Enterprise Profitability Analysis
Higher Education
Higher Education
Jeremy Morse CPA | MAICD
Jeremy Morse is a CPA qualified Business Analyst, lecturer and company director.
In 2015, he left a Financial Planning & Analysis position with a Tasmanian not-for-profit to work in the field of Corporate Performance Management (CPM) at LightARC, a consultancy firm that aims to help businesses unlock and utilise the data they already gather and hold.
Over the past ten years, I’ve been a consumer of post-graduate education, was chair at a College which delivered bachelor and postgraduate courses and have lectured undergraduate units.
Over that time, I’ve become aware of the increasing compliance pressure on tertiary education providers. I love to see businesses and individuals make better informed decisions, and help them tap the power of their existing knowledge and data.
Australia’s higher education sector is operating under the influence of some serious impacts. In this e-book, we will discuss how Enterprise Profitability Analysis can be used to improve strategic decision making.
Taking into account the key considerations, providers are under significant pressures, even where they are delivering courses they know back to front. They’re also faced with wide open opportunities to take on new business, either through face to face learning or online delivery.
How can education facilities adapt to these changing conditions? How can they ensure that the right decisions are made that will result in long term sustainability for the organisation?
Long term sustainability is primarily a function of ensuring the financial viability of the organisation. While this involves significant market forces research, it also relies on sound financial principles and a cause based understanding of revenue and costs.
To address and analyse these challenges, we are going to use a hypothetical college, the Learning And Research Centre (LARC) and apply the concept of EPA to their strategy.
LARC is a hypothetical TEQSA accredited college delivering degrees in two faculties - Arts and Business. They have a couple of majors in each, and a modest 215 EFTSL (75% load) in attendance.
Profit margins are low, but consistent, so the College isn’t about to close their doors, but they do want to be in a stronger position. Some courses may not really be pulling their weight, and so a new Entrepreneurship major under the business faculty might be a more sustainable alternative.
LARC runs all of its programs on campus. They have a Principal, a Registrar and some administrative support on staff. The Faculty includes an Academic Dean, faculty leadership and a student support team.
LARC know that their current profitability is acceptable. Their budget projection shows a gross profit margin of 51% and a net profit margin of 6.7%.
However, they are unable to generate profitability by unit straight from their BI tool or accounting system. When they started thinking about determining cost by class, they needed to develop a system to allocate overhead costs on a more detailed level.
To facilitate this process, LARC will research what the key drivers are for all costs in the business. Taking this approach will allow the organisation to adjust key assumptions and test the results of those changes.
The goal of driver-based planning is to focus business plans upon the criteria that are most capable of driving success
After knowing why you want to assign costs, the next challenge is to select appropriate drivers for apportionment of those costs to cost objects.
Drivers are not always black & white, so there may be negotiation on what they are meant to measure and influence.
LARC chose two drivers for cost allocation – revenue dollars ($2.4m) for staff and general overheads, and enrolled student units (1,295) for faculty and course delivery overheads.
Raw student numbers (390) or units delivered (84) might be other possibilities for allocating faculty or teaching costs such as lecture facilities. Overheads might be allocated on the basis of enrolled student units instead of revenue. These allocation drivers may also vary by month or semester depending on seasonality of enrolments or service requirements.
An initial analysis shows that lecturer costs for the Literature and Language units were around 20% more per enrolled student.
Going back to their SMS, they confirmed lower class sizes in those courses. From their administrative records, they also found some permanent teaching staff were underutilised in course delivery.
The net profitability of each unit will obviously change depending on how overhead costs are applied. This is not just the choice of driver for each expense, but also the level of detail applied.
There is also the way programs which are ‘driverless’ are treated. For LARC, overhead costs might be applied against enrolled student units, with nominal ‘units’ assigned to Donations and Investment revenue.
Once possible drivers have been identified, there should be a consensus on how to apply them and what future analysis of these is meant to determine.
What will your organisation do if there is a significant variance?
What is the underlying message being sent by the choice of driver or cost object?
The raw number of workstations might drive ICT costs, but do mobile phones or tablets or laptops also count as workstations?
Does the choice of driver create a disincentive for good resourcing?
This is a complex issue and it takes time to arrive at the ‘best’ allocation drivers. The reality is that there will usually be more than one driver used for different cost objects. The challenge with detail and complexity is to know when enough is enough. Too much detail, and it becomes impossible for managers to know what’s caused cost variance and then effectively manage their areas of responsibility.
When considering cost allocation for new business, there needs to be a consideration of which costs will be transferable in the same proportions and which costs are affected by economies of scale.
For example, if the existing site hosting LARC’s Business degree is underutilised, more classes could be held there without increasing facility costs. Other overheads for admin support, supervision & quality assurance might only increase in line with new enrolments (e.g. as below).
This analysis answers questions of not just marginal profit per unit or course, but fully exposes the real cost and benefits of strategic decisions.
The theory is simple, and while the process is simple, the decisions are complex. A significant cause of complexity is the multiple information systems which aren’t connected enough to effectively analyse the underlying data.
When data is brought together in one place there are massive benefits. The choice of driver can be made and then modified and then compared using a what-if scenario. Other inputs such as CPI, wage inflation, or interest rates can be manipulated for further what-if analysis.
These investigations allow better decision making as well as more efficient and effective resource allocation.
Ultimately, the aim is to create a manageable solution that works for your organisation.
If you would like to explore this topic further, please get in touch for an obligation-free initial discussion
www.lightarc.com.au