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Estimating – Methods and Practise

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1 Estimating – Methods and Practise
A discussion paper Ó Galorath Incorporated 2003

2 es·ti·mate (es′ti mit), n.
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

3 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

4 The estimating environment

5 Time available to generate the estimate
Estimate continuum Assumptions High Low Domain experience Low High Time available to generate the estimate Low High

6 Rough Order of Magnitude Percentage expected error
Classes of estimates 20 40 60 80 100 -20 -40 -60 Class 1 Class 2 Class 3 Class 4 Class 5 Rough Order of Magnitude Feasibility studies Preliminary estimate Definitive estimate Detailed Estimates Worst range of expected accuracy Best range of expected accuracy Project Phases Percentage expected error 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.

7 Information availability
Part Knowledge Estimate assumptions Conceptual design Detail design Part level

8 Effort vs. accuracy

9 Classes of estimates Class 1 0 - 20% Concept Screening 1 Class 2
Primary Characteristic Secondary Characteristic LEVEL OF PROJECT DEFINITION Expressed as % of complete definition END USAGE Typical purpose of estimate METHODOLOGY Typical estimating method EXPECTED ACCURACY RANGE Typical variation in low and high ranges PREPARATION EFFORT Typical degree of effort relative to least cost index ESTIMATE CLASS 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 % Budget, Authorisation control Semi-detailed unit cost with assembly level line items Low = -10 to -20% High = +10 to +30% 3 - 10 Class 4 % Control or Bid/Tender Detailed Unit costs Low = -5 to -15% High = +5 to +20% 4 - 20 Class 5 % Check Estimate Detailed Unit costs Low = -3 to -10% High = +3 to +15% Source: WOODWARD, C. & CHEN, M. Cost Estimating Basics. Skills and Knowledge of Cost Engineering, 4th edition, 1999.

10 How do we estimate

11 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

12 Guess the time - 1 20mm 10.00mm 32mm 16mm Material is Aluminium

13 Guess 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

14 Guess the time - 2

15 Comparison 20mm 10.00mm 32mm 16mm 20mm 10.00mm 100mm 16mm

16 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

17 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

18 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 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 G A SEER Technologies 16

19 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

20 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

21 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

22 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.” Pert mean Galorath Inc. 2003 All Rights Reserved

23 Parametric 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

24 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

25 Learning Curves Time Units T1 = 388 Represents Learning with 95% slope
400 B Learning Step 2 A Learning Step 1 End Production 1000 T1B = 388 Represents Learning with 85% slope

26 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

27 What should you be using?

28 Use several estimating tools
Macro level Risk Cost Features / functions Conceptual design data Low data requirements Process based High risk elements Detailed design data Should cost models Purchase target range Process variants Quick High volume Generative Models Parts outside expected range Detailed supplier data Supplier cost models Accepted new base line for product type Slow Low volume

29 Estimating solution overlap
Macro Level Over-lap – large sub-systems, single component costing Overlap allows for calibration and sanity checks Process Level Overlap – mid value, low volume, spares, tooling estimates. Generative Models

30 Estimating toolbox for the integrated enterprise
Model at process level Set reference Output target cost range Macro level model baseline Assess risk Detail Level Apply supplier cost model negotiations negotiations

31 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

32 Estimates are always wrong!
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

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