**Imagine being able to increase revenues without increasing the cost base!**

This topic is bound to create a lot of robust discussion, which is encouraged and it should be discussed. We are talking about understanding the capacity of the teaching environment so that schools maintain the optimum number of lecturers/teachers for the enrolled students. This doesn’t mean making academic staff work longer hours, or necessarily dropping other responsibilities; it’s about identifying and reporting on available capacities so that:

1) It’s transparent

2) Management can take appropriate steps to maximize teaching capacities

Pilbara Group’s standard University Management Model (with historical costs and revenues) is built using a range of metrics including teaching profiles. These profiles include hours to teach, prepare for lectures, grade papers, meet with students, etc. These parameters allow the model to calculate the total academic time required to deliver a course. Of course, teaching profiles vary from school to school within an institution as well as varying from institution to institution (there is no one size fits all!). The model is flexible and can account for a full range of variable factors.

The total academic time required to deliver a course, when combined for all courses across a department or school, gives the total number of hours required to deliver the teaching for the department or school. This is known as the Teaching Pool.

The human resource (HR) data that is added to the model includes academic work profiles of academic staff. These academic work profiles determine how academic staff time is divided between their primary activities – typically teaching, research, community engagement, and administration. The academic work profile is usually captured on a percentage basis (e.g. 40% teaching, 10% administration, and 50% research). This academic work profile combined with the standard working hours for an academic gives the total number of hours available in the department or school for each of the primary activities undertaken by academics.

The academic work profile (available hours), in particular the teaching component of the profile, and the teaching pool (required hours) can then be compared to provide an efficiency ratio for the department or school. For example, a school with an efficiency ratio of 0.9 indicates that for every 10 ‘available’ teaching academic hours, 9 are being spent teaching. Whereas a ratio of 0.6 indicates that for every 10 ‘available’ teaching academic hours, only 6 are being spent teaching – an indication that the school has some spare capacity and that more actual teaching time is available without the need to employ additional academic staff.

**So what does all this mean?**

In the latter example there could be an opportunity to increase the number of enrolled students, and hence increase revenues, without increasing the cost base. Following is a worked example from one of Pilbara Group’s demonstration predictive models. It uses fictitious numbers that are for demonstration only.

The example demonstrates the use of a predictive model in determining current spare capacity and also in determining how that spare capacity can be absorbed by increasing student numbers.

This worked example starts with a base predictive model. This base model contains assumptions and relationships that have been derived from historical data. One of the data elements in this base predictive model is an efficiency ratio that is based on the school’s most recent historical data. While not shown in this document, these efficiency ratios vary quite significantly from department to department.

The first step is to quantify the overall spare capacity within the school. To do this a new scenario is created: Predictive Model Scenario – Capacity (1). Within this scenario all department efficiency ratios are set at 90% – a number nearing theoretical maximum capacity. It was decided not to demonstrate this using an efficiency ratio of 100% because no one can be 100% efficient. Everyone needs to take a break now and then!

There are two other key assumptions within the base predictive model and the scenario that affect this example.

Namely:

1. Available working hours: It is assumed that the total annual available working hours for a full time academic are 1920 hours.

2. Percent Teaching: In this predictive model the academic work profile is set such that academics spilt their time as follows: 80% teaching, 20% general administration. For the sake of simplicity this example does not account for academic research or community engagement activities, but it is possible to do so if required.

The table above shows that the current student teaching load can be achieved by approximately 81 academic FTE working at full capacity. In other words, the school currently has approximately 8.6 Academic FTE surplus and, as a result, 4 non-academic FTE surplus. This indicates that the school has a unique opportunity to grow without increasing its staff numbers. But how much can it grow?

In order to answer this question there is a need to create another scenario: Predictive Model Scenario – Capacity (2) This scenario considered what would happen to expenses and revenue if the academic FTE were left at the current levels (i.e. those in the predictive model base) and the number of students was increased to maximize use of the spare capacity. Increasing the student numbers in the scenario means that the required teaching hours for the school will increase. The question that needs to be answered is: how many extra students can be accommodated so that the required teaching hours equals the available teaching hours for 89.3 academics with a 90% efficiency ratio?

In order to answer this question the Capacity (2) scenario was created from the previous Capacity (1) scenario. The student numbers were then increased using an across-the-board percentage increase until the academic FTE within the school was back to the level in the original predictive base model. This indicated that the excess capacity quantified in the Capacity (1) scenario had been utilized to address the increase in required teaching hours that came about because of the increase in student numbers.

At the end of the analysis it was found that the school can increase its student numbers by approximately 17% with its existing faculty. The table below shows the results of maximizing the capacity of the academic FTE through increasing student numbers.

Maximizing efficiency as well as the number of enrolled students leaves expenses the same as the base model but increases the delivery hours, revenue, and therefore margin for the school. It should be noted that the student increase scenario could have been more targeted. Rather than an across-the-board increase it is possible to increase student numbers in a scenario for specific programs (e.g. Bachelor of Science) or for any subset of programs. Student number increases can be factored as a percentage increase on the base enrolment or as a set number.

So, based on this example the University has three choices:

• Keep operating as is and continue to lose $203,380 per year;

• Reduce expenses by removing 8-9 academic staff and approximately 4 non-academic support staff , while increasing remaining academic staff utilization, to bring the faculty into a profit of approximately $800,000;

• Or, alternatively retain all the staff (both academic and non-academic) and increase the student throughput which increases profit for the University to over $2m.

Obviously there are a number of ways for a University to increase its margin. This example is just one path that could be available to a University. The point of the example is to demonstrate the power of being able to quantify the impact of decisions before they are made. Any strategic decision that looks to reduce expenses or increase students is something that is going to be politically sensitive. The example provided is particularly sensitive due to the nature of the questions being posed. As with any analysis designed to support a strategic decision we highly recommend that the analysis is conducted with the full involvement of faculty members.

The value of this analysis has enterprise impacts that can be beneficial for a university. First, it can help universities “right size” their faculty where the ratio of full time faculty and adjunct faculty are optimized to account for student fluctuations on a semester-to-semester or year-to-year basis. Second, it can bring stability to the university and retain precious institutional knowledge among faculty by presenting opportunities to retain faculty and reduce turnover. Third, the option to increase student revenue by increasing student enrollment versus reducing the faculty based on annual fluctuations can be utilized with other revenue producing activities that do not increase expenses such as determining the facility and administrative costs incurred by grants and submitting for reimbursement. And lastly, understanding the available time for teaching allows universities to make changes to their definition of a “full time” faculty member. In some cases, universities are requiring faculty to teach 5 courses or 15 credits in lieu of the traditional 4 courses or 12 credits per semester.

All said, the capacity analysis described here would allow an institution to conduct what-if scenarios to determine the best strategy forward.