How does resource count impact resource management maturity? #PMOT
Here is some more interesting data coming out of our in-progress Resource Management Maturity Survey. In my last blog post we focused on the cross-tabulation of an organization's overall resource management maturity level with the geographic scope of the resources being managed. We found that regardless of geographic scope (ranging from single site to global deployments) resource management maturity levels as defined in the Resource Management Maturity Model (RMMM) followed the same pattern.
The data suggested that moving beyond simple “work visibility” (Level 1) doesn’t seem to be easier to achieve if your resources are all located in a single site versus being globally deployed. And, the higher levels of resource management maturity are no more attractive or achievable when resources are consolidated or dispersed geographically.
In this post, I will look at how resource management maturity levels correlate with the number of resources being managed. Given that it would make sense to assume that resource counts would be highly correlated to whether or not resources were located in a single site, distributed across multiple sites or distributed globally, I was expecting a similar lack of substantial correlation. However, that is not exactly what was found.
As you can see in the chart above, organizations managing less than 50, 50-100 and over 500 resources all follow the same pattern with (Level 1) work visibility representing the plurality of maturity levels achieved with a subsequent fall-off in maturity levels at varying rates moving beyond Level 1.
However, interestingly organizations managing 101 to 500 resources (see red plot line) show increasing levels of maturity, peaking at the maturity level 3 which has been characterized in the RMMM as the maturity level “sweet spot”. It appears that there is a sweet spot not only in terms of the ideal maturity level for most organizations, but a sweet spot in terms of the number of resources managed.
How can we explain this? Well the answer may lie in the conclusions drawn from the previous post regarding the impact of geographic scope. In that analysis, I hypothesized that the drivers for achieving higher levels of maturity in global deployments maybe be counter-balanced by the complexity of achieving higher levels of maturity in a distributed environment. Inversely, in local environments the need for resource management process sophistication might not be as high for a typical single-site operation, but there is likely to be a higher success rate when there is a need to increase the maturity level of the resource management process. Thus, on average local and global resource deployments look similar in terms of their resource maturity profiles.
However, this generalization masks what’s going on somewhere in the middle where there is a critical mass of need to improve resource and capacity management processes (i.e., the organization is not too small) at a scale that is not too complex (i.e., the organization is not too big).
OK, so what? I think the useful conclusion here is that if you are in an organization that is managing between 100 and 500 resources, it might be important from a “peer” benchmarking perspective to know that achieving the “sweet spot” level of maturity is certainly within your grasp barring any unusual company culture factors or other company context anomalies. If you are not in this range, this data serves to set some expectations; clearly the need and value proposition of achieving the desired level resource and capacity management maturity must outweigh the risks associated with the complexity of the endeavor.