The airframe industry is usually recognized as being different from most manufacturing industries. These differences, which are characterized by the number of units produced and the frequency of design changes, have been evident for many years. This uniqueness and the corresponding implications for cost estimation became particularly evident during World War II. The aircraft industry generally has been considered unique in that it differs from other manufacturing in the quantity of units manufactured and with the frequency with which changes are made during the course of manufacturing operations. In mass-production industries, manufacturing thousands or hundreds of thousands of identical units, methods and cost of production tend to remain fairly constant after production has been stabilized, whereas in the aircraft industry, method improvements are constantly being made and cost is a variable depending on the number of airplanes being manufactured (Berghell, 1944). These differences, coupled with political considerations, place unusual demands on cost modelers.
This has been particularly true in recent years where large cost overruns have generated Congressional demands for better cost estimates. Traditionally, cost estimators in the airframe industry have used one or more of the following estimating techniques: 1. industrial engineering time standards, 2. parametric cost estimating models, 3. learning curves. All of the methods have been used with mixed results in specific situations. The general emphasis of all three approaches is cost estimation for planning purposes prior to beginning production, although some of the techniques may be used during the production phase of a program.
I Introduction.- Statement of the Learning Augmented Economic Planning Problem.- Scope and Methodology.- II Historical Perspective.- The Origins of the Learning Curve.- Linking the Progress Function with Economic Theory.- Early Solutions for the Learning Augmented Planning Problem.- III Recent Results in the Analysis of Made-to-Order Production.- General Theoretical Results.- Profit Maximization.- Cost Minimization.- Revenue Maximization.- Comments.- A Refinement of Alchian's Propositions.- A Basic Model for Learning Augmented Production Analysis.- The Basic Model with a Variable Delivery Schedule.- The Production Situation.- The Planning Situation.- A Constant Workforce Policy.- Summary.- The Importance of the Theoretical Framework.- IV Model Applications in the Airframe Industry.- Application to the C141 Airframe Program.- Production Cost Drivers.- The C141 Model.- Empirical Results.- Sensitivity Analyses.- Model Modifications and Extensions.- Sensitivity Analyses.- Application to the F4 Airframe Program.- Summary.- V Two Production Function Model.- The Model Formulation and Solution.- Strategy for Application.- Sensitivity Analyses.- Application to the F102 Airframe Program.- Estimation and Results.- Summary.- VI Discrete Dynamic Cost Models.- A Dynamic Programming Model for Made-to-Order Production.- Simulations with the Dynamic Programming Model.- A Second Dynamic Programming Model for Made-to-Order Production.- A Third Dynamic Programming Model for Made-to-Order Production.- Relationships Among the Three Models.- A Limiting Case.- A Transformation.- An Application of a Discrete Dynamic Model.- Summary.- VII Empirical Production Rate Assessment Models.- Estimating Cost Impacts.- Contractor Behavior.- Summary.- VIII Summary and Conclusions.
Series: Lecture Notes in Economic and Mathematical Systems
Number Of Pages: 138
Published: March 1986
Publisher: SPRINGER VERLAG GMBH
Country of Publication: DE
Dimensions (cm): 24.41 x 16.99
Weight (kg): 0.25