Transcript Cost Estimating at Rolls

Estimating – Methods and Practise
A discussion paper
Galorath Incorporated 2003
Estimate 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. 2003
All Rights Reserved
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 environment
Estimate continuum
Low
Assumptions
High
High
Low Time available to High
generate the
estimate
Domain
experience
Low
Classes of estimates
Project Phases
A
B
C
D
E
F
Percentage expected error
100
Worst range of expected accuracy
80
60
Best range of expected accuracy
40
20
0
-20
-40
-60
Class 1
Rough Order of
Magnitude
Class 2
Feasibility
studies
Class 3
Preliminary
estimate
Class 4
Definitive
estimate
Class 5
Detailed Estimates
Calendar Time (No Scale)
Source: WOODWARD, C. & CHEN, M. Cost Estimating Basics. Skills and Knowledge of Cost Engineering, 4 th edition, 1999.
Conceptual design
Part Knowledge
Detail design
Part level
Estimate assumptions
Information availability
Effort vs. accuracy
EFFORT
Acuracy
Classes of estimates
Primary
Characteristic
Secondary Characteristic
LEVEL OF
PROJECT
DEFINITION
Expressed as % of
complete definition
END USAGE
Typical purpose of
estimate
EXPECTED
METHODOLOGY
ACCURACY
Typical estimating
RANGE
method
Typical variation in
low and high ranges
Class 1
0 - 20%
Concept
Screening
Parametric Models,
Judgment, or
Analogy
Low = -20 to -50%
High = +30 to +100%
1
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
Estimate
Detailed Unit costs
Low = -3 to -10%
High = +3 to +15%
5 - 100
ESTIMATE
CLASS
PREPARATION
EFFORT
Typical degree of
effort relative to
least cost index
Source: WOODWARD, C. & CHEN, M. Cost Estimating Basics. Skills and Knowledge of Cost Engineering, 4 th edition, 1999.
How do we estimate
Types 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 - 1
32mm
16mm
20mm
10.00mm
Material is Aluminium
Guess the time - 1 - Results
Time dependent on domain knowledge
• Accuracy
• Volume
• Process
Time dependant on level of detail
Add some more
information
Does this change
your estimate?
Guess the time - 2
Comparison
32mm
16mm
20mm
10.00mm
100mm
20mm
16mm
10.00mm
Commodity 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 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 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
- Clock Speed (Frequency)
- Fault Detection
Electronic Classification
-
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 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 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 - 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 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. 2003
All Rights Reserved
Parametric vs. Generative - 2
Problems with Generative
Problems with Parametric
• Most data is at T250 + and
• Too generic
may be unknown or differ
• Need experience to understand results between process types
• “black box”
• Hard to determine risk as
mono input
• Too Good to be True!
• Typical systems have no
learning curves
• Takes a long time to build
and maintain the system
What 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
Learning Curves
Time
T1B =
388
T1 =
388
Represents
Learning
with 85%
slope
Represents
Learning
with 95%
slope
A
Learning
Step 1
B
Learning
Step 2
Units
400
1000
End
Production
Risk 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?
Use several estimating tools
Conceptual
design data
Macro level
Risk
Low data
Cost
requirements
Features / functions
High risk elements
Detailed
design data
Process based
Should cost models
Quick
Purchase target
High volume
range
Process variants
Parts outside expected range
Detailed
supplier data
Generative
Models
Supplier cost models
Slow
Accepted new base Low volume
line for product type
Estimating solution overlap
Over-lap – large sub-systems,
single component costing
Macro
Level
Process Level
Overlap – mid value,
low volume, spares,
tooling estimates.
Generative
Models
Overlap allows for
calibration and
sanity checks
Estimating toolbox for the integrated enterprise
Model at process level
Detail Level
Macro level model
Set reference
baseline
Apply supplier
cost model
Output target cost range
negotiations
Assess risk
negotiations
Benefits 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 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