Cost Analysis and Estimating: Tools and Techniques PDF
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Cost Analysis and Estimating Willis R. Greer, Jr. Daniel A. Nussbaum Editors Cost Analysis and Estimating 8 Tools and Techniques With 48 Figures Springer Science+Business Media, LLC Willis R. Greer, Jr. College of Business Administration University of Iowa Iowa City, IA 52242 USA Daniel A. Nussbaum Naval Center for Cost Analysis Washington, D.C. 20350-1100 USA Library of Congress Cataloging-in-Publication Data Greer, WiIIis R. Cost analysis and estimating : tools and techniques / Willis R. Greer. Jr., Daniel Nussbaum. p. cm. 1. Costs, Industrial-Estimates. 2. Manufacturing processes -Costs-Estimates. 3. Manufactures-Costs-Estimates. 1. Nussbaum, Daniel, 1943- II. Title. TS167.G74 1990 658.15'52-dc20 90-9783 Printed on acid-free paper. ©1990 Springer Science+Business Media New York Originally published by Springer-Verlag New York, Inc. in 1990 Softcover reprint of the hardcover Ist edition 1990 AII rights reserved. This work may not be translated or copied in whole or in par! without the written permission of the publisher Springer Science+Business Media, LLC, except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use of general descriptive names, trade names, trade marks, etc., in this publication, even if the former are not especially identified. is not to be taken as a sign that such names, as understood by the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone. Permission to photocopy for internal or personal use, or the internal or personal use of specific c1ients, is granted by Springer Science+Business Media, LLC for libraries registered with the Copyright C1earance Center (CCC). provided that the base fee of $0.00 per copy, plus $0.20 per page is paid directly to CCC. 21 Congress St., Salem, MA 01970. USA. Special requests should be addressed directly to Springer Science+Business Media, LLC. ISBN 0-387-97325-7/1990 $0.00 + $0.20 Camera-ready text prepared by the authors. 987654321 ISBN 978-1-4612-6976-2 ISBN 978-1-4612-0995-9 (eBook) DOI 10.1007/978-1-4612-0995-9 FOREWORD Changes in production processes reflect the technological advances permeat ing our products and services. U. S. industry is modernizing and automating. In parallel, direct labor is fading as the primary cost driver while engineering and technology related cost elements loom ever larger. Traditional, labor-based ap proaches to estimating costs are losing their relevance. Old methods require aug mentation with new estimating tools and techniques that capture the emerging environment. This volume represents one of many responses to this challenge by the cost analysis profession. The Institute of Cost Analysis (lCA) is dedicated to improving the effective ness of cost and price analysis and enhancing the professional competence of its members. We encourage and promote exchange of research findings and appli cations between the academic community and cost professionals in industry and government. The 1990 National Meeting in Los Angeles, jointly spo~sored by ICA and the National Estimating Society (NES), provides such a forum. Presen tations will focus on new and improved tools and techniques of cost analysis. This volume is the second in a series. The first was produced in conjunction with the 1989 National Meeting of ICA/NES in Washington, D.C. The articles in this volume, all refereed, were selected from about 100 submitted for presen tation at the Los Angeles meeting. On behalf of the cost professionals who will benefit from this volume, I want to thank those who brought it to us. Professor Willis R. Greer, Jr. and Dr. Daniel A. Nussbaum, the editors, dealt ably with an extremely tight schedule and gained commendable cooperation from the authors. Once again, we thank Pro fessor Thomas R. Gulledge who conceived the idea for the series and worked with the editors and authors to perpetuate it. Stephen J. Balut President Institute of Cost Analysis PREFACE The articles which appear in this volume could have been published in a vari ety of high quality, scholarly journals. Among them would have been operations research, information systems, economics, and defense systems journals. However, these authors chose to make these manuscripts available to us rather than the more conventional literature for laudable purpose. They recognized that the readers of this volume, the attendees of the ICA/NES Conference, and oth ers, would benefit by having their work assembled in one convenient reference manual. Accordingly, the reader is treated to a broad cross section of work with a single focus; to improve our ability to estimate and analyze the cost of defense. We, the editors, and the readers are deeply indebted to the authors, whose self less contributions made this volume possible. We are also deeply indebted to the following individuals, who served as refer ees for th~ complex task of reviewing manuscripts, some of which have been published here and others not. Steve Balut Bruce Miller Avijit Banerjee R. P. Mohanty Peter Beck Doug Moses Dan C. Boger Richard Nelson Steve Book Michael Peters J. D. Camm Dan Sheldon Sidhartha R. Das Mike Sovereign Ed Deane Philip Tsung Brian Flynn Karen Tyson Thurman Gardner Rsit Unal Thomas R. Gulledge V. Valdmanis John Honig H.D. Vinod Roland Kankey Ted Wallenius David Lamm James Weathersbee Schuyler C. Lawrence J . C. Westland Bin-Shan Lin Norman K. Womer Lewis A. Litteral CONTENTS Foreword.............................................................................. v Preface ................................................................................ VII I. Cost Estimating and Changing Technology Reestimating the Cost of Production in a Fuzzy Technological Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 H.D. Vinod and Parantap Basu Estimating and Explaining the Cost of High-Technology Systems....................................................................... 30 O. Douglas Moses II. Lot Sizing and Cost Control Allocated Cost Structures to Control Equipment Usage "Bottlenecks"............................................................... 67 J. Christopher Westland The Effects of Different Production Rate Measures and Cost Structures on Rate Adjustment Models................................. 82 Dan C. Boger and Shu S. Liao Production Lot Sizing in a Class of Batch Process Flow Shops ......................................................................... 99 Avijit Banerjee and Somkiat Eiamkanchanalai Ill. Schedule Estimating Schedule Estimating Relationships for Air-Launched Missiles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I 15 Bruce Harmon and Lisa Ward IV. Uncertainty in Cost Estimating A General Analytic Approach to System Cost Uncertainty Analysis.... . ... .... ...... . . .... . ....... .... ..... ....... .......... . . ......... . 161 P,aul R. Garvey The Incorporation of Uncertainty into Investment Evaluations..... 182 William Resnick x V. Quality Control Techniques Economic Design of Fraction Defective Control Charts: Simplification of the Multiple Assignable Causes Situation........ 203 Michael~H. Peters VI. Warranties and Repair Parts Costing An Active Decision Support System for Warranty Cost Estimation. . .. . . .. . .. . .. . . . . . .. . . . . . . . . . . . . . . .. . .. . . . .. . . . . . . . .. . . . . . . . . . . . . . . 221 Bin-Shan Lin Parametric CERs for Replenishment Repair Parts.................... 245 Richard A. Katz VII. Test and Evaluation Issues Maintaining the Capital Stock at DoD Test and Evaluation Sites ........................................................................... 283 Daniel B. Levine and K. M. Olver I. Cost Estimating and Changing Technology REESTIMATING THE COST OF PRODUCTION IN A FUZZY TECHNOLOGICAL ENVIRONMENT BY H. D. VINOD and PARANTAP BASU Economics Dept, Fordham University, New York, 10458 ABSTRACT Most profit maximizing firms are aware that the boundary of the feasible set of technological opportunities is fuzzy. The neoclassical theory of the firm assumes that the boundary of the feasible set of technological opportunities is well-defined. We use a spheroidal neighborhood of a neoclassical profit maximizing equilibrium as a suitable approximation of a fuzzy input demand correspondence. Using trigonometric functions this spheroidal neighborhood is parameterized and a fuzzy cost function is generated. We use Nerlove's (1963) famous electric utility data to estimate a generalized Leontief cost function, with and without inclusion of a fuzz factor. The concept of fuzz used here seems to be in accord wi th Popper's (1973) notion of "plastic control" and Simon's notion of "bounded rationality". For concreteness we plot a fuzzy isoquant for Nerlove's data. We also report numerical estimates of scale elasticity, marginal cost, Hessian matrix, etc . .L. Introduction The traditional neoclassical theory of the firm generally accepts that firms maximize their objective function in a precisely defined technological environment. It is usually assumed that the producer kriows without any vagueness the boundary of his feasible set of technological opportunities. Technological uncertainty is treated in terms of the producer's rational expectations about the objective probability distribution of the random shock driving the technology. Actually, all producing agents cannot be expected to be well versed in probability theory when they make their production and sales decisions. It is now recognized in the literature that the observed behavior of agents can be imprecise or fuzzy. The fundamental premise of fuzzy set theory arises from a value-loaded description of the environment. According to the Paretian notion, tech- 4 nological alternatives are either "efficient," meaning that they are on. the boundary of the feasible set, or "inefficient," which means that they are inside the production possibility set. In practice, a company manager is likely to perceive varying degrees of technological efficiency (e .g., very efficient, Jess efficient, not so efficient etc.). In this context, an application of fuzzy set theory is potentially useful. In the literature, this issue has been slowly becoming prominent. Lester (1947) drew attention to the inadequacy of marginalism to deal with this kind of situation and argued that the equilibrium is a vague zone rather than a well defined point. Hirshleifer and Riley (1979, 1981) recognize that this type of fuzzy uncertainty about an agent's environment may have possible implications for his decision. There are several attempts to formalize the fuzzy nature of a producer's behavior (see, Blin et al.(1974), Chang (1977), Ponsard (1982), Taranu (1977)). Chen and Yu (1988) derive some useful properties of the fuzzy production corres pondence and show how the deterministic analysis of production generalizes in a fuzzy environment. Following Savage's focal decision theory, McCain (1987) introduces a notion of fuzzy confidence interval and applies it to price theory. There may be other attempts to formalize theore tically a fuzzy production environment in the vast literature on the subject. The measurement of fuzz in technology is important because it has definite implications for the estimation of a firm's cost function. In this paper, we suggest a new methodology for estimating cost and input demand functions in a technological environ ment with fuzz. We approximate an input correspondence with fuzz in terms of an ellipsoidal neighborhood around a standard neoclassical profit maximizing equilibrium. Using trigonometric functions we first obtain a parametric form for the input correspondence. In the next step, we derive a firm's cost function using these parametric forms. Applying the duality theory, we illustrate that this cost function with fuzz can be used to generate a fuzz technology of empirical interest. Using Nerlove's (1963) famous electric utility data, we finally estimate a generalized Leontief cost function incorporating the fuzz factor. As one might expect, it turns out that the neoclassical non-fuzzy (we call it crisp) equilibrium is a special case of our cost function. Statistical tests are conducted to verify the observed importance of the proposed fuzz in the technology. The paper is organized as follows. In Section 2, we review
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