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Design analysis through techniques of parametric cost estimation
Engineering Costs and Production Economics, 14 ( 1988) 87-93 Elsevier Science Publishers B.V., Amsterdam - Printed in The Netherlands
DESIGN ANALYSIS THROUGH TECHNIQUES PARAMETRIC COST ESTIMATION*
87
OF
J.M. Daschbach The University
of Toledeo,
Toledo, OH (U.S.A.)
and Henry Apgar Management
Consulting
& Research Inc., Sanra Barbara, CA (U.S.A.)
ABSTRACT
This paper focuses on the parametric model as tool for design engineering analysis. The technique is made relevant to routine, every-day living and proceeds to the engineering design function with a case study of over 4000 in-
stances where the parametric model provided estimates closer to actual costs than the traditional ‘bottom-ups’ means to cost estimation in an aerospace industrial application.
INTRODUCTION
at $250, one may lose the “clean teeth”, if the newspaper price is $450, one probably looks for something else to read. In such cases, costs so greatly exceed expectations that refusal to pay is an instinctive reaction. This reaction reflects use of a parametric model. A first level parametric model applies experience to analyze a cost. Questioning the reason for the cost is the second phase - examining the circumstances to determine why costs vary. There may be a key variable not accounted for in the model used which explains a cost. The same experience that assists cost prediction helps one arrive at possible explanations when costs seem out of line. In tracking down the reasons for cost variance, the objective as well as subjective factors are considered. Objective factors such as size, location, and materials effect cost and these are sometimes modified by less quantifiable, subjective fac-
Engineering design is no longer isolated from or independent of the manufacturing process or market competition pressures. The product design, development, production, and use phases are each subject to analysis of cost by customers, suppliers and users. Tools available at these analytical levels are fast becoming indispensable for product comparisons under unparalleled rules of competition. Such cost analysis tools are best represented by parametric methods. The parametric method is neither new nor complex. It’s an approach used every day by each of us. Each time one buys a toothbrush or a newspaper, there already exists an idea as to the price to be paid. If the toothbrush is priced This article is based on materials provided by the authors from Ref. [ 1 ] and [ 2 ] as modified by the stated authors above.
0167-188X/88/$03.50
0 1988 Elsevier Science Publishers B.V.
88 tors such as the competition, quality levels required, management skills available, and organizational objectives. In drawing on personal experience to predict costs or assess their reasonableness, and in examining variables to explain discrepancies, one follows intuition to the increasingly used parametric approach to cost estimating. Applying the above to the design engineer’s environment, no modern day product designer would risk acceptance of a preliminary design by ignoring the necessity for stress analysis or environmental analysis. Similar disregard of another of the available aids for design analysis - the parametric cost estimating model measn risking potentially disastrous material, manufacturing, quality or other cost pressures on the product’s use. The parametric analysis technique has become a universal design tool for assuring that a design not only meets performance specifications but also is cost effective and affordable. Many lead design engineers depend upon parametric modeling to assure a balanced satisfaction of the three supporting attributes of a good design - manufacturability, suitable quality, and minimal cost. The name for this innovative and effective tool comes from empirical relationships that exist between cost parameters (dollars or labor hours) and selected physical or performance parameters (product size, quality, complexity, and design maturity) as well as the product’s method of production. Such empirical relationships are kown as Cost Estimating Relationships or CERs. Parametric models, typically computerized and analyst interactive, reflect an aggregate of such CERs in functional relationshp formats. Engineering managers may recognize the parametric technique by another name - “top down estimating”. In their purest form, the parametric cost model’s primary variables or “drivers” originate from the same design factors determining the bottoms up or “grass roots” cost drivers. The primary purpose of any cost estimate, of course, is to enhance the ef-
fectiveness of managerial decision making, whether it be questions concerning make or buy, bid or no-bid, concept evaluation, contractor selection, or product planning. THE IMPORTANCE ESTIMATES
OF GOOD
An organization’s future often depends on the accuracy of its cost estimates. Both overestimates and underestimates can spell disaster. Frank Freiman, for many years the head of cost estimating for RCA and developer of the FAST cost-estimating system, graphically represents the over- and underestimates relationships in the Freiman curve ( [ 2 1, see Fig. 1). Succinctly stated, ( 1) the greater the underestimate, the greater the actual expenditure; (2) the greater the overestimate, the greater the actual expenditure; (3 ) the most realistic estimate results in the most economical project cost. Underestimates may land a contract or result in a project approval, but they also frequently lead to financial loss and business failure. Initial project plans of stafIing, scheduling, machine processing, tooling and mate-
Fig. 1. The Freiman curve.
89
rials’ forming, etc., are not achievable. Though the project plan is established to realize the underestimated cost, the project mid-point management begins to realize that milestones and schedules are slipping. In response, there is reorganization, replanning, and possibly the addition of personnel and equipment. Delays and reorganization invariably increase costs. The cost to the organization is also high in other ways, including poor morale and the loss of capable and trained staff. Projects that suffer significant cost growth are often projects scheduled, planned, and staffed based on early underestimates, that eventually lead to a detailed project plan that simply cannot be realized. Underestimates threaten an organization’s ability to survive. Overestimates serve an organization as poorly as the underestimate. Rather than resulting in greater profits, as one might hope, the overestimate reflects a Parkinson’s law application: the money is available, it must be spent. Unless there is firm management control, the estimate becomes a self fuljilling prophesy and the organization becomes weak, unable to deliver a good product for a reasonable price. Realistic estimates result in the most economical cost. They remind managers to control the excess resources. Good estimates let the orgnaization’s resources work in harmony. PROFESSIONAL COST ESTIMATING AND THE PARAMETRIC APPROACH
Professional cost estimating differs only in details from what the “man-on-the-street” does when he/she buys that toothbrush, newspaper or a home. Professionals base a cost forecast on various kinds of knowledge about the product and the circumstances under which it will be bought or produced and on experience with actual costs of similar items. The difference is that professionals - cost estimators, engineers, and managers - are dealing with larger sums of money and ,therefore, must strive for greater
precision than the “man-on-the-street”. To achieve greater precision, formalized and organized experiences are aggregated with those of others in their field and functional relationships are established. Many such formal techniques employ parametric concepts. Some relay so heavily on parametrics that they are labeled Parametric Models. All, however, rely to some degree on parametric principles. Ranging from simple to highly sophisticated, they all have one thing in common: the attaching of values to specific ratios or variables. In home building, for example, square footage is a primary cost parameter. Builders typically state costs in terms of dollars per square foot to build a home. Such statement is a CER and might be used as a rule of thumb in early cost estimating by both builder and home buyer. Such rules of thumb - cost per square foot, cost per pound, cost per horsepower - are easy to use, and yield in preliminary stages, rough estimates. Design, location, materials, year of construction, and labor productivity are other factors that significantly effect cost. Arriving at reasonable estimates involves compromise of designer needs with production/construction problems, balancing gradual improvement of estimating accuracy and escalation as the design approaches completion, and detailed bills of material that can be prepared against the typically fast increasing level of the estimate itself. Professional cost estimating, then, involves managers, engineers, designers, technical experts, and cost estimators in an iterative process responding to different questions at different phases of project or product during the life cycle. The first decision - whether to allocate resources necessary to prepare the bid or undertake more detail project analysis - is a big one because cost estimating and analysis consume time and money. As work proceeds, it is important to arrive at a reasonable cost estimate as soon as possible because - as Fig. 2 illustrates - decisions affecting about 70-80%
90 lQEI%
+
__--4 _+_----
I
saz
i
333
I
/-__..i
__.-
._c--
.j
Fig. 2. Cost impact of decisions.
of the total eventual costs are made in the concept formulation stage when only l-2% of the design work is complete. APPLYING DESIGN
THE COST PARAMETER
TO
The responsible lead designer begins with a perception of the product architecture, based on many factors including the customer’s specification, the company’s previous experience and the engineers’ design capability. Among many constraints, one must insure against the inherent nature of the design engineer to overdesign or to ignore cost-effectiveness. The lead designer’s solution, therefore, is to establish the simplest form of the product architecture and its manufacturing process. To do this, one must evaluate many design approaches and identify the optimum design approach. Evaluating candidate design approaches by comparing their relative development, production, or maintenance costs might utilize empirically-derived CERs of the following form:
(1) Development cost of a mechanical system = I& x (weight)0.95 x (Oh of new design) x ( 1 - % redundant design) where Kd is a development calibrating factor that converts development parameters to development dollars. (2 ) First piece manufacturing cost of a machined assembly = I$ x (weight)0.92 x (tolerance) -‘.4 x (number of parts) ‘.’ where Jp is a production calibrating factor that converts production parameters to production dollars. (3) Annual maintenance cost of a mechaical subsystem = I&, x (mean time between failures) x (quantity deployed) x (environment factor) x ( 1/number of operating locations)0.8 where K, is a maintenance calibrating factor converting maintenance parameters to maintenance dollars. These equations represent a parametric model and a measuring instrument wherein the parametrically-estimated unit production cost serves as a common denominator for evaluat-
91
Fig. 3. Comparing alternate designs. Which is more cost effective: the new single-chip computer or the current PCB?
ing the relative simplicity of the candidate design approaches. To be effective, such a technique must be capable of modeling the real world influences of budgets and other constraints. For example, in evaluating alternative design options for high-technology avionics, the designer may be forced to intelligently choose between (a) a new design “computer on a chip” approach with its inherently large development investment, and (b) the more conventional current design printed circuit boards (PCB) approach with lower development costs but with greater production and maintenance costs. A real life comparison is shown in Fig. 3, with all costs determined by parametric methods. A CASE STUDY: HOW ACCURATE PARAMETRIC ESTIMATES?
ARE
Skillful employment of any design tool depends upon its accuracy. How close are parametric estimates to other forms of estimates and to actual costs when the project has been completed? British Aerospace Corporation, of Steven-
u, ;“,‘.I e, I
:sy’.l.i,I
.--.t ; ,:-c ,I ” ,,( -1,:I
Fig. 4. Estimate accuracy distribution. Probability distribution function shows mean difference for 6 years to be + 7.1%. Source: British Aerospace Corporation. History 4395 estimating cases between October 1979 and December 1985.
age, England, tracks estimated costs and actual costs for each of its projects. Data collected over a six-year period, from October 1979 until December 1985, allow vivid comparison of parametric cost estimates to completed project cost actuals on 4395 separate cases! These projects were typically destined for high technology small lot production systems, whose individual production costs were in the range of $200 to $500. The average “error” (not really an error, but the difference between an expected unit cost and its actual outcome) was
92 early stages and voluminous in later stages. Early-stage methodologies embody the parametric concepts noted previously and a growing number of models are commercially available to a growing list of users. Recapitulating, a parameter is an attribute of a system, the measure of which is in some degree a key element of the whole for which it is a part. The radius of a circle, for example, is a parameter of the circle. By measuring the radius and applying simple mathematical equations, the circle’s diameter, area, and circumference may be determined. This general practice of using parameters can be used to find, quantify, and exploit such relationships among the parts of almost any system. Most parametric relationships are not as directly linked to results as those governing the geometry of a circle but can be equally useful. For example, the number of barrels per day of an oil refinery’s throughput is one parameter of the cost to operate the refinery. There are many others, including the number of refinery products and the difficulty of their processing - but, if throughput goes up, costs will go up in a generally predictable way. Quantifying such relationships gives us a way to predict refinery cost. A modern revolution is quietly overtaking industry’s traditional approach to cost-effective product design. Engineers, armed with computerized parametric models and industry-wide data bases are evaluating the cost effectiveness of their own designs, often expanding the design review meeting into a manufacturing planning meeting, a marketing strategy session, and a proposal caucus. The parametric cost model is a key element in this design review process. in
/
,
.
Fig. 5. Estimate accuracy trend. The estimating accuracy improved during the survey period. Source: British Aerospace Corporation. Best-fit trend line shows improvement.
7.10/o,as shown in Fig. 4. The probability distribution function (pdf) shows, further, that more of the 4395 estimates were higher than actuals (versus lower than actuals) and that nearly all of the cases resulted in estimated cost accurately within the + 20% to -20% of the actual. Can designers boast of a better track record in predicting product performance? Perhaps even more astonishing is the trend of this error, or difference between estimated and actual product cost, over the four core years of the survey. As shown in Fig. 5, the “% difference” declined dramatically after the first year, reflecting increasing skill by the parametric modeler and improved accuracy in the models used through continual calibration with real data. This result dramatically illustrates how one company effectively utilizes parametric modeling and its inherent instantaneous feedback mechanism to assist design teams to achieve, not only more acurate estimates, but also reduced production costs! CONCLUSION
Cost-estimating methodologies have been developed for making estimates of various types and for various purposes. The method to use depends on the amount and the degree of definition of the information available at successive stages of product development - sparse
REFERENCES 1 Apgar, H.E. and Daschbach, J.M., 1987. Analysis of design through parametric cost estimation. Parametrics, 7(2): 6-13.
93 2
Gasperow, L.A., Hackney, J.W. and Hudson, K.K., 1986. Parametric Cost Estimating, A Guide. U.S. Government Printing Office under auspices of the U.S. Department of Energy.
3 Apgar, H.E. and Hargrove, N.E., 1985. Parametric Cost Estimating. Federal Publications, Inc. 4 Project Manager’s Guide, Technical Document 108. Naval Ocean System Center, San Diego, CA. June 1977.
DESIGN ANALYSIS THROUGH TECHNIQUES PARAMETRIC COST ESTIMATION*
87
OF
J.M. Daschbach The University
of Toledeo,
Toledo, OH (U.S.A.)
and Henry Apgar Management
Consulting
& Research Inc., Sanra Barbara, CA (U.S.A.)
ABSTRACT
This paper focuses on the parametric model as tool for design engineering analysis. The technique is made relevant to routine, every-day living and proceeds to the engineering design function with a case study of over 4000 in-
stances where the parametric model provided estimates closer to actual costs than the traditional ‘bottom-ups’ means to cost estimation in an aerospace industrial application.
INTRODUCTION
at $250, one may lose the “clean teeth”, if the newspaper price is $450, one probably looks for something else to read. In such cases, costs so greatly exceed expectations that refusal to pay is an instinctive reaction. This reaction reflects use of a parametric model. A first level parametric model applies experience to analyze a cost. Questioning the reason for the cost is the second phase - examining the circumstances to determine why costs vary. There may be a key variable not accounted for in the model used which explains a cost. The same experience that assists cost prediction helps one arrive at possible explanations when costs seem out of line. In tracking down the reasons for cost variance, the objective as well as subjective factors are considered. Objective factors such as size, location, and materials effect cost and these are sometimes modified by less quantifiable, subjective fac-
Engineering design is no longer isolated from or independent of the manufacturing process or market competition pressures. The product design, development, production, and use phases are each subject to analysis of cost by customers, suppliers and users. Tools available at these analytical levels are fast becoming indispensable for product comparisons under unparalleled rules of competition. Such cost analysis tools are best represented by parametric methods. The parametric method is neither new nor complex. It’s an approach used every day by each of us. Each time one buys a toothbrush or a newspaper, there already exists an idea as to the price to be paid. If the toothbrush is priced This article is based on materials provided by the authors from Ref. [ 1 ] and [ 2 ] as modified by the stated authors above.
0167-188X/88/$03.50
0 1988 Elsevier Science Publishers B.V.
88 tors such as the competition, quality levels required, management skills available, and organizational objectives. In drawing on personal experience to predict costs or assess their reasonableness, and in examining variables to explain discrepancies, one follows intuition to the increasingly used parametric approach to cost estimating. Applying the above to the design engineer’s environment, no modern day product designer would risk acceptance of a preliminary design by ignoring the necessity for stress analysis or environmental analysis. Similar disregard of another of the available aids for design analysis - the parametric cost estimating model measn risking potentially disastrous material, manufacturing, quality or other cost pressures on the product’s use. The parametric analysis technique has become a universal design tool for assuring that a design not only meets performance specifications but also is cost effective and affordable. Many lead design engineers depend upon parametric modeling to assure a balanced satisfaction of the three supporting attributes of a good design - manufacturability, suitable quality, and minimal cost. The name for this innovative and effective tool comes from empirical relationships that exist between cost parameters (dollars or labor hours) and selected physical or performance parameters (product size, quality, complexity, and design maturity) as well as the product’s method of production. Such empirical relationships are kown as Cost Estimating Relationships or CERs. Parametric models, typically computerized and analyst interactive, reflect an aggregate of such CERs in functional relationshp formats. Engineering managers may recognize the parametric technique by another name - “top down estimating”. In their purest form, the parametric cost model’s primary variables or “drivers” originate from the same design factors determining the bottoms up or “grass roots” cost drivers. The primary purpose of any cost estimate, of course, is to enhance the ef-
fectiveness of managerial decision making, whether it be questions concerning make or buy, bid or no-bid, concept evaluation, contractor selection, or product planning. THE IMPORTANCE ESTIMATES
OF GOOD
An organization’s future often depends on the accuracy of its cost estimates. Both overestimates and underestimates can spell disaster. Frank Freiman, for many years the head of cost estimating for RCA and developer of the FAST cost-estimating system, graphically represents the over- and underestimates relationships in the Freiman curve ( [ 2 1, see Fig. 1). Succinctly stated, ( 1) the greater the underestimate, the greater the actual expenditure; (2) the greater the overestimate, the greater the actual expenditure; (3 ) the most realistic estimate results in the most economical project cost. Underestimates may land a contract or result in a project approval, but they also frequently lead to financial loss and business failure. Initial project plans of stafIing, scheduling, machine processing, tooling and mate-
Fig. 1. The Freiman curve.
89
rials’ forming, etc., are not achievable. Though the project plan is established to realize the underestimated cost, the project mid-point management begins to realize that milestones and schedules are slipping. In response, there is reorganization, replanning, and possibly the addition of personnel and equipment. Delays and reorganization invariably increase costs. The cost to the organization is also high in other ways, including poor morale and the loss of capable and trained staff. Projects that suffer significant cost growth are often projects scheduled, planned, and staffed based on early underestimates, that eventually lead to a detailed project plan that simply cannot be realized. Underestimates threaten an organization’s ability to survive. Overestimates serve an organization as poorly as the underestimate. Rather than resulting in greater profits, as one might hope, the overestimate reflects a Parkinson’s law application: the money is available, it must be spent. Unless there is firm management control, the estimate becomes a self fuljilling prophesy and the organization becomes weak, unable to deliver a good product for a reasonable price. Realistic estimates result in the most economical cost. They remind managers to control the excess resources. Good estimates let the orgnaization’s resources work in harmony. PROFESSIONAL COST ESTIMATING AND THE PARAMETRIC APPROACH
Professional cost estimating differs only in details from what the “man-on-the-street” does when he/she buys that toothbrush, newspaper or a home. Professionals base a cost forecast on various kinds of knowledge about the product and the circumstances under which it will be bought or produced and on experience with actual costs of similar items. The difference is that professionals - cost estimators, engineers, and managers - are dealing with larger sums of money and ,therefore, must strive for greater
precision than the “man-on-the-street”. To achieve greater precision, formalized and organized experiences are aggregated with those of others in their field and functional relationships are established. Many such formal techniques employ parametric concepts. Some relay so heavily on parametrics that they are labeled Parametric Models. All, however, rely to some degree on parametric principles. Ranging from simple to highly sophisticated, they all have one thing in common: the attaching of values to specific ratios or variables. In home building, for example, square footage is a primary cost parameter. Builders typically state costs in terms of dollars per square foot to build a home. Such statement is a CER and might be used as a rule of thumb in early cost estimating by both builder and home buyer. Such rules of thumb - cost per square foot, cost per pound, cost per horsepower - are easy to use, and yield in preliminary stages, rough estimates. Design, location, materials, year of construction, and labor productivity are other factors that significantly effect cost. Arriving at reasonable estimates involves compromise of designer needs with production/construction problems, balancing gradual improvement of estimating accuracy and escalation as the design approaches completion, and detailed bills of material that can be prepared against the typically fast increasing level of the estimate itself. Professional cost estimating, then, involves managers, engineers, designers, technical experts, and cost estimators in an iterative process responding to different questions at different phases of project or product during the life cycle. The first decision - whether to allocate resources necessary to prepare the bid or undertake more detail project analysis - is a big one because cost estimating and analysis consume time and money. As work proceeds, it is important to arrive at a reasonable cost estimate as soon as possible because - as Fig. 2 illustrates - decisions affecting about 70-80%
90 lQEI%
+
__--4 _+_----
I
saz
i
333
I
/-__..i
__.-
._c--
.j
Fig. 2. Cost impact of decisions.
of the total eventual costs are made in the concept formulation stage when only l-2% of the design work is complete. APPLYING DESIGN
THE COST PARAMETER
TO
The responsible lead designer begins with a perception of the product architecture, based on many factors including the customer’s specification, the company’s previous experience and the engineers’ design capability. Among many constraints, one must insure against the inherent nature of the design engineer to overdesign or to ignore cost-effectiveness. The lead designer’s solution, therefore, is to establish the simplest form of the product architecture and its manufacturing process. To do this, one must evaluate many design approaches and identify the optimum design approach. Evaluating candidate design approaches by comparing their relative development, production, or maintenance costs might utilize empirically-derived CERs of the following form:
(1) Development cost of a mechanical system = I& x (weight)0.95 x (Oh of new design) x ( 1 - % redundant design) where Kd is a development calibrating factor that converts development parameters to development dollars. (2 ) First piece manufacturing cost of a machined assembly = I$ x (weight)0.92 x (tolerance) -‘.4 x (number of parts) ‘.’ where Jp is a production calibrating factor that converts production parameters to production dollars. (3) Annual maintenance cost of a mechaical subsystem = I&, x (mean time between failures) x (quantity deployed) x (environment factor) x ( 1/number of operating locations)0.8 where K, is a maintenance calibrating factor converting maintenance parameters to maintenance dollars. These equations represent a parametric model and a measuring instrument wherein the parametrically-estimated unit production cost serves as a common denominator for evaluat-
91
Fig. 3. Comparing alternate designs. Which is more cost effective: the new single-chip computer or the current PCB?
ing the relative simplicity of the candidate design approaches. To be effective, such a technique must be capable of modeling the real world influences of budgets and other constraints. For example, in evaluating alternative design options for high-technology avionics, the designer may be forced to intelligently choose between (a) a new design “computer on a chip” approach with its inherently large development investment, and (b) the more conventional current design printed circuit boards (PCB) approach with lower development costs but with greater production and maintenance costs. A real life comparison is shown in Fig. 3, with all costs determined by parametric methods. A CASE STUDY: HOW ACCURATE PARAMETRIC ESTIMATES?
ARE
Skillful employment of any design tool depends upon its accuracy. How close are parametric estimates to other forms of estimates and to actual costs when the project has been completed? British Aerospace Corporation, of Steven-
u, ;“,‘.I e, I
:sy’.l.i,I
.--.t ; ,:-c ,I ” ,,( -1,:I
Fig. 4. Estimate accuracy distribution. Probability distribution function shows mean difference for 6 years to be + 7.1%. Source: British Aerospace Corporation. History 4395 estimating cases between October 1979 and December 1985.
age, England, tracks estimated costs and actual costs for each of its projects. Data collected over a six-year period, from October 1979 until December 1985, allow vivid comparison of parametric cost estimates to completed project cost actuals on 4395 separate cases! These projects were typically destined for high technology small lot production systems, whose individual production costs were in the range of $200 to $500. The average “error” (not really an error, but the difference between an expected unit cost and its actual outcome) was
92 early stages and voluminous in later stages. Early-stage methodologies embody the parametric concepts noted previously and a growing number of models are commercially available to a growing list of users. Recapitulating, a parameter is an attribute of a system, the measure of which is in some degree a key element of the whole for which it is a part. The radius of a circle, for example, is a parameter of the circle. By measuring the radius and applying simple mathematical equations, the circle’s diameter, area, and circumference may be determined. This general practice of using parameters can be used to find, quantify, and exploit such relationships among the parts of almost any system. Most parametric relationships are not as directly linked to results as those governing the geometry of a circle but can be equally useful. For example, the number of barrels per day of an oil refinery’s throughput is one parameter of the cost to operate the refinery. There are many others, including the number of refinery products and the difficulty of their processing - but, if throughput goes up, costs will go up in a generally predictable way. Quantifying such relationships gives us a way to predict refinery cost. A modern revolution is quietly overtaking industry’s traditional approach to cost-effective product design. Engineers, armed with computerized parametric models and industry-wide data bases are evaluating the cost effectiveness of their own designs, often expanding the design review meeting into a manufacturing planning meeting, a marketing strategy session, and a proposal caucus. The parametric cost model is a key element in this design review process. in
/
,
.
Fig. 5. Estimate accuracy trend. The estimating accuracy improved during the survey period. Source: British Aerospace Corporation. Best-fit trend line shows improvement.
7.10/o,as shown in Fig. 4. The probability distribution function (pdf) shows, further, that more of the 4395 estimates were higher than actuals (versus lower than actuals) and that nearly all of the cases resulted in estimated cost accurately within the + 20% to -20% of the actual. Can designers boast of a better track record in predicting product performance? Perhaps even more astonishing is the trend of this error, or difference between estimated and actual product cost, over the four core years of the survey. As shown in Fig. 5, the “% difference” declined dramatically after the first year, reflecting increasing skill by the parametric modeler and improved accuracy in the models used through continual calibration with real data. This result dramatically illustrates how one company effectively utilizes parametric modeling and its inherent instantaneous feedback mechanism to assist design teams to achieve, not only more acurate estimates, but also reduced production costs! CONCLUSION
Cost-estimating methodologies have been developed for making estimates of various types and for various purposes. The method to use depends on the amount and the degree of definition of the information available at successive stages of product development - sparse
REFERENCES 1 Apgar, H.E. and Daschbach, J.M., 1987. Analysis of design through parametric cost estimation. Parametrics, 7(2): 6-13.
93 2
Gasperow, L.A., Hackney, J.W. and Hudson, K.K., 1986. Parametric Cost Estimating, A Guide. U.S. Government Printing Office under auspices of the U.S. Department of Energy.
3 Apgar, H.E. and Hargrove, N.E., 1985. Parametric Cost Estimating. Federal Publications, Inc. 4 Project Manager’s Guide, Technical Document 108. Naval Ocean System Center, San Diego, CA. June 1977.