Schedule Management Techniques For
Complex Projects
W. Scott Nainis
Noblis, Inc.
August 12, 2009
Project Management Seminar Series 2009
Today’s Agenda
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Motivation for the topic
Why do many projects get behind (and cost more)?
How do we track project progress?
Role of Earned Value
Transition to Earned Schedule
How can we be both Pessimistic and Optimistic at the Same Time?
How can Event Chains (and similar simulation approaches) help us?
Power of Synergy –
– How can ES and Event Chain work together?
– How is the schedule management article related to
other articles within the SIGMA PMO Edition?
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Project Management Seminar Series 2009
Motivation for the Topic
 Historic value of quantitative methods for project management – role of management
science/operations research (MS/OR)
– PERT (Program Evaluation and Review Technique)
– CPM (Critical Path Method)
– Network and Optimization (linear programming, dynamic programming, simulation, etc.)
 What has MS/OR done for project management lately?
– Project management tools (e.g. MS Project) have incorporated many of the MS/OR
quantitative methods
– Simulation (Monte Carlo analysis, simulation-based training, what-if analysis) has been
an active area for development
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Project Management Seminar Series 2009
Motivation for the Topic (Concluded)
 What is still one of the biggest problem areas in project management –
project schedule
– Projects come in very late or never
(61% IT projects fail / 78% are late or over budget)
– Project costs and project quality often suffers
 What techniques and approaches can support project schedule
management?
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Project Management Seminar Series 2009
Why do Many Projects get Behind (and cost more)?
 Overly optimistic project schedules
– Human nature
– Political pressures
 Lack of effective responses to project problems as they occur
– Need to anticipate
– Time and cost to implement
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Why are We Overly Optimistic in Project
Estimation?
 Human nature tends to overestimate achievement and tends to forget negative
outcomes
– Daniel Kahneman and Amos Tversky performed research into the psychological
underpinning of such biases (Kahneman received the Nobel Prize in 2002 partially for
these theories)
– Research has shown that people estimate overly-optimistically even in spite of contrary
evidence
 Political forces apply pressure for optimistic forecasts even if planners are aware
of the risks and less optimistic
– Pressure from supervisors and peers
– Decision-making forces optimistic forecasting (e.g. competitive contracts)
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Project Management Seminar Series 2009
Over-Optimism and Political Pressure Lead to
Unrealistic Project Schedules
 Project Managers take the optimistic, shortest estimate
 Project issues during execution are ignored
– Lengthen planned schedule
– Raise costs and lower cost-benefit assessment
– Raise issues that need to be resolved
– Not prepared ahead of time for many contingencies
 Don’t Forget Plain Old Incompetence
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Project Management Seminar Series 2009
Example: Boston’s “Big Dig”
 Boston wanted to submerge the “Central Artery”- an elevated highway that bifurcated
the city for nearly 50 years.
 Serious planning started around 1980
 By 1985 the estimate for the work was:
– Project length 10 years
– Project cost 2.8 Billion dollars
 Work “concluded” December 31st 2007
– Project length 22 years
– Project cost $14.6 Billion plus about
$7 billion in interest for a total
of nearly $22 billion
– Still not done, definitely not
the litigation!
not
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Alternative Methods for Project Forecasting
 Concept of “insider” forecasting versus “outsider” forecasting
 Developed in 2006 to the concept of Reference Class Forecasts
– Use of real data from similar projects
– Become aware of what can actually go wrong with complex projects
– Take into account the “distributional” nature of project activities, impacts and
results
– Allow for input and appraisal from those who are not “too close” to the project
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Project Management Seminar Series 2009
Alternative Methods for Project Forecasting
(concluded)
 Parametric Software Project Cost and Schedule Estimating Techniques
– COCOMO II, CoStar, Cost Modeler, CostXpert, Knowledge Plan, PRICE S,
SEER, SLIM, and SoftCost
– The above methods have aspects of being reference-based approaches
– How good is the data? Will it be used fairly?
 Heuristic: “Task-based” versus “Time/Support-based” estimation –
“Collective Wisdom”
 Use of simulation-based project management tools
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Heuristic Scheduling Example
 Small Project budget estimation
– Simple Data Analysis and Reporting Project of Four tasks:
“Task-based” Approach
Staff
A – Main Investigator
B – Right-hand support
C - Oversight Manger
D - Date Collector
E – Data Collector
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Task 1: Develop Data Collection Plan (Staff A and B - 40 hours each, Staff C – 10 hours)
Task 2: Collect Data (Staff B, D, and E - 80 hours each)
Task 3: Analyze Data (Staff A and B - 80 hours each)
Task 4: Produce Results Presentation Report and Deliver Report (Staff A and B - 60
hours each, Staff C - 15 hours)
• Total Staff Hours = 625 hours + 10% contingency = 690 hours
• Placing Tasks End-to-End would result in 2.5 month schedule, rounded up to 3
months.
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Project Management Seminar Series 2009
Heuristic Scheduling Example (Concluded)
“Time/Support-based” Approach
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Experience tells us this is no less than a four month project
Staff A is the project leader day-to-day – 70% of time required
Staff B is the other main on-going support person – 50% of time required
Staff C is the oversight senior manager – 10% of time required
Staff members D and E are focused on data collection – 50% of time required over a 1.5 month
window
Assume 158 hours available per staff per average month
Allocation: Staff A – 440 hours, Staff B – 320 hours, Staff C – 60 hours, Staff D and E – 120
hours each = total 1,060 hours.
About 50% greater hours than the “Task-based” approach, 33% -38% longer schedule
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Project Management Seminar Series 2009
How do We Track Project Progress?
 Start with a base-line project schedule
 Project subtasks and milestones completed
 Keep track of project expenditures compared to project budgets and credit
for task completed
 Keep track of change control status and map back to current schedule
estimates – may not be that apparent
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Project Management Seminar Series 2009
Role of Earned Value Management
 Earned Value Management (EVM) has developed over the years as an
important approach to management of both project budget and schedule
– Track project for budgeted versus actual expenditures
– Use the metrics from project financial measures to track project progress
– Required by OMB for most software projects
• OMB Circular A-11, Part 7 (ANSI/EIA Standard 748) 7
– Time is typically not an explicitly tracked quantity
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Project Management Seminar Series 2009
Earned Value and Schedule Performance
Earned
Value
(EV) = 48
at week 10
Schedule Variance (SV) = 48 – 71 = -23
Schedule Performance Indicator (SPI)
= 48/71 = 0.68
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Project Management Seminar Series 2009
Earned Value and Schedule
Performance (Continued)
CV= 48 – 79 = -31
Earned Value and Cost Performance
CPI = 48/79 = 0.61
CV= 48 – 48 = 0
CPI = 48/48 = 1.00
Alternative
ACWP
SV and SPI still as before.
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Earned Value and Schedule Performance
(Continued)
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Project Management Seminar Series 2009
Earned Value and Schedule Performance
(Continued)
SV reaches –45 and then goes to 0 at the end of the project
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Project Management Seminar Series 2009
Earned Value and Schedule Performance
(Concluded)
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Project Management Seminar Series 2009
Earned Value and Schedule Performance
(Concluded)
Schedule Delay
SPI reaches a low of 0.72 but then tends back to 1.0
as the project completes 7.5 months late!
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Project Management Seminar Series 2009
What is Earned Schedule?
 Simple, but elegant concept
 Uses EVM data to produce a more useful index of project schedule status
 Devised in 2003 by Walter Lipke, software project manager who has
pioneered the use of EVM for software development project management
 Empirical studies found Earned Schedule (ES) to be a superior predictor of
project schedule and completion
www.earnedschedule.com
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Project Management Seminar Series 2009
Calculating Earned Schedule (ES)
ES = 7 (first 7 weeks of schedule progress) + Portion of week seven
accomplished [(48-45)/(54-45)=0.33] = 7.33 weeks
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Project Management Seminar Series 2009
How Can We be Both Pessimistic and Optimistic at the
Same Time?
 Monte Carlo simulation analysis allows us to consider reference class
forecasting
– Distributional impacts on activities duration and cost
– Takes into account the interaction of project activity events
– Leads to longer, more costly and pessimistic forecasts
 Need a way to counter-balance the pessimistic trends with Monte Carlo
simulation
– Consider risk moderation responses
– “What if?” responses considered ahead of time
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Basically We Need to Establish a Risk Analysis Exercise
During Project Planning and Continue It During
Project Execution
Source: Jane Powanda, Noblis, Inc.
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How can the Event Chain Method help us?
 An external event can occurs which impacts the status of one or more project
activities
 In response to the first event subsequent events are triggered to respond to the
effects of the first event
 Event Chains are established and simulation software is used to track and manage
all the events across the project activities
 Interventions included in response events attempt to modify and manage the
inherent risk to the project
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Project Management Seminar Series 2009
Project Activities Can be Linked Through an
Event Chain
• Events can be external and
autonomous – a Triggering Event
• Event can be in response to a
Triggering event
Excited
State
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Project Management Seminar Series 2009
Event Chains Can Initiate Mitigation Plans
Example - Trigger
Event: Machine tools
found to be out of
specification, yielding
lower quality output and
lower throughput.
Triggered Event Response:
Machine tools inspected,
recalibrated and
repaired/replaced if necessary.
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Project Management Seminar Series 2009
Features Useful to Support Event Chain
Method – Wish List
 Provide classic project management scheduling reporting and resource
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management capabilities
Incorporate and interface with major project management scheduling software (e.g.
MS Project, Primavera, etc.)
Handle development and management of event chains
Allow for interaction of triggering events and responsive events impacting one or
more project activities and their associated resources
Be capable of supporting Monte Carlo Analysis and statistical results reporting
Support project resource utilization and activity completion accounting
Support EVM maintenance
Allow for project branching due to event occurrence
Allow for re-baselining and maintenance of all project accounts for each baseline
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Project Management Seminar Series 2009
Possible Software Candidates for Supporting
Event Chain Method
 Microsoft Project
– Standard for many users
– Does project scheduling and tracks activities and resources
– Supports critical path determination
– Does not support statistical simulation/Monte Carlo analysis directly
 @ Risk for Microsoft Project
– Add-on to Microsoft Project
– Performs simulation/Monte Carlo analysis to obtain distribution impact of project and
resource variability
– Does not handle event chain methods
 Primavera Risk Analysis
– Works with Primavera PM Software
– Performs a fully capable risk analysis along with project scheduling and other PM
functions
– Fully capable statistical simulation / Monte Carlo analysis with incorporated schedule
analytics
– Full reporting with statistical information and all project financial assessment measures
– Works with Primavera EVM module
– Event chain methods can be formulated
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Project Management Seminar Series 2009
Possible Software Candidates for Supporting
Event Chain Method (Concluded)
 ProChain
– Designed to work with MS Project and replace the MS Project scheduler
– Performs analysis to determine “critical chain” situations which are similar to
event chains (Goldratt)
– No statistical simulation/ Monte Carlo analysis
 Risky Project
– Can be used stand-alone as a project management planning tool
– Can be used and interface with MS Project, Primavera and other PM software
packages
– Designed for event chain modeling
– Supports statistical simulation/ Monte Carlo analysis
– Performs detailed resource and activity accounting and support EVM
calculations
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Project Management Seminar Series 2009
Power of Synergy – How can Earned Schedule and Event Chain Work
Together?
Step 1. The project team develops the work breakdown
Structure (WBS) and lays out project plan
with resources and durations.
EVM accounting is put in place along with ES.
Step 2. A second team or sub-team group takes plan
and introduces risk elements to activities.
Identifies negative impact areas.
Both teams consider response events
to mitigate or avoid risk effects.
Step 3. Both teams work to develop an event chain structure
incorporating all information known to date.
New plan with incorporated event chains
is run to finalize the project schedule and costs.
Step 4. Original Project team continues to monitor and
manage project execution.
Implements planned event responses as necessary.
Can deviate and modify the event chains as events unfold.
Completed activities are documented.
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Project Management Seminar Series 2009
Power of Synergy –Schedule Management and the other SIGMA PMO
Articles
The Modern Program Office: New Goals,
New Organization
by Michael D. Nelson and Shawn J. Margolis
• 61% IT projects fail / 78% are late or over budget
• Project leadership differs from project management
• Schedule management needs both
• The PMO can be the source of expertise and knowledge to
support improved schedule management approaches
Toward Best-Practice Management
by Robert G. Vorthman, Jr.
• Many methods, templates and practices in PM are mentioned
• Some relate to schedule management, particularly risk analysis
• Monte Carlo and simulation cited as less useful, but what
does this information from Bresner and Hobbs mean?
Toolkit for Federal Information Technology
Project Managers
by Brian H. Price and David W. Vera
Managing Mutiple Information Technlogy Projects:
Lessons Learned
by Daphne B. Byron and Chip Steiner
• Project tracking knowledge and response essential
• Must understand how change control impacts schedule
• Existing EVM schedule indices not as useful, suggests ES
Using Six Sigma in Project Forensics
by John K. Stevenson and Frederick W. James
• Looking for project “defects” after the fact
• Uses DMAIC* framework
• Found project forecasting and unrealistic project schedule
defects
• Also found project experience and requirements
development defects
• Devised an integrated approach to financial management/
investment control and the SDLC
• Linkage to proper IT support roles
• Schedule management approaches must be consistent
with financial and resource requirements
The Case for Agile Management
by John E. Freeman
• “Plan-driven” PMO may not be responsive enough
• Agile PM looks for internal initiative and controls, and
flexible responses
• Schedule management can take advantage of pre-planned
knowledge, yet be responsive to continuous
learning and adjustment – point of operation mid-way between
agile management and the “plan-driven” PMO
* Design-Measure-Analyze-Identify-Control
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The Reality of Project Management Practice
Besner, C. and Hobbs, B. (2004), University of Quebec
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Project Management Seminar Series 2009
The Reality of Project Management Practice
Besner, C. and Hobbs, B. (2004), University of Quebec
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