A couple of weeks ago I posted on some of the anomalies in CPM logic that will cause unexpected results: CPM Scheduling – the logical way to error #1. A comment on the post by Santosh Bhat started me thinking about the effect of these logical constructs on risk analysis.
The various arrangement of activities and links shown in CPM Scheduling – the logical way to error #1 (with the addition of a few more non-controlling links) follow all of the scheduling rules tested by DCMA and other assessments. The problem is when you change the duration of a critical activity, there is either no effect or the reverse effect on the overall schedule duration.
In this example, the change in the overall project duration is the exact opposite of the change in the duration of Activity B (read the previous post for a more detailed explanation). For this discussion, it is sufficient to know that an increase of 2 weeks in the duration of ‘B’ results in a reduction of the overall project duration of 2 weeks (and vice-versa).
The effect these anomalies on the voracity of a Monte Carlo analysis is significant. The essence of Monte Carlo is to analyze a schedule 100s of times using different activity durations selected from a pre-determined range that represents the uncertainty associated with each of the identified risks in a schedule. If the risk event occurs, or is more serious, the affected activity duration in increased appropriately (see more on Monte Carlo).
In addition to calculating the probability of completing by any particular date, most Monte Carlo tools also generate tornado charts showing the comparative significance of each risk included in the analysis and its effect on the overall calculation. For example, listing the risks that have the strongest correlation between the event occurring and the project being delayed.
Tornado charts help the project’s management to focus on mitigating the most significant risks.
When a risk is associated with an activity that causes on of the anomalies outlined in CPM Scheduling – the logical way to error #1 the consequence is a reduction in the accuracy of the overall probability assessments, and more importantly to reduce the significance of the risk in tornado charts. The outcome of the anomalous modelling is to challenge the fundamental basis of Monte Carlo. There are more examples of similar logical inconsistencies, that will devalue Monte Carlo analysis, included in Section 3.5 of Easy CPM.
Easy CPM is designed for schedulers that know how to operate the tools efficiently, and are looking to lift their skills to the next level. The book is available for preview, purchase (price $35), and immediate download, from: https://mosaicprojects.com.au/shop-easy-cpm.php