrates that a background in mathematics equivalent to that needed for typical engineering and scientific disciplines at the masters or doctorate level is appropriate within the field of cost risk.
Quality Function Deployment (QFD) is typically applied to small subsystems. This paper describes efforts to extend QFD to large scale systems. It links QFD to the system engineering process, the concurrent engineering process, the robust design process, and the costing process. The effect is to generate a tightly linked project management process of high dimensionality which flushes out issues early to provide a high quality, low cost, and, hence, competitive product. A pre-QFD matrix linking customers to customer desires is described.
Parametric cost analysis is presently defined as the generation and application of equations to cost analysis; equations defining cost as a function of system-design parameters, performance parameters, or engineering process parameters, constitute a valuable tool for design-to-cost efforts. Cost sensitivity is expressed through the partial derivatives of these functions, furnishing insight into the magnitude and direction of cost change for given parameter changes. The robust design method thus obtained is a systematic and efficient approach for determining the near-optimum set of design parameters for cost, which highlights important factors affecting operation and performance as well as cost.
A model is developed for simulating the dynamics of a newly formed organization, credible during all phases of organizational development. The model development process is broken down into the activities of determining the tasks required for parametric cost analysis (PCA), determining the skills required for each PCA task, determining the skills available in the applicant marketplace, determining the structure of the model, implementing the model, and testing it. The model, parameterized by the likelihood of job function transition, has demonstrated by the capability to represent the transition of personnel across functional boundaries within a parametric organization using a linear dynamical system, and the ability to predict required staffing profiles to meet functional needs at the desired time. The model can be extended by revisions of the state and transition structure to provide refinements in functional definition for the parametric and extended organization.
Designing for cost is a state of mind. Of course, a lot of technical knowledge is required and the use of appropriate tools will improve the process. Unfortunately, the extensive use of weight based cost estimating relationships has generated a perception in the aerospace community that the primary way to reduce cost is to reduce weight. Wrong! Based upon an approximation of an industry accepted formula, the PRICE H (tm) production-production equation, Dean demonstrated theoretically that the optimal trajectory for cost reduction is predominantly in the direction of system complexity reduction, not system weight reduction. Thus the phrase "keep it simple" is a primary state of mind required for reducing cost throughout the design process.
A primary issue in organizing a new parametric cost analysis function is to determine the skill mix and number of personnel required. The skill mix can be obtained by a functional decomposition of the tasks required within the organization and a matrixed correlation with educational or experience backgrounds. The number of personnel is a function of the skills required to cover all tasks, personnel skill background and cross training, the intensity of the workload for each task, migration through various tasks by personnel along a career path, personnel hiring limitations imposed by management and the applicant marketplace, personnel training limitations imposed by management and personnel capability, and the rate at which personnel leave the organization for whatever reason. Faced with the task of relating all of these organizational facets in order to grow a parametric cost analysis (PCA) organization from scratch, it was decided that a dynamic model was required in order to account for the obvious dynamics of the forming organization. The challenge was to create such a simple model which would be credible during all phases of organizational development. The model development process was broken down into the activities of determining the tasks required for PCA, determining the skills required for each PCA task, determining the skills available in the applicant marketplace, determining the structure of the dynamic model, implementing the dynamic model, and testing the dynamic model.
Computer integrated manufacturing (CIM) is a cohesive database of manufacturing information, providing an opportunity to track quality costs and measure progress toward their reduction. This paper presents the quality cost concept as an approach to identify, measure and reduce quality costs while improving quality within CIM. The effect of advanced failure prevention methodologies, such as continuous process improvement and the quality engineering methods of Taguchi, on quality and cost, is discussed.
This paper evaluates the use of Markov chain process in probabilistic life cycle cost analysis and suggests further uses of the process as a design aid tool. A methodology is developed for estimating operations and support cost and expected life for reusable space transportation systems. Application of the methodology is demonstrated for the case of a hypothetical space transportation vehicle. A sensitivity analysis is carried out to explore the effects of uncertainty in key model inputs.
A mathematical technique which both quantifies the design-to-cost process and the mass/complexity issue is presented. A simplified approximation of the PRICE H production-production cost is used to generate a dual set of differential equations which define the directions of maximum and minimum cost change over (mass, complexity) space. The equations are solved in closed form to obtain the one-dimensional design-to-cost and design-for-cost spaces. Preliminary results indicate that cost is relatively insensitive to changes in mass and that the reduction of complexity, both in the manufacturing process and in the spacecraft itself, is dominant in reducing cost. Two major objections to the design-to-cost method are discussed.
Calibrations to existing cost of doing business in space indicate that to establish human presence on the Moon and Mars with the Space Exploration Initiative (SEI) will require resources, felt by many, to be more than the national budget can afford. In order for SEI to succeed, we must actually design and build space systems at lower cost this time, even with tremendous increases in quality and performance requirements, such as extremely high reliability. This implies that both government and industry must change the way they do business. Therefore, new philosophy and technology must be employed to design and produce reliable, high quality space systems at low cost. In recognizing the need to reduce cost and improve quality and productivity, Department of Defense (DoD) and National Aeronautics and Space Administration (NASA) have initiated Total Quality Management (TQM). TQM is a revolutionary management strategy in quality assurance and cost reduction. TQM requires complete management commitment, employee involvement, and use of statistical tools. The quality engineering methods of Dr. Taguchi, employing design of experiments (DOE), is one of the most important statistical tools of TQM for designing high quality systems at reduced cost. Taguchi methods provide an efficient and systematic way to optimize designs for performance, quality, and cost. Taguchi methods have been used successfully in Japan and the United States in designing reliable, high quality products at low cost in such areas as automobiles and consumer electronics. However, these methods are just beginning to see application in the aerospace industry. The purpose of this paper is to present an overview of the Taguchi methods for improving quality and reducing cost, describe the current state of applications and its role in identifying cost sensitive design parameters.
Design-to-cost is a popular technique for controlling costs. Although qualitative techniques exist for implementing design to cost, quantitative methods are sparse. In the launch vehicle and spacecraft engineering process, the question whether to minimize mass is usually an issue. The lack of quantification in this issue leads to arguments on both sides. This paper presents a mathematical technique which both quantifies the design-to-cost process and the mass/complexity issue. Parametric cost analysis generates and applies mathematical formulas called cost estimating relationships. In their most common forms, they are continuous and differentiable. This property permits the application of the mathematics of differentiable manifolds. Although the terminology sounds formidable, the application of the techniques requires only a knowledge of linear algebra and ordinary differential equations, common subjects in undergraduate scientific and engineering curricula. When the cost c is expressed as a differentiable function of n system metrics, setting the cost c to be a constant generates an n-1 dimensional subspace of the space of system metrics such that any set of metric values in that space satisfies the constant design-to-cost criterion. This space is a differentiable manifold upon which all mathematical properties of a differentiable manifold may be applied. One important property is that an easily implemented system of ordinary differential equations exists which permits optimization of any function of the system metrics, mass for example, over the design-to-cost manifold. A dual set of equations defines the directions of maximum and minimum cost change. A simplified approximation of the PRICE H(TM) production-production cost is used to generate this set of differential equations over [mass, complexity] space. The equations are solved in closed form to obtain the one dimensional design-to-cost trade and design-for-cost spaces. Preliminary results indicate that cost is relatively insensitive to changes in mass and that the reduction of complexity, both in the manufacturing process and of the spacecraft, is dominant in reducing cost.