Many of us will have pondered the question of what level of staffing is needed to carry out professional services tasks in our university. This may well have led to trying some form of benchmarking to determine how our university compares with others. Benchmarking can range from the very simple (e.g. comparing student: staff ratios) to complicated, structured tools – such as those offered by some consultancy firms. However, when comparing between universities all methods suffer, to a greater or lesser extent, from the issue of not knowing whether you are really comparing like with like.

University professional services vary enormously in terms of structure, how tasks are deployed to functions, levels of devolution, and the range and type of programmes supported. It is this that leads to the difficulties in benchmarking and comparing between universities. At best the outcomes can give a general indication of how universities compare but more often than not the outcomes can be fairly meaningless. The sophisticated, structured tools offered by some consulting firms do best but come at a cost, both in terms of payment for the tool and the in-house resource required to use it.

As an alternative, and based on our work with universities, AUA Consulting has developed a ‘bottom-up’ approach to defining optimum staffing levels for professional services functions. Our approach is a form of workload allocation model which builds up from ‘standard’ and ‘non-standard’ activities to define a baseline, or notional, staffing level. The baseline can then be measured against actual resourcing levels and can be used in planning future staffing requirements or within a budgetary round to assess staffing needs and requests.

How does it work?

The approach starts by defining the standard activities that are part of the professional services area being analysed. In terms of staff required, we consider there are both fixed elements to these activities and variable elements.

The fixed elements of the standard activities are those that do not vary with the volume of the task. For example, if the task is programme administration there are standard elements, such as annual monitoring or maintaining course material content, which need to be carried out regardless of the number of students on the programme. In the model a fixed, or constant staff FTE would be assigned to these elements.

The variable elements of the standard activities are those that do vary with volume. Referring to programme administration again, these elements might be activities like maintaining student records or examination and assessment organisation. In the model a staff FTE per student would be assigned to these elements.

Many professional services tasks also involve additional ‘non-standard’ elements which are particular to the specific, local situation. Continuing with our programme administration example, these might be activities linked to programmes which operate on a non-standard academic calendar. An additional FTE calculation would be added into the model to account for these.

AUA Consulting has developed a ‘bottom-up’ approach to defining optimum staffing levels for professional services functions. 

You’ll see that the mechanics of the approach I’m describing are relatively straightforward. However, the more difficult part is reaching agreement on the various tasks and activities and the level of FTE to be assigned. This requires broad stakeholder input – perhaps via a cross-university working group.

Once the parameters for the activities in any given task have been agreed a total FTE staffing level for the task can be calculated. This notional, or baseline, staffing level can be compared to the actual level of resourcing and to feed into planning and budgeting discussions and to drive change projects. Depending on the task, the model can be applied on a whole university basis or at a faculty, school, or department level.

Benefits of this approach

Aside from not needing to have to tackle the issue of how to compare like with like, this approach has four other benefits.

1. In order to set the parameters of the proposed model correctly, agreement is required across the university on what standard and non-standard professional services activities are as well as what the level of staffing should be for these. This ensures that even before applying the model there is already a good deal of commitment to its outcomes.

2. Whilst the model establishes norms, it also provides a way for additional, non-standard activities to be defined, justified, agreed, and included in the model.

3. Because the approach goes to a relatively detailed level it can fairly easily be updated to take into account such things as increased efficiency as a result of automation, or optimisation of processes.

4. As this approach has a detailed basis, it can provide a more robust and accurate reference point for forecasting and planning.

Application of the approach

In this short blog I’ve been able to set out a high level overview of a professional services workload allocation model – as an alternative to benchmarking. If you are interested in using the tool with your university’s professional services, AUA Consulting would be pleased to provide more information and we have the experience to help and advise you.