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Behind Schedule? Escape the Risk with Monte-Carlo

Every project of any consequence has some exposure to risks. While specific risks might have a direct impact on cost, quality, scope, or compliance with external requirements, risks that impact one or more of these success factors usually also impact schedule. And since, schedule drives at least some costs for most projects, it can be very helpful to quantify schedule risk, to justify contingency funding and drive other risk management decisions.

Schedule Baselines and Critical Paths

Time and cost estimates are normally subject to a margin of error. It is a good practice to produce a three-point estimate—best-case, most likely, and worst-case—for each activity that will be a part of the project schedule. It is common to create an initial schedule using the most likely duration and the “ideal” resource for each task, sequencing the tasks based on previously identified dependencies. This will allow you to identify the sequence of tasks that most likely represents the critical path. It will also help to identify resource constraints. The initial pass should be baselined, so it can serve as a point of reference for assessing progress during execution.

Monte Carlo Simulations

The baseline schedule is used when conducting Monte Carlo simulations. The Monte Carlo technique simulates project execution by recording a random duration between the best-case and worst-case for each task, iterated hundreds or even thousands of times. Each iteration reflects “one possible future” for the project, and helps to identify variations in total duration, alternative critical paths, possible resource conflicts, and cost variations. Monte Carlo simulations can identify tasks that might be on the critical path, if predecessor tasks are delayed. They can also measure the correlation between activity duration and cost and total project duration and cost.

Using Data from Simulations

Monte Carlo data can be helpful in selecting milestones and checkpoints for management review during project execution. It can also quantify the various contributing factors to variations in schedule. It is not uncommon for a simulation run to identify specific activities that add significantly to overall project schedule risk. These activities can be re-assessed to identify alternative approaches, reduce variability, or even remove them from the scope of the project. It is also possible to identify resources that might be over-subscribed at various points in time so that alternative staffing can be arranged. Each of these variations can be assessed as schedule risks and appropriate strategies identified and monitoring procedures adopted.

Running Additional Simulations

Once you have the data from your initial analysis, it can be useful to update your Monte Carlo model at one or more project milestones. Completed activities can be updated with the actual time to complete; the best-case and worst-case estimates can be updated for those activities already under way, and the simulations re-run. For most projects, uncertainty and variability are reduced as the project progresses. It can be helpful to brief the sponsor and key stakeholders on the residual risk and the effectiveness of the risk management measures you’ve already taken. Nothing encourages decision makers to act decisively on future problems like hard data on the results of their past decisions.

The more activities encompassed by the project, the greater the potential for variability in the overall project schedule and cost. The data generated by Monte Carlo simulations can be used to drive decisions ranging from the contingency budget to who will work on the project, and even whether to delay or cancel the project. Consider developing a Monte Carlo simulation when planning your next project.

For more brilliant insights, check out Dave’s blog: The Practicing IT Project Manager

Are you involved in a data conversion project? Then check out Dave’s indispensable book: The Data Conversion Cycle

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