Galorath Incorporated 2003

Estimating – Methods and PractiseEstimating – Methods and Practise

A discussion paper

Estimate definedEstimate defined

es·ti·mate (es′ti mit), n.an approximate judgment or calculation, as of the value or amount of something

a prediction that is equally likely to be above or below the actual result (Tom DeMarco)

Galorath Inc. 2003All Rights Reserved

Estimating – why ?Estimating – why ?

Conceptual design

• Which way

• Feature / function implications

• Budget setting

• Feature / function trade offs

• Bid no / bid evaluation

System / assembly level

• Trade studies

• What if

Detail design

• Target cost modelling

• Design to cost

• Value analysis

Part level

• Should cost models

• Supplier cost modelling

• Make buy decisions

• Process selection

• Material implications

The estimating environmentThe estimating environment

Estimate continuumEstimate continuum

Assumptions

High

Low

Time available to generate the

estimate

Low High

Domain experience

Low

High

Classes of estimatesClasses of estimates

20

40

60

80

100

0

-20

-40

-60

Class 1 Class 2 Class 3 Class 4 Class 5Rough Order of

MagnitudeFeasibility

studiesPreliminary

estimateDefinitive estimate

Detailed Estimates

Worst range of expected accuracy

Best range of expected accuracy

Project Phases

Per

cen

tag

e ex

pec

ted

err

or

Calendar Time (No Scale)

A B C D E F

Source: WOODWARD, C. & CHEN, M. Cost Estimating Basics. Skills and Knowledge of Cost Engineering, 4th edition, 1999.

Information availabilityInformation availability

Conceptual design

Detail design

Part level

Part K

nowledge

Est

imat

e as

sum

ptio

ns

Effort vs. accuracyEffort vs. accuracy

EFFORT

Acuracy

Classes of estimatesClasses of estimates

ESTIMATE CLASS

LEVEL OF PROJECT

DEFINITIONExpressed as %

of complete definition

Class 1 0 - 20%Concept

Screening

Primary Characteristic

Secondary Characteristic

Parametric Models,

Judgment, or Analogy

Low = -20 to -50%High = +30 to +100%

1

END USAGETypical purpose

of estimate

METHODOLOGYTypical

estimating method

EXPECTED ACCURACY

RANGETypical variation in low and high

ranges

PREPARATION EFFORT

Typical degree of effort relative to least cost index

Class 2 1 - 15%Study or

feasibility

Equipment factored or Parametric

Models

Low = -15 to -30%High = +20 to +50%

2 - 4

Class 3 10 - 40%Budget,

Authorisation control

Semi-detailed unit cost with assembly level

line items

Low = -10 to -20%High = +10 to +30%

3 - 10

Class 4 30 - 70%Control or Bid/Tender

Detailed Unit costs

Low = -5 to -15%High = +5 to +20%

4 - 20

Class 5 50 - 100%Check

EstimateDetailed Unit

costs

Low = -3 to -10%High = +3 to +15%

5 - 100

Source: WOODWARD, C. & CHEN, M. Cost Estimating Basics. Skills and Knowledge of Cost Engineering, 4th edition, 1999.

How do we estimateHow do we estimate

Types of estimateTypes of estimate

Domain experience driven

• Guess

• Comparison

• Commodity parametric

• Domain value

General parametric

• Macro level

• Process level

• Feature based

Generative

• Variant process

• Generic plan with variables

Measured

• Time study

• MTM

Guess the time - 1Guess the time - 1

20mm 10.00mm

32mm 16mm

Material is Aluminium

Guess the time - 1 - ResultsGuess the time - 1 - Results

Time dependent on domain knowledge

• Accuracy

• Volume

• Process

Time dependant on level of detail

Does this change your estimate?

Add some more information

Guess the time - 2Guess the time - 2

ComparisonComparison

20mm 10.00mm

32mm 16mm

20mm 10.00mm

100mm 16mm

Commodity ParametricCommodity Parametric

What is it

• Cost estimate relationship built for a specific commodity within a specific industrial instance

What's it based on

• Current supply costs and trends

• Common part attributes

• Lots of assumptions

Example

• Need to estimate the cost of a die casting for use within the aerospace industry

• Review history of cost against parts

• Plot number of features, weight, accuracy, volume, application against cost

• Look for correlation of key cost drivers

• Derive CER

• Test CER

Macro Level Parametric EstimatingMacro Level Parametric Estimating

Little knowledge of details / high assumptions

Estimates based on high level information

• Weight

• Boards

• Complexity

Quicker than manual methods

Able to estimate without cost data

Should be calibrated to local environment

Can/Should include Development, Production, Logistics, Operations, & Support Costs all in one model

Should include sensitivity and risk analysis

Macro level parameter examplesMacro level parameter examples

Electronics circuitry can be accurately described- Number of Printed Circuit Boards - Number of Discretes per PCB - Operating Environment

- Circuitry Composition - Number of Integrated Circuits per PCB - IC Technology

- Packaging Density - Number of I/O Pins per PCB - Fault Isolation

Electronic Classification - Clock Speed (Frequency) - Fault Detection

- Note: Weight to board conversion available for those dealing with weight statements only

Mechanical subsystem aspects tailor estimate to user situation- Weight - Material Composition - Hardware Classification

- Volume - Operating Environment - Internal Pressure

- Complexity of Form - Construction Process - Operating Service Life

- Complexity of Fit

Program attributes are easily defined (for both Electronics & Mechanical)- New Design - Certification Level - Dev/Prod Tools & Practices

- Design Replication - Hardware Integration Level - Production Qty’s Prototypes

- Requirements Volatility - Dev/Prod Experience & Capability - Purchased Parts

- Schedule - Labor Rates - Wraps & Fees

Process based parametric estimatingProcess based parametric estimating

Based on mathematically derived CER’s

Estimates based on generic manufacturing details

Production methods evaluated

Should be calibrated to local environment

Includes sensitivity and risk analysis

Should produce an acceptable range for the items / assembly

Process knowledge but no time

Good part data available but no time

Need to run multiple trade studies

Generative estimatingGenerative estimating

Deterministic

Base on formulas

Detailed process plan

• Speeds

• Feeds

• Precise removal rates

• Scrap rates

Virtual factory model for suppliers

Can add new process models

Tends to be in-house verified data

Parametric vs. Generative - 1Parametric vs. Generative - 1

Parametric Benefits

• Speed

• Level of data required

• Learning curves

• Design as well as production

• Operation and support costs

• Three value input indicates level of uncertainty

Generative Benefits

• Detail

• Accuracy

• Flexibility

• “open” data source

Expressing UncertaintyExpressing Uncertainty

Estimates of Size and Technology expressed as single point values don’t tell the whole story:

• How confident am I in this value; i.e., what is the probability of not exceeding this value?

• How certain am I in this value; i.e., how wide is the probability distribution?

Three-point estimates are better:

• LEAST: 1% Probability; “I can’t imagine the result being any smaller than this.”

• LIKELY: Best Guess; “If I were forced to pick one value, this would be it.”

• MOST: 99% Probability; “I can’t imagine the result being any larger than this.”

Galorath Inc. 2003All Rights Reserved

Parametric vs. Generative - 2Parametric vs. Generative - 2

Problems with Parametric

• Too generic

• Need experience to understand results

• “black box”

• Too Good to be True!

Problems with Generative

• Most data is at T250 + and may be unknown or differ between process types

• Hard to determine risk as mono input

• Typical systems have no learning curves

• Takes a long time to build and maintain the system

What is learningWhat is learning

Simply the effect that experience with a process has on the time taken to complete the process

Two main types

• Unit (Crawford)

• Cumulative Average (Wright)

Effects low unit volumes and manual work more than automated processes

• Hand lay-up

• Complex assembly

T1 = 388

Units

Time

Represents Learning with 95% slope

400

B Learning

Step 2

A Learning

Step 1

End Production

1000

T1B = 388

Represents Learning with 85% slope

Learning CurvesLearning Curves

Risk for short programsRisk for short programs

If your project runs at lower rates than your data generated from you could risk losing money as the learning curve is not taken into account

Opportunities for reporting real cost reduction via process improvements are lost

What should you be using?What should you be using?

Use several estimating toolsUse several estimating tools

Macro level

Risk

Cost

Features / functions

Conceptual design data

Process based

High risk elements

Detailed design data

Should cost models

Purchase target range

Process variants

GenerativeModels

Parts outside expected range

Detailed supplier data

Supplier cost models

Accepted new base line for product type

QuickHigh volume

SlowLow volume

Low data requirements

Estimating solution overlapEstimating solution overlap

Macro Level

Process Level

Generative Models

Over-lap – large sub-systems, single component costing

Overlap – mid value, low volume, spares, tooling estimates.

Overlap allows for calibration and sanity checks

Estimating toolbox for the integrated enterpriseEstimating toolbox for the integrated enterprise

Macro level model

baseline

Assess risk

Model at process level

Set reference

Output target cost range

negotiations

Detail Level

Apply supplier cost model

negotiations

Benefits of multiple tool approachBenefits of multiple tool approach

Use appropriate estimating technology at each stage of the product life cycle

Top-down Parametric tool can be used with the minimum of process knowledge

Bottom-up parametric tool allows fast accurate ranges to be established for family groups

Generative modelling will establish base lines for supplier modelling

Pyramid approach supports ALL the company cost engineering needs

Use for sanity check and calibration between models

Increased confidence and overall capability

Last but not leastLast but not least

Remember

• No matter how long you spend

• How much you discuss with your colleagues

• Who you involve

• How experienced you are

Estimates are always wrong!

Our task is to understand how wrong and to make sure our organisation is wise to the risks and assumptions