June 17, 2004

1
Transitioning from Parametric to Buildup Estimates
Allison Wiley, Megan Dameron, Sarah
Grinnell Brian Brophy, Dick Coleman, Jessica
Summerville
June 17, 2004
2
Outline

• Introduction
• Explanation of the Challenge
• Developing a Buildup Estimate
• Parametric Pullout Using a Toy Problem

3
Introduction

• Purpose of this Presentation
• Discuss the challenge presented in transitioning
  from parametric to buildup cost estimates
• Present some of the ideas and approaches
  considered to address the challenge
• Generate ideas and discussion with the audience
  in order to advance our thinking

4
The Challenge

• The transition from a parametric to a buildup
  cost estimate
• Develop cost estimates specific to a subsystem
• Calculate the appropriate amount to remove from
  the parametric estimate for the system, in order
  to insert the buildup estimate
• Utility
• Provides detailed information so that the cost of
  operations and support or specific production
  items can influence the system design
• Buildups are instructive and can lead to
  improvements in parametric estimates

5
Developing a Buildup Estimate

• Why the transition?
• There could be subsystems that behave in a way
  that is inherently different from the legacy
  subsystems in the cost data
• It may not be possible to estimate the entire
  system at a detailed level, but there may be
  detailed information available for one or more
  subsystems
• Defining buildup
• In this presentation, the word buildup refers to
  an estimate specific to a subsystem
• Intuitively the authors had in mind traditional
  buildup estimates as well as analogies
• Parametric estimates of a subsystem alone could
  also be considered

6
Parametric Pullout

• The term parametric pullout refers to the
  amount of the parametric estimate that is
  attributable to a specific subsystem in short,
  the amount of the parametric estimate that is
  pulled out so that the buildup estimate may be
  inserted.
• The idea of parametric pullout and some of the
  methods considered to address this problem are
  explained in the next several slides with the use
  of a toy problem.

7
Parametric Pullout

• Introduction to the toy problem
• You have a CER that estimates the cost of a car
  based on its weight
• To reduce confusion, lets call the car the TE-4
• You have recently developed a buildup estimate
  for the transmission alone, and you hope to
  eventually have buildup estimates for most of the
  major parts of the car
• Lets call the transmission the BE-2
• The challenge Incorporate the buildup estimate
  for the BE-2 into the total cost estimate for the
  TE-4

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Parametric Pullout (contd)

• Method 1 Historical Percentage
• If the transmission of a car is historically 8
  of car cost, remove 8 of the TE-4 estimated cost
• Advantages Easy to execute, also works for costs
  estimated using historical averages instead of
  CERs
• Disadvantages Method requires specific
  historical data that may not be available in all
  cases

9
Parametric Pullout

• Method 1 Historical Percentage
• Toy Problem

Historical Data
25,680 x 15.6
25,680 - 4,015 3,650
10
Parametric Pullout

• Method 1 Historical Percentages
• Concerns
• Consider the case where the BE-2 comprises a much
  larger percent of TE-4 weight than any legacy
  transmission and car. There is a concern that
  since the BE-2 weighs so much more, the
  percentage method may cause the removal of an
  inadequate amount of cost.

11
Parametric Pullout

• Method 2 Parameter-based
• Use the parameters of the CER to determine the
  correct piece to pullout
• Advantage Requires little data and is easily
  executed
• Disadvantage Subsystem may not have parameters
  comparable to the parent system. For example,
  there is not an intuitive way to pull the cost of
  a tire out of a car CER that uses the weight of
  the cars electrical system.

Tip It is frequently best to take the CER result
from the parameters of the whole system, and the
CER result from the parameters of the whole
system minus the subsystem and note the
difference. This is especially important when
CERs have multiple variables and/or are
non-linear.
12
Parametric Pullout

• Method 2 Parameter-based
• Toy Problem
• Run the car CER on the weight of the entire car
• Run the car CER on the weight of the entire car,
  less the weight of the transmission
• Subtracting the latter from the former yields the
  amount to be removed
• Add in the buildup estimate for the transmission

13
Parametric Pullout

• Method 2 Parameter-based
• Concerns
• In a CER for a total system model, the parameters
  may mask or interplay with other parameters
• For example, in a car CER based on electrical
  system weights, the electrical system weight acts
  as a proxy for other system weights (like the
  tires, the seats, and the frame of the car).
• Intuitively, removing a few pounds of electrical
  system weight removes an electrical component,
  but mathematically it also removes all of the
  frame of the car that supports the electrical
  component.
• It is important to understand the meaning of the
  CER.

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Parametric Pullout

• Method 3 Obtain new CER
• Example
• Y legacy car cost legacy transmission cost
• X (legacy car weight legacy transmission
  weight))
• Results should yield reasonable F and t
  statistics, and a reasonable R2
• Use TE-4 minus BE-2 weights in the new equation
• Subtract the new result for a transmission-less
  car from the existing result for all of TE-4
  this is the pullout amount
• Advantages Provides a good check of the existing
  CER
• Disadvantages Requires significant resources and
  data to accomplish, some CERs will not respond
  well to this method without being completely
  reworked

15
Toy Problem (contd)

• Method 3 Re-run CER
• Return to the original data that produced the CER
• Subtract the cost of the transmission from the
  cost of the car, and the weight of the
  transmission from the weight of the car

Car Cost 4,499 4.75(Car Weight) R2 0.99 t
and F significant Estimated weight of new car
4,000 lbs Estimated cost of new car 23,496
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Toy Problem (contd)

• Method 3 Re-run CER
• Rerun the regression (ensure that t and F
  statistics are still significant, and R2 is
  reasonable)
• Run the new CER on the weight of car to be
  estimated, minus the weight of the transmission

Transmission-less CarCost 4,167
4.46(Car Weight Transmission Weight) R2
0.99 t and F significant Estimated weight of new
car w/o transmission 4,000 500 3,500
lbs Estimated cost of new car w/o transmission
19,791
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Parametric Pullout (contd)

• Method 3 Obtain new CER (contd)
• Concerns
• This method may not behave intuitively
• Changes are observed in the coefficients of
  unchanged parameters (when multiple parameters
  are present)
• What is the expectation for behavior?

If Method 2 and Method 3 are to yield the same
result, the new CER must intersect the Car CER at
the weight of the transmission-less car
Car CER Possible Transmission-less Car CER
Case 1 Method 2 and Method 3 yield the same
result
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Parametric Pullout (contd)

• Method 3 Obtain new CER (contd)
• Concerns (contd)
• What is the expectation for behavior?

Car CER Possible Transmission-less Car CER
Case 2 Method 3 removes more cost than Method 2
but the new CER behaves intuitively
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Parametric Pullout (contd)

• Method 3 Obtain new CER (contd)
• Concerns (contd)
• What is the expectation for behavior?

Car CER Possible Transmission-less Car CER
Case 3 Method 3 removes less cost than Method 2,
the new CER may not behave intuitively
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Parametric Pullout (contd)

• Method 3 Obtain new CER (contd)
• Concerns (contd)
• What is the expectation for behavior?

Car CER Possible Transmission-less Car CER
Case 4 The new CER yields an estimate higher
than the existing CER the method does not
produce a parametric pullout
21
Parametric Pullout

• Second order effects
• In some models, particularly in operating and
  support cost, some elements are estimated using a
  parametric relationship to another cost element
• If this is the case, it is important to keep
  careful track of order of operations, etc. to
  be certain that the appropriate results are
  captured

Warning It is easy to get caught in the trap of
thinking that the element which is being pulled
out and put back in is causing changes to other
elements in the model. This is not the case -
second order changes to elements are attributable
to a total change in the cost element value, not
to the system being incorporated into the model.
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Parametric Pullout

• Complexities beyond the toy problem
• The toy problem demonstrates an example of a CER
  that is linear and uses just one variable
• There are many complexities of the problem that
  occur in non-linear and/or multivariable CERs

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Lessons Learned

• Buildups are useful because of the insight they
  may provide. The following has occurred during
  the authors experience with this topic
• A significant improvement in understanding of the
  nuances of a main source of historical data
  resulted
• Led to an examination of several existing CERs
  and led directly to improvements in at least one
• Allowed system designers to focus future cost and
  CAIV resources on specific areas of interest
• The details of the process generated discussions
  with the system designers that provided further
  insight into design and cost issues of all types