Critical path finder8/2/2023 ![]() ![]() ![]() Rather than team members having to brainstorm from a blank sheet of paper, they can take into account previously realized risks and opportunities from similar historical projects. To fully capitalize from the benefits of AI, team members are provided a list of suggested risks from the company’s knowledge library repository. So instead of identifying risks in isolation of the schedule and then trying to embed them back in, why not provide an environment where risks are both identified and scored directly in context of the schedule itself? Without overstating, this process is treacherous at best and one that causes huge challenges in project risk workshops. Where the modeling challenge arises is in the mapping of those identified risks from the register into the schedule risk model. Risk registers themselves are fundamentally sound and well proven in the field. Traditionally, risk events have been tracked in what is known as a project risk register. In addition to more efficiently capturing duration ranges through the approach described above, the second step in the risk model building process is to capture and quantify risk events. Using Artificial Intelligence (AI) to Help Establish Your Risk Register To date, the problem hasn’t been in the mathematical modeling, but more with software solutions not making the risk and uncertainty capturing process more meaningful to the project team. Again, while this is mathematically sound, getting a team member or discipline lead to define such a range in the form of, say, a 3-point triangular or 2-point uniform distribution quickly leads to you being marched out of the room in error. This range is typically defined using what is known as a 3-point estimate comprising a minimum, most likely and maximum value. In each simulation iteration, a given duration is selected from a range of values and applied to activities in the CPM schedule. What has been an ongoing challenge, though, is how best to capture and model the inputs needed for a Monte Carlo simulation. The mathematics behind Monte Carlo is simple and defendable. In the past decade, risk analysis, specifically in the form of Monte Carlo simulation, has become widely accepted as a means of moving from ‘best-case’ planning to ‘most-likely.’ In simple terms, the Monte Carlo analysis simulates a very high number of potential project outcomes accounting for the huge number of possible variations in activity durations. How Risk Analysis via Critical Path Method Helps If we march our project to a best-case target, we are much more likely to fail as we are unfairly benchmarking against a highly unlikely outcome. The downside to this, though, is that we then end up with a best-case forecast rather than a most-likely forecast. ![]() It’s easier for us to forecast by not taking into account these variables and simply assume everything will work out fine. It’s no wonder, then, that CPM plans suffer from poor accuracy. External factors such as weather or availability of materialsĪll of these drive uncertainty and variability of duration.How much work is there to do and what are the quantities involved?.Why? Well, the problem lies with the fact there are multiple influencers on duration: Historically, modeling sequence has not been the biggest bottleneck in planning - that falls under “forecasting durations.”Īccurately forecasting activity durations is just plain difficult - period. Knowledge of such sequencing typically resides with the expertise of the field execution team through their experience on prior projects. In many ways, defining such a sequence is easier than determining durations as it is a simple, logical definition of when scope may be built. These ties establish a relationship between activities and define hard rules as to the order of operations e.g., “we can’t lay the decking before we have completed the underlying structure.” In a CPM schedule, sequence of work is modeled by linking activities together using logic ties. But at the end of the day, the CPM-forecasted project completion is entirely driven by sequence of work and how long this work will take. Of course, there are some additional layers of complexity involved such as working calendars, critical and non-critical path(s) and associated float, etc. Critical Path Method (CPM) is based on a very simple premise: break down the scope of a project into activities estimate how long these activities will take link these into a sequence and from this you can calculate the total duration of all work leading to project completion. ![]()
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