Schedule Management Techniques For Complex Projects

1. Schedule Management Techniques For Complex Projects W. Scott Nainis Noblis, Inc. August 12, 2009

2. 2 Today�s Agenda 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?

3. 3 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

4. 4 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?

5. 5 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

6. 6 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)

7. 7 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

8. 8 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 not the litigation!

9. 9 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

10. 10 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

11. 11 Heuristic Scheduling Example Small Project budget estimation Simple Data Analysis and Reporting Project of Four tasks: �Task-based� Approach 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.

12. 12 Heuristic Scheduling Example (Concluded) �Time/Support-based� Approach 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

13. 13 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

14. 14 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

15. 15 Earned Value and Schedule Performance

16. 16

17. 17 Earned Value and Schedule Performance (Continued)

18. 18 Earned Value and Schedule Performance (Continued)

19. 19 Earned Value and Schedule Performance (Concluded)

20. 20 Earned Value and Schedule Performance (Concluded)

21. 21 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

22. 22 Calculating Earned Schedule (ES)

23. 23 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

24. 24 Basically We Need to Establish a Risk Analysis Exercise During Project Planning and Continue It During Project Execution

25. 25 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

26. 26 Project Activities Can be Linked Through an Event Chain

27. 27 Event Chains Can Initiate Mitigation Plans

28. 28 Features Useful to Support Event Chain Method � Wish List Provide classic project management scheduling reporting and resource 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

29. 29 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

30. 30 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

31. 31 Power of Synergy � How can Earned Schedule and Event Chain Work Together?

32. 32 Power of Synergy �Schedule Management and the other SIGMA PMO Articles

33. 33 The Reality of Project Management Practice Besner, C. and Hobbs, B. (2004), University of Quebec

34. 34 The Reality of Project Management Practice Besner, C. and Hobbs, B. (2004), University of Quebec