Download presentation

1
**Pitfalls of Project Estimating:**

An Applied Lesson from Dr. Deming’s Funnel Experiment John Miller, PMP, CLSSBB 4/28/10

2
**Dr. Deming’s Funnel Experiment What is a Process? Statistical Measures **

Contents Introduction Dr. Deming’s Funnel Experiment What is a Process? Statistical Measures Estimating Methods Concepts Averages and Standard Deviation Data Points to Data Trends Control Charts Lessons from the Funnel Experiment Using Excel to Avoid Common Mistakes J. Miller, 4/28/10

3
**Dr. Deming’s Funnel Experiment**

Introduction Parametric and evidenced-based estimating tools have become very sophisticated. Many essential benefits of these tools can inexpensively be provided using Excel, but project managers need to be aware of risks associated with improperly adjusting estimating metrics. Dr. Deming’s Funnel experiment serve’s as a model for improperly adjusting estimating metrics. A simple example will demonstrate how statistically valid estimating methods can be duplicated in Excel. Dr. Deming’s Funnel Experiment “A experiment that demonstrates the effects of tampering [with a process]. Marbles are dropped through a funnel in an attempt to hit a flat-surfaced target below. The experiment shows that adjusting a stable process to compensate for an unstable result or an extraordinarily good result will produce output that is worse than if the process had been left alone.” Donna C. Summers, Quality Management 2nd Edition, p 546 J. Miller, 4/28/10

4
**DR. Deming’s Funnel Experiment**

Funnel Comparison to Project Estimating Funnel = Estimating process Target = Goal -- project time goal or cost goal or resource goal Marble = Actual project or estimate that creates measurable output – cost, schedule, etc. Each marble drop = actual project or part of project Moving the Funnel = Adjusting the project resources after each project to try to meet the target on the next project. (Ex. Adding people or tools, if schedules are being missed, removing resources if budgets are being missed) Moving the Target = Adjusting the time or cost estimate estimating process after each project based on the results from the last project. J. Miller, 4/28/10

5
**DR. Deming’s Funnel Experiment**

Funnel Comparison to Project Estimating Each of the four rules is discussed below. Rule 1: Leave the funnel fixed over the target. Why don't we simply adjust the funnel after each drop so the next drop will be closer to the target? Don’t change the estimating process, accept the variation, the difference between the estimate and actual projects. Rule 2: For every drop, the marble will come to rest a distance "z" from the target. Rule 2 is to move the funnel a distance -z from its last position. Move the funnel based on the funnels last position. Chang the estimating process (metrics) after each project or difference between estimate and actual. Rule 3: Move the funnel a distance -z from the target after each drop of the marble that ends up a distance z from the target. Note that Rule 2 moves the funnel based on the funnel's last position. Rule 3 moves the funnel a distance from the target. Rule 4: Rule 4 is simply to set the funnel over where the last drop came to rest. J. Miller, 4/28/10

6
What is a Process? A series of steps or actions that convert input to output. Project estimating is a process. Processes are described by statistical measures – numbers that describe “groups”. Groups are made of data points. What do we need to better understand this group of data points? Centering – Mean Spread - Stand Deviation Shape – Normality, data distribution (assume all distributions here are Normal) We need data. J. Miller, 4/28/10

7
**Statistical Measures Dimensional attributes**

Product or Process metrics such as: # or % defects % on time deliveries Efficiency (productivity, ratios) # units of output / unit of time (may also include inputs) Time / # units of output xx units / hr Turn Around Time (TAT), xx Points / unit Cost/Unit Which can be averages? J. Miller, 4/28/10

8
**Estimating Methods PMBOK 3rd**

6.4.2 Activity Duration Estimating: Tools and Techniques .1 Expert Judgment Activity durations are often difficult to estimate because of the number of factors that can influence them, such as resource levels or resource productivity. Expert judgment, guided by historical information, can be used whenever possible. The individual project team members may also provide duration estimate information or recommended maximum activity durations from prior similar projects. If such expertise is not available, the duration estimates are more uncertain and risky. .2 Analogous Estimating Analogous duration estimating means using the actual duration of a previous, similar schedule activity as the basis for estimating the duration of a future schedule activity. It is frequently used to estimate project duration when there is a limited amount of detailed information about the project for example, in the early phases of a project. Analogous estimating uses historical information (Section ) and expert judgment. Analogous duration estimating is most reliable when the previous activities are similar in fact and not just in appearance, and the project team members preparing the estimates have the needed expertise. J. Miller, 4/28/10

9
**Estimating Methods PMBOK 3rd**

6.4.2 Activity Duration Estimating: Tools and Techniques .3 Parametric Estimating Estimating the basis for activity durations can be quantitatively determined by multiplying the quantity of work to be performed by the productivity rate. For example, productivity rates can be estimated on a design project by the number of drawings times labor hours per drawing, or a cable installation in meters of cable times labor hours per meter. The total resource quantities are multiplied by the labor hours per work period or the production capability per work period, and divided by the number of those resources being applied to determine activity duration in work periods. .4 Three-Point Estimates The accuracy of the activity duration estimate can be improved by considering the amount of risk in the original estimate. Three-point estimates are based on determining three types of estimates: • Most likely. The duration of the schedule activity, given the resources likely to be assigned, their productivity, realistic expectations of availability for the schedule activity, dependencies on other participants, and interruptions. • Optimistic. The activity duration is based on a best-case scenario of what is described in the most likely estimate. • Pessimistic. The activity duration is based on a worst-case scenario of what is described in the most likely estimate. An activity duration estimate can be constructed by using an average of the three estimated durations. That average will often provide a more accurate activity duration estimate than the single point, most-likely estimate. J. Miller, 4/28/10

10
**Estimating Methods .1 Expert Judgment**

Expert judgment, guided by historical information, can be used whenever possible. .2 Analogous Estimating Analogous estimating uses historical information and expert judgment. .3 Parametric Estimating Estimating the basis for activity durations can be quantitatively determined by multiplying the quantity of work to be performed by the productivity rate. .4 Three-Point Estimates An activity duration estimate can be constructed by using an average of the three estimated durations. Mean The mean is the average data point value within a data set. To calculate the mean, add all of the individual data points then divide that figure by the total number of data points. Published estimating methods overlook critical statistical attributes. What’s missing? J. Miller, 4/28/10

11
**Estimating Methods – Averages**

What’s missing? Mean Standard Deviation If I use the “average” of past projects to estimate future projects, what is virtually guaranteed? J. Miller, 4/28/10

12
Concepts Statistical measure – numbers that describe groups. A numerical value, such as standard deviation or average, that characterizes the sample or population from which it was derived. What do we need to better understand this group of data points? Centering – Mean Spread - Stand Deviation Shape – Normality, data distribution (assume all distributions here are Normal) J. Miller, 4/28/10

13
**Averages and Standard Deviation**

Mean The mean is the average data point value within a data set. To calculate the mean, add all of the individual data points then divide that figure by the total number of data points. Standard Deviation A statistic used to measure the variation in a distribution. Standard deviation is a measure of the spread of data in relation to the mean. It is the most common measure of the variability of a set of data. J. Miller, 4/28/10

14
**Averages and Standard Deviation**

Standard Deviation Example Suppose we wished to find the standard deviation of the data set consisting of the values 3, 7, 7, and 19. Step 1: find the arithmetic mean (average) of 3, 7, 7, and 19 ( ) / 4 = 9. Step 2: find the deviation of each number from the mean, 3 − 9 = − 6 7 − 9 = − 2 19 − 9 = 10. Step 3: square each of the deviations, which amplifies large deviations and makes negative values positive, ( − 6)2 = 36 ( − 2)2 = 4 (10)2 = 100. Step 4: find the mean of those squared deviations, ( ) / 4 = 36. Step 5: take the non-negative square root of the quotient (converting squared units back to regular units), so, the standard deviation of the set is 6. J. Miller, 4/28/10

15
**Standard Deviation Discovered**

Assume: Mean = 30, Std Dev = 6 - 1 s = - 6 - 2 s = 2(-6) = -12 - 3 s = 3(-6) = -18 - 4 s = 4(-6) = -24 - 5 s = 5(-6) = -30 - 6 s = 6(-6) = -36 s -6 -5 -4 -3 -2 -1 30 1 s = 6 2 s = 2(6) = 12 3 s = 3(6) = 18 4 s = 4(6) = 24 5 s = 5(6) = 30 6 s = 6(6) = 36 s 1 2 3 4 5 6 +6 +12 +18 +24 +30 +36 -36 -30 -18 -12 -6 -24 36 42 48 54 60 66 -6 6 12 18 24 J. Miller, 4/28/10

16
**Standard Deviation Discovered**

Assume: Mean = 30, Std Dev = 6 30 36 42 48 54 60 66 -6 6 12 18 24 J. Miller, 4/28/10

17
**Standard Deviation is Critical**

Assume: There are two projects, each using the Three Point Estimate Method Project A Project B Optimistic = Optimistic = 49 Most likely = Most likely = 50 Pessimistic = Pessimistic = 51 50 60 70 80 90 100 10 20 30 40 51 49 Explain the definition Ask the class, ‘What is this symbol?’, referring to sigma. This is a lead in for the next slide. 99 1 Project A Data points are 1, 50 ,99 Mean = 50 StdDev = 49 Project B Data points are 49, 50, 51 Mean = 50 StdDev = 1 J. Miller, 4/28/10

18
**Normal Distribution with Std Dev**

19
**Estimating Methods PMBOK 3rd **

6.4.3 Activity Duration Estimating: Outputs .1 Activity Duration Estimates Activity duration estimates are quantitative assessments of the likely number of work periods that will be required to complete a schedule activity. Activity duration estimates include some indication of the range of possible results. For example: • 2 weeks ± 2 Points to indicate that the schedule activity will take at least eight Points and no more than twelve (assuming a five-Point workweek). • 15 percent probability of exceeding three weeks to indicate a high probability—85 percent—that the schedule activity will take three weeks or less. Let’s take a closer look at making a valid estimate… J. Miller, 4/28/10

20
**Data Points to Data Trends**

J. Miller, 4/28/10

21
**Data Points to Data Trends**

J. Miller, 4/28/10

22
**Data Points to Data Trends**

J. Miller, 4/28/10

23
**Data Points to Data Trends**

J. Miller, 4/28/10

24
**Data Points to Data Trends**

J. Miller, 4/28/10

25
**Data Points to Data Trends**

J. Miller, 4/28/10

26
**Data Points to Data Trends**

J. Miller, 4/28/10

27
**Data Points to Data Trends**

J. Miller, 4/28/10

28
**Data Points to Data Trends**

J. Miller, 4/28/10

29
**Data Points to Data Trends**

J. Miller, 4/28/10

30
**Data Points to Data Trends**

J. Miller, 4/28/10

31
** ? Data Points to Data Trends X UCL LCL P1 P2 P3 P4 P5 P6 P7**

J. Miller, 4/28/10

32
**Data Points to Data Trends**

J. Miller, 4/28/10

33
Process Exercise What about when we don’t have data for all process steps? What if we had a way to estimate the data?

34
**Creating a Control Chart**

Define the metric: time/unit of output, delta of actual vs. planned time or cost, etc Must be homogeneous Data collection planning Who will collect data? What aspect of the process will be measured? Where, or at what point in the process will the measurement be taken? When or how frequently will the data be collected? Why is this sample being taken? How will the data be collected? How many samples will be taken? Collect the data What if data isn’t collected for every project or period?

35
**Creating a Control Chart**

OR As long as the units are the same, the model works!

36
**Creating a Control Chart**

J. Miller, 4/28/10

37
**Creating a Control Chart**

Calculate the centerline – Sum of Means Calculate the Upper and Lower Control Limits. UCL = Sum of Means + 3 σ Sum of Means 541.4 Sum of Variances 503.3 Sq Rt of Variances 22.4 LCL = Sum of Means - 3 σ σ = Sq Rt of Sum of Variances - 3 StdDev - 2 StdDev - 1 StdDev Mean + 1 StdDev + 2 StdDev + 3 StdDev -3* 22.4 -2* 22.4 -1* 22.4 1* 22.4 2* 22.4 3* 22.4 -67.2 -44.8 -22.4 541.4 22.4 44.8 67.2 474.2 496.6 519.0 563.8 586.2 608.6

38
**Creating a Control Chart**

- 3 StdDev - 2 StdDev - 1 StdDev Mean + 1 StdDev + 2 StdDev + 3 StdDev -3* 22.4 -2* 22.4 -1* 22.4 1* 22.4 2* 22.4 3* 22.4 -67.2 -44.8 -22.4 541.4 22.4 44.8 67.2 474.2 496.6 519.0 563.8 586.2 608.6 After calculating the control limits, place the center line and control limits on the chart 541.4 608.6 474.2

39
**Creating a Control Chart**

A Guide to the Project Management Body of Knowledge (PMBOK® Guide) Third Edition 2004 Project Management Institute, Four Campus Boulevard, Newtown Square, PA USA J. Miller, 4/28/10

40
**Determine if the Process is Stable Over Time**

Check the date for trends Check the date for patterns J. Miller, 4/28/10

41
**Process Stable Over Time?**

Learn how to decide if the process is stable and if the data can be used for further analysis. Data is analyzed using a control chart. Point beyond UCL and LCL (beyond 3 sigma) J. Miller, 4/28/10

42
**Process Stable Over Time?**

(2) Seven consecutive points on the same side of center line. J. Miller, 4/28/10

43
**Process Stable Over Time?**

(3) Trend: 6 consecutive points steadily increasing or decreasing: J. Miller, 4/28/10

44
**Process Stable Over Time?**

(4) Repeating pattern and cycles. J. Miller, 4/28/10

45
**Process Stable Over Time?**

Can the above data be used for any further analysis and decision making? Observations: No point is beyond UCL and LCL There is no pattern repetition There is no continuous increase or decrease of 6 data points There is NO 7 consecutive points on the same side of center line So, this data can be used for future predictions J. Miller, 4/28/10

46
**Process Stable Over Time?**

Can the above data be used for any further analysis and decision making Observation : There are points beyond UCL and LCL There is no pattern repetition There is no continuous increase or decrease of 6 data points There is 7 consecutive points on the same side of center line This tells, there is a special cause influencer, which is causing this to happen. Before making any predictions for the future, need to analyze the special cause J. Miller, 4/28/10

47
**Process Stable Over Time?**

Can the above data be used for any further analysis and decision making : Observation : No point is beyond UCL and LCL There is no pattern repetition There is no continuous increase or decrease of 6 data points There is 7 consecutive points on the same side of center line So, Before using the data for future predictions, need to analyze the cause for data points to be on same side (during the period) and if required separate those points from the rest, in making the decision J. Miller, 4/28/10

48
**Determine if the Process is Stable Over Time**

Check the date for trends Check the date for patterns Apply Lessons from the Funnel Experiment Rule 1: Leave the funnel fixed over the target. Why don't we simply adjust the funnel after each drop so the next drop will be closer to the target? Don’t change the estimating process, accept the variation, the difference between the estimate and actual projects. Rule 2: For every drop, the marble will come to rest a distance "z" from the target. Rule 2 is to move the funnel a distance -z from its last position. Move the funnel based on the funnels last position. Chang the estimating process (metrics) after each project or difference between estimate and actual. Rule 3: Move the funnel a distance -z from the target after each drop of the marble that ends up a distance z from the target. Note that Rule 2 moves the funnel based on the funnel's last position. Rule 3 moves the funnel a distance from the target. Rule 4: Rule 4 is simply to set the funnel over where the last drop came to rest. J. Miller, 4/28/10

49
**Contact Questions? Using Excel to Avoid Common Mistakes John H. Miller**

J. Miller, 4/28/10

Similar presentations

Presentation is loading. Please wait....

OK

Quality Assurance (Quality Control)

Quality Assurance (Quality Control)

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on job interview skills Ppt on team building training module Ppt on earth hour wikipedia Ppt on formal education articles Ppt on unity in diversity religion Ppt on abstract art prints Ppt on face recognition technology using matlab Read ppt on iphone Ppt on road signs Encyclopedia ppt one time presentation