Which method uses probabilistic techniques to handle duration uncertainty?

Which method uses probabilistic techniques to handle duration uncertainty?

• A. CPM.
• B. Critical Chain.
• C. PERT and Monte Carlo simulations.
• D. Resource leveling.

Answer: (C)

When you’re dealing with duration uncertainty in a project schedule, you want approaches that explicitly model that uncertainty rather than fix every time. One approach, PERT, uses three-point estimates—optimistic, most likely, and pessimistic—so each activity duration is represented by a probability distribution. From those estimates you get an expected duration and a measure of variability, which lets you assess how likely it is that the schedule will meet a target date. Monte Carlo simulations take this a step further by repeatedly sampling random durations from those distributions for all activities and running the project model many times. This builds a full probability distribution of the project completion time, so you can estimate the chance of finishing by any date, quantify risk, and set more informed buffers or contingency plans. The other options don’t inherently handle duration uncertainty with probabilistic modeling: CPM uses fixed, deterministic durations; Resource leveling focuses on aligning resource use rather than modeling duration risk; Critical Chain emphasizes buffers and resource constraints but isn’t primarily a probabilistic duration technique.

When you’re dealing with duration uncertainty in a project schedule, you want approaches that explicitly model that uncertainty rather than fix every time. One approach, PERT, uses three-point estimates—optimistic, most likely, and pessimistic—so each activity duration is represented by a probability distribution. From those estimates you get an expected duration and a measure of variability, which lets you assess how likely it is that the schedule will meet a target date.

Monte Carlo simulations take this a step further by repeatedly sampling random durations from those distributions for all activities and running the project model many times. This builds a full probability distribution of the project completion time, so you can estimate the chance of finishing by any date, quantify risk, and set more informed buffers or contingency plans.

The other options don’t inherently handle duration uncertainty with probabilistic modeling: CPM uses fixed, deterministic durations; Resource leveling focuses on aligning resource use rather than modeling duration risk; Critical Chain emphasizes buffers and resource constraints but isn’t primarily a probabilistic duration technique.