1. Schedule Metrics – Beyond the
Ordinary
NASA Project Management Challenge 2007
John Krahula/PM Metrics
2. What is a Schedule
► Model/Simulation of the process leading to
the creation of a desired event or
deliverable
► Source of important management
information
► Repository of historic information for
contractual purposes and for creating the
subsequent schedules
Copyright 2006 John Krahula and PM Metrics 2
3. What Makes a Good Schedule
► Properly Structured
Activity Type Usage
Durations, Constraint Use, Logic
Follows your Scheduling Business rules
► Effective and appropriate Statusing Process
► Effective Coding Strategy
WBS is only the beginning
► Effective Measurement/Reporting Strategy
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4. Performance Measurement/Metrics
► Schedules Generate Tons of Information, What is
Relevant, Appropriate, In Context.
► Levels
Schedule – Validate the Schedule as a Tool
Project – Validate the Project Success
► Types
Structural (S) – Schedule Development/Statusing
Progress/Status (S/P)
Code/Calculated/Management (Key Performance
Indicators etc.) (P)
► Filter What is Measured
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5. Metrics in Perspective/Context
► Snapshot Metrics
Description of the Current Situation
► Trends
Analysis of Values over time
Trend of Current Period/Snapshot Metrics
Examine Cumulative Values – History
Time phased Metrics
► Measurement Strategy Matches Project
Not all Projects are the Same
PM ROI
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6. Trip Levels
Step 1 – Set Up Rules and
Triggers to Highlight Areas for
Analysis
12. The High Level View
► Triage
► Red is Bad
► Validate Statusing or Schedule Structure
► Drill down to a lower level of Detail.
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13. Find Details, Weights, Those
Responsible
Step 4 – Sort through the detail, find what is
important and communicate with those
responsible
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29. More Trends and Timephased
Trends
► Look at What Happens over time.
► Combine different Metrics
► Defensive Metrics – Use Metrics to tell a
story.
Copyright 2006 John Krahula and PM Metrics 29
30. Comparing Baseline and Forecast
Distributions Give Idea of Weight
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31. Baseline Vs. Forecast/Actual
Baseline vs. Forecast Finishes
2500
Cummulatvie Finishe
2000
1500 bfinishc
1000 ffinishc
500
0
25-Aug-05
01-Jul-04
27-Jul-06
08-Apr-04
02-Jun-05
15-Jan-04
23-Sep-04
10-Mar-05
17-Nov-05
09-Feb-06
04-May-06
19-Oct-06
16-Dec-04
Time
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32. Milestone Finishes
15
-J
a
26 n-0
0
2
4
6
8
10
12
14
16
18
20
-F 4
e
08 b-0
-A 4
p
20 r-0
-M 4
ay
01 -04
-J
12 ul-
-A 04
u
23 g-0
-S 4
e
04 p-0
-N 4
o
16 v-0
-D 4
e
27 c-0
-J 4
a
10 n-0
-M 5
a
21 r-0
-A 5
p
02 r-0
-J 5
un
14 -05
-J
25 ul-0
-A 5
u
06 g-0
-O 5
17 ct-0
-N 5
o
Milestones By Week
29 v-0
-D 5
e
09 c-0
-F 5
e
23 b-0
-M 6
Copyright 2006 John Krahula and PM Metrics
04 ar-0
-M 6
a
15 y-0
-J 6
un
27 -06
-J
07 ul-
-S 06
e
19 p-0
-O 6
Milestones By Time
30 ct-0
Subcontract MS, Givers/Receivers, Lower Level Events
-N 6
ov
-0
6
bccm
32
33. Tasks Active Weekly
0
10
20
30
40
50
60
70
80
15-Jan-04
19-Feb-04
25-Mar-04
29-Apr-04
03-Jun-04
08-Jul-04
12-Aug-04
16-Sep-04
21-Oct-04
25-Nov-04
30-Dec-04
03-Feb-05
10-Mar-05
14-Apr-05
19-May-05
23-Jun-05
28-Jul-05
01-Sep-05
Task Density
06-Oct-05
10-Nov-05
15-Dec-05
19-Jan-06
23-Feb-06
Copyright 2006 John Krahula and PM Metrics
30-Mar-06
Task Density
04-May-06
08-Jun-06
How many tasks are in play at any one time
13-Jul-06
17-Aug-06
21-Sep-06
26-Oct-06
30-Nov-06
04-Jan-07
bcct
33
34. Task Density for 1 WBS Element
Task Density WBS: 01020312
7
6
5
N me o T s s
u br f a k
4
3
2
1
0
1 2 3 4 5 6 7 8
Month
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35. Is –DV Good or Bad
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39. When are We Tackling the Big Boys?
Average Finish Variance of Late Tasks
40
35
30
F is V ria c D y
25
in h a n e a
20
15
10
5
0
1 2 3 4 5 6 7 8
Week No.
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40. Did a RW and Now…
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41. But – Greatly Reducing Lag Use
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42. But 3X Number of Tasks/MS
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43. Task Duration Down – Lower level of
Detail
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44. So What Happened
► Went to a Lower Level of Detail
Smaller Durations
More Tasks and Milestones
► Got away from using lags
► This is an IMS Level Schedule, many
represent interactions between IPTs
► Tons of Spec Reviews etc.
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50. Conclusion
► There is a lot of Data in a schedule
► Find the right measurement strategy
► Look for answers, but also more important,
look for the questions
Contact PM Metrics at johnmtnair@aol.com or
info@pmmetrics.com
Copyright 2006 John Krahula and PM Metrics 50
52. Activities
► Types
of Activities, Tasks, Milestones, and
Summary
Tasks may represent work packages or more likely EV
Milestones
Milestones may be part of a strategy for performance
measurement
Could use Summary Activities for Work Packages or Cost
Accounts, WBS Elements, Etc.
► Start, Finish, and Isolated Activities give indication
of linking in schedule and/or number of
Deliverables.
► Summary Logic is generally not acceptable
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53. Relationships
► Ratio gives indication of linking
► Ratio Type usage important in determining
structure health of schedule and possible
hiding of lateness (FS to SS or FF)
► Beware of Schedulers using SF
► The Great Debate: Lags Versus Constraints
► Neg Lags can help model Total Slack Better
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54. Constraints
► Honor Constraints Option is Dangerous
► Hard Constraints Should be used Sparingly
Use for Deliverables
Deadlines in MS Project can act as Hard
Constraints
► Develop a strategy for using soft constraints
Logic still wins
Ersatz Resources – Used to Model Resource
Availability
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55. Progress
► Numbers of Complete, In Progress and
Planned Activities
► Missing Baselines
► Should have Started/Finished – Tasks
unstatused before the Status Date
► Future Status – Out of Sequence Status
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56. Duration and Duration Variance
► Long Duration Tasks
► >0 Duration Variance are tasks that have
taken longer than expected
► <0 Duration Variance are tasks that have
completed sooner than expected
► Duration Variance useful for History
► Useful for Dynamic/Radical/Agile PM
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57. Finish Variance
► Used Along with Total Slack for standard
analysis of schedules
► Have User Definable Distribution
► Finish Variance calculated on Interim Dates
helps gauge performance if Baseline not
relevant.
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58. Total Slack and Free Slack
► Large values show missing relationships
► Negative values show missed Deliverables
or delay of entire project
► Standard method for identifying problem
areas
► Depends on Constraints Being Properly Used
► Decreasing Free Slack means compressing
schedule.
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59. Distributions
► Start Variance, Finish Variance, Duration
Variance, Total Slack and Lags
► Duration Distribution gives an idea of how
discrete you are planned
► Gives idea of scope of problems rather than
just the long pole
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60. Chokepoints
► Breakpoints are an analysis of how many
relationships a task has
► As with important reviews etc., tasks with
many relationships are important to track
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61. Other Information
► Subprojects
► Task Calendars
► Deadlines
► Elapsed Duration
► Estimated Duration
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62. Averages
► Average Durations give an idea of
granularity of the tasks.
► Changes/Trends of averages can show
degradation or the turning around of a
project.
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63. Baseline Metrics
► Determine what should have been worked
► Started/Finished on Exact Day
► Started/Finished within Status Period
► Start Early/Finish Early
► What was Not Started or Finished
► Previous Lates and Healed
► Results by Activity Type
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64. Forecast Date Metrics
► Metrics based on Forecast Dates
► Metrics for both Starts and Finishes
► Metrics results by Activity Type
► Negative Differences mean tasks not
completed and not re-forecast
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65. Previous Status Period
► How is the schedule doing week to week or
status period to status period
► Starts/Finishes Within Status Period
► Actual Starts/Finishes within Status Period
► Start Early/Finish Early
► By Activity Type
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66. Task Density
► Concurrent tasks are all tasks scheduled
during a time period.
► The more tasks in the works during a
period, the greater chance of not meeting
deliverables
► How many tasks can we effectively manage,
do we have structure problems?
► Defining the Bow Wave
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