**Abstract**

A common issue in assigning tasks in a group environment neglecting to consider an individual’s specific skill profile and their familiarization with the specific task. One particular group member may be very efficient in one or two areas of focus, but very inefficient in others. While another member may be much more familiar with the task. A standardized methodology is presented here which will allow a project manager to assess task assignment from an efficiency perspective, and also to determine the overall efficiency of a group working on a specific task or project, resulting in an “Effective Group Size”.

**Keywords**

Task assignment, skill profile, involvement profile, efficiency, effective group size

**Introduction**

For the purposes of this paper, let us consider a group of engineering designers, with varying degrees of skill in a given set of specialty areas. A manager, several reporting levels above these designers, but who still has influence on the task assignment for each designer, would naturally have the following assumptions:

- All designers possess equal skills in all areas, and so, are interchangeable;
- Doubling the number of designers on a given task will double the rate at which the task is complete;
- Halving the number of designers will halve the rate at which the task is complete.

These assumptions may seem harmless, but they can manifest themselves in various ways. The manager may remove one designer from a “Task A”, halving the number of designers on that task, and move her to “Task B”, doubling the number of designers working on that task. Now, if the above assumptions are to be believed, then “Task A” would be completed in twice the time previously thought, and “Task B” would be completed in half the time previously thought. The methodology below will disprove these assumptions, and provide a robust method for determining the true effect of task assignment on efficiency.

**Methodology**

There is some background information that we require to proceed. That is to say, we need some knowledge of both the designers and the task.

First, the designer. We shall assume certain skills that we can assign a ranking to for each designer. We shall also assign desired rankings in the same skills for the specified task. For our purposes, the skills are as follows:

- Pure modelling
- Conceptual Design
- Detail Design
- Drafting
- Checking

Now, we should note that this set of skills is arbitrary, and can be modified for specific roles. The size of this set can also be expanded or contracted, as required. This is accounted for as the “n” variable in Equation 2.

This shall result in our first variable, which we will refer to as the “Skill Factor” (see Equation 2)

Below are examples of a “Required Skill Profile” for a given task, and of a specific “Designer’s Skill Profile”:

**Figure 1 Example of Required Skill Profile**

**Figure 2: Example of Skill Profile**

Secondly, we need to address the issue of a designer’s familiarity with the task at hand. Let us assume here that a designer assigned to a task from its conception will be more efficient at that task then if she were to be assigned at some time after work has already been performed. Let us also assume that there is a level of involvement during each phase of the task that will either increase or decrease the efficiency at which the designer can work. This is to say, the higher the level of involvement, for a longer period of time will increase efficiency.

Below is an example of an “Involvement Profile”. In this case, the specific designer has been involved for all 10 weeks that the task has been ongoing, which varying levels of involvement.

**Figure 3 Example of “Involvement Profile”**

This results in our second variable, τ, which we will refer to as the “Knowledge Stream Factor”.

*Equation 1 Knowledge Stream Factor*

where

*τ = Knowledge Stream Factor*

*j = the number of weeks (or time units) that the task has been ongoing.*

*LOI = the Level of Involvement for that given week (or time unit)*

*(Note that “10” derived from the size of the “LOI” scale, which is from “0-10”)*

The combination of these two factors will provide us with our “Ranking Factor”.

*Equation 2 Ranking Factor*

*where *

*R*_{Ranking} = designer’s “Ranking Factor”

*i = the number of skills on which the assessment is based*

*x*_{req} = the required skill level required for “i” skill

*x*_{n} = the skill level of the designer

If we perform the calculation to determine the “Ranking Factor” for Designer X, using the profiles in Figure 1, Figure 2, and Figure 3, we find a R_{Ranking} = 0.51. This shows that this designer would be approximately 51% efficient at this task.

**Results**

We are now able to produce some data that can be analyzed. We can determine each designers “Ranking Factor” for a given task, and determine which is best suited. Some interesting information can be gleaned from these factors.

Firstly, the closer to R_{Ranking}=1.0, the more suited that particular designer is for the given task.

Secondly, when we see a value of R_{Ranking}<1.0, then we see that the designer would be less than 100% efficient working on the given task. That is to say, if two designers with a Raking Factor of R_{Ranking}=0.5 were to be assigned to a given task, they would only have a combined efficiency equal to that of one designer with a Raking Factor of R_{Ranking}=1.0.

This can be summarized as a combined group efficiency rate, or as an effective group size, as follows:

*Equation 3 Effective Group Size*

*where *

*n = the total number of group members*

This infers that we can use the Ranking Factors not only as means of assigning designers to tasks, but also to determine the effective strength of a given group, working on a specific task, or project.

**Discussion**

One can argue that the further removed from a group a manager is, the lower their specific knowledge of a team’s abilities can be known. This is only natural. Contrarily, this does not necessarily prevent such a manager from making requests on adding or removing group members from a specific task, her assumption being that the rate at which a task is complete is proportional to the number of people assigned to said task. It can be difficult for a Project Manager or Team Lead to quantitatively demonstrate that this is not necessarily the case. This new methodology may be a solution to this problem.

Once can also see this methodology being used to efficiently hire new team members, if their skill sets can be accurately assessed against the desired skill set.

It is expected that this methodology can also be extended to also incorporate specific tasks within a larger project, and also to assess a groups efficiency on a project with multiple, discreet tasks.

**Conclusion**

The methodology described here is robust enough that it can be applied to large groups, with varying tasks distributed and shared between and amongst multiple team members. This is useful in planning and loading large scale projects to ensure that the effective group size is what is expected, and to ensure that estimates and project targets are achieved.

However, it is also simple enough that it can be used quickly, on small groups and individual tasks. This is especially useful, as mentioned several times, when assessing the impact of team member reassignment. The result is an effective and quantitative method of communicating this impact.