galorath incorporated 2003 estimating – methods and practise a discussion paper
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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