Motivated by the economic need to have resources, 100% utilised causes unintentional client congestion and excessive project delays. 5 min read.
Project Services Congestion
Our previous blog, Project Services Queuing Model, identified uncertain project arrival and delivery times result in long queues and wait times that frustrate clients even when resources are utilised less than 100%. This occurs when the project arrival rate into a project organisation exceeds the project delivery rate at that point in time. Intuitively this makes sense. What is less well known is how congestion is affected by project resource utilisation.
Project Services Congestion Simulation
The following is a simulation showing how increasing uncertain project delivery times in 0.1 hr increments from 0.1-to-1.0 hrs for a single resource affects congestion, which correlates with a resource utilisation of 10%-to-100%. In the interests of running time, this video has been truncated in places to cover the simulation period of 480 hours, which allows the project organisation to overcome warm-up bias and achieve steady-state.
Project Services Queuing Results
As the project delivery rate is increased, both project queuing length and wait exhibit exponential outcomes when utilisation increases beyond 60 or 70%, with dramatic affects being shown for both the average results and the maximum. For the given simulation run if a manager were trying to achieve 100% resource utilization, then clients would have a project experience of 76.1 hours for 1 hour of project delivery time! Clearly, the chances of this project services organisation staying in business for long are limited.
Project Services Queuing Summary
The simulation and results show that to avoid excessive congestion and client waiting times, a resource utilisation figure of less than 60% is desired in a single-channel single-phase project system. This insight has important implications for the management of projects. Even though management might require a project organisation to be 100% utilised, the model shows that congestion and waiting times are very sensitive to resource utilisations, particularly when it exceeds 60%. As such, management can significantly improve congestion and waiting times by planning some idle capacity in the project organisation.
In summary, our intuitive understanding of queuing systems is not adequate to understand the size and affect of high resource utilisation, and by modelling project organisations using queuing models, better client outcomes can be achieved.
If you found this article informative, then please show your appreciation by liking this post.
If you would like to know more about leveraging data-driven actionable insights for your project portfolio, then feel free to contact me on email@example.com.