In the world of project management, schedule and cost are two key metrics on any project. When planning a project, the project team tries to forecast how long it will take to complete and how much it will cost. When the team creates a baseline schedule and a baseline cost estimate, they are creating a standard by which the project’s performance will be measured. The baseline schedule will have a project completion date and a critical path. The cost estimate will have estimated costs for each work package in the work breakdown structure; those costs will roll up into a single number, which is the estimated project cost.
When the project team creates a baseline cost and schedule which forecast, respectively, a total dollar amount and duration for the project, these are called deterministic estimates and schedules. By “deterministic,” we mean a single cost or completion date is determined.
When we risk a schedule or a cost estimate, we usually start with the deterministic schedule at level two, which is comprised of a few hundred activities and the key costs and resources for the project. The key activities and costs are “ranged,” which is to say subject matter experts opine as to the likelihood of the best, worst, and most likely outcome for each activity’s duration and cost in the deterministic schedule and cost estimate.
Then, this static model of cost and schedule is, in essence, animated. Using a statistical method such as Monte Carlo or Latin Hypercube, the deterministic schedule is then iterated. The process of iteration involves the statistically random selection of costs and durations from the ranges for each activity and cost. Once selected, the new durations and costs for each activity are applied and a new realization of the scheduled is created. Over a number of iterations, the number varies depending on the schedule; we reach “convergence” – the point at which further iterations produce essentially the same result.
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So far, it seems like the title of this blog entry is wrong, because so far schedule risk and cost risk are exactly the same. However, during the application of ranged durations in the Monte Carlo analysis, duration changes impact every activity to which they are logically linked in the schedule network, whereas cost changes are most often independent.
It’s almost as though schedule durations are dependent variables, while costs are independent variables. One duration change can impact many activities in the network. If an activity is on the critical path, and the duration in a particular iteration is greater than the deterministic date of the baseline schedule, the project completion will be extended. Additionally, depending on work calendars, it may be that a one-day change in a single activity could cascade into weeks of extended schedule by pushing downstream activities into non-working periods.
This is why building a high quality deterministic schedule and carefully ranging the activities prior to simulation is so important. And that is why schedule and cost simulations are so different.