Questionnaire findings
The importance of forecasting to various activities (Question E1) is shown in the table below, with respondents asked to rank activities using a 5-point Likert scale. Responses from institutions have been weighted and ranked in accordance with the importance attached to each activity. Where an activity is considered to be the most important the response is weighted by a factor of 5, where it is the least the weighting is 1.
Table 5.14 Reasons for undertaking forecasting
Most important Least important Weighted Ranking
Weighting 5 4 3 2 1 Score
Annual budget process 280 60 15 4 4 363 1
Strategic planning at the University level 180 92 42 8 3 325 2
Formal planning of surpluses/(deficits) 175 100 30 8 4 317 3
Cash flow management 160 108 24 18 3 313 4
Strategic planning within colleges/faculties/schools 125 88 63 18 4 298 5
Communication with the Funding Council 110 104 48 20 5 287 6
On-going performance management 80 76 66 26 3 251 7
Preparation of the Margin for Sustainability and Investment 100 40 63 28 8 239 8
Debt financing 95 56 36 30 14 231 9
Other external reporting requirements 40 44 75 28 10 197 10
Tax planning 25 20 33 40 28 146 11
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Respondents identified that forecasting was most important for informing the budget, strategic planning, the planning of surpluses or deficits and cash flow management. However, despite its importance, forecasting as a process is only really effective if the results are acted upon (Berry et al., 2004). Some interviewees (see below) indicated a lack of confidence in their forecasting in the current uncertain environment which may affect how well they are used.
Most (67%) indicated that they updated medium-term forecasts quarterly or more frequently (Question E2). However, the validity of the responses seem doubtful. When interviewed, respondents who had indicated a frequent update were actually referring to a re-forecast of the annual budget rather than recasting the medium term forecasts. These latter forecasts were more commonly updated when re-running strategic models for internal planning purposes once student numbers for the current year had been established that could be rolled forward to future years, or were undertaken for external reporting purposes when updating figures reportable to the Funding Council. The survey questionnaire included a definition of terms at the start which stated that future forecasts were those falling beyond the period of the annual budget. With hindsight, it would have been beneficial to repeat the definition at the outset of section E of the questionnaire which dealt with forecasting questions.
Question E3 sought details of who provided information for forecasts. However, it is possible that some responses were in respect of both forecasts and revisions to budgets.
Table 5.15 Participation in forecasting
Central finance
Academic areas
Support areas Corporate management
Other
Funding Council grants 79 3 14 6 1
Academic fees and education contracts 65 44 28 11 1
Research grants and contracts 54 60 26 6 0
Other operating income 69 45 38 8 1
Endowment income and interest rec’d 82 1 4 3 1
Staff costs 74 49 52 14 0
Other operating expenses 72 60 56 11 2
Depreciation 84 0 3 2 0
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The results indicated that forecasts were not prepared by central finance in isolation and included other departments in key aspects of the forecasts, but the degree of involvement of other areas is probably variable. The importance of central finance liaising with other areas is explained by Berry et al. (2004). They noted that senior finance managers tended to be distant from academic work, had an institutional focus and lacked an insight into the detailed nature of academic work. Interviewees were looking to address a lack of knowledge of academic areas by seeking to enhance business partnering practices (NU1, NU7, NU17, OU5, OU18, OU21).
Student number forecasting is becoming increasingly important. Question E4 therefore sought to discover who played a major role in preparing these forecasts.
Table 5.16 Role in preparing student number forecasts
Major Minor None
Central finance function 23 33 5
Registry department 24 15 11
Planning department 52 9 5
Academic areas 39 19 3
Other 2 0 0
Table 5.17 Type of institution where the central finance function has a major role
Pre-1992 9
Post-1992 12
College of HE 2
The lead was usually taken by the planning or registry department with the assistance of academic areas. However, as explained earlier, interviewees expressed a view that these forecasts were generally inadequate. Increased input from academic areas might have improved the end result, particularly as greater responsibility for generating income now rests with those closest to the recruitment process.
When forecasts were recast very few institutions (13%) felt the need to reset the current year’s budget (Question E5). However, Question E6 indicated that most respondents used the current year’s budget as the base-line for deriving future forecasts, so there was a clear link between the two.
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83% of responses to Question E7 indicated that the most common forecasting period was 3 to 5 years. Most tied the period to external reporting requirements or internal planning needs. Those who chose a much longer period appeared to do so in order to match their forecasts to the length of their strategic planning cycle, their estates and other capital plans or to inform their cash flow projections.
Over 67% of respondents had considered the effect on forecasting of the new reporting standard FRS102 and FEHE SORP 2015 (Question E8). At the time of the survey, the sector generally appeared to recognise that the new reporting requirements might have a significant effect on forecasting. Most interviewees seemed aware of the requirements and some had already modelled the potential effect in areas such as revenue recognition. Although university interviewees seemed generally unconcerned, an interviewee at HEFCE explained that FRS102 and the new SORP could have a significant impact on the Annual Accountability Return to HEFCE with reported surpluses becoming more volatile.
Question E9 sought to understand what areas caused institutions the most difficulty when trying to set accurate forecasts in terms of functions over which forecasters might be able to exert some control.
Table 5.18 Impediments to producing accurate forecasts
Major Minor Not an
impediment
Total Responses
Quality of financial data inputs 17 39 23 79
Quality of non-financial data inputs 20 40 19 79
Quality of student number data inputs 37 27 15 79
Pressure to match target rather than a realistic outlook 14 33 34 81
IT tools employed 17 34 29 80
Insufficient involvement of operational areas 7 34 38 79
Insufficient involvement of senior management 9 26 45 80
Time available to produce forecasts 15 37 29 81
Tendency to focus too much on detail 15 39 25 79
Difficulty accessing relevant data 14 47 18 79
The most common problem was obtaining meaningful student number data. This was consistent with earlier responses to Question E4 where central finance had difficulty in deriving accurate monetary forecasts from student number data. Most respondents recognised the importance of consulting widely and had good processes in place to do this even if the data obtained was not
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always considered to be sufficiently robust. Furthermore, interviewees indicated that actions were usually taken to drive the institution towards its targets if the initial forecasts suggested that desired outcomes might not be achieved.
Question E10 sought views on the accuracy of forecasting.
Table 5.19 Accuracy of forecasts
mean 4.44 (standard deviation 1.77)
Cautious Optimistic Scale 1 2 3 4 5 6 7 8 9 10 Total Respondents 0 1 21 25 14 7 8 3 0 1 80 Percentage 0% 1% 26% 31% 18% 9% 10% 4% 0% 1% 100% Cumulative 0% 1% 28% 59% 76% 85% 95% 99% 99% 100% Accurate
59% of respondents produced cautious forecasts. CFO Research Services (2011) also found a preference for a cautious approach amongst large companies but to a lesser degree, with 44% setting conservative forecasts which would understate actual performance.
This tendency to set achievable forecasts was similar to findings for Question C1 and indicates that a similar attitude is adopted to annual budgeting and medium-term forecasting. Despite potentially more time and effort being employed on the budgeting process, the greater certainty of the outcome because of the shorter time period covered and the less granular approach taken to deriving forecasts, there appears to be little difference in the perception of accuracy between budgeting and forecasting.
When considering the IT used for budgets and forecasts, 50 institutions (62%) used one tool to integrate actuals, budgets, forecasts and reporting (Question E11). Thus preferring an integrated approach. However, only 28 respondents used specialist software for their student number planning (Question E12) despite the difficulties experienced in achieving accurate tuition fee income projections.
Reviewing the accuracy of forecasts against the actual outturn at a later date (Question E13) was undertaken by 68 institutions (84%) indicating the importance placed on achieving forecasting
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accuracy. Furthermore, 55 respondents (68%) said they would be interested in comparing the accuracy of their forecasting against other institutions if benchmarking data were available (Question E14), but due to the lack of data available it was perhaps unsurprising that only 5% claimed to have made any attempt to benchmark the accuracy of forecasts against external data (Question E15).
Question E16 sought to discover if aspirational targets were maintained in addition to forecasts. Respondents appeared to understand the difference between the two. Over 30% said ‘yes’ and 34% ‘sometimes’. Respondents therefore sent forecasts to their Funding Councils which they considered to be a realistic reflection of the institution’s activities even if they might be somewhat cautious compared with the institution’s targets.
The penultimate question in Section E sought views on the forecasting process (Question E17).
Table 5.20 Perceptions of the forecasting process
Strongly disagree
Disagree Neutral Agree Strongly
agree
Total
Forecasting is more an art than a science 1 10 24 40 6 81
At this institution, forecasting is just part of the budgeting
process, rather than a broader performance management tool 7 28 17 28 4 84
Reliability of the institution’s forecast is compromised because
operational functions are not sufficiently involved 7 42 17 16 2 84
A greater understanding of how the various parts of the
organisation operate would improve the forecasting undertaken 4 18 15 41 6 84
Forecasting accuracy has deteriorated in recent years 17 44 17 5 1 84
It is difficult to set accurate forecasts because of the
unpredictability of factors influencing the institution’s activities 4 22 19 36 1 82
Forecasts quickly become obsolete or outdated 3 21 17 37 4 82
Inaccurate forecasting has adversely affected the institution 5 36 17 20 5 83
Governing body takes an interest in the accuracy of budgeting
and financial forecasting 0 13 14 40 17 84
(Note: Shading is used to identify whether more respondents disagreed/strongly disagreed or agreed/strongly agreed)
The process was considered to be more of an art than a science (57%) that could be influenced by perceptions and the culture of the institution. 67% of senior executives in the private sector took a similar view (Economist Intelligence Unit, 2007). However, respondents were almost equally split in their view of whether forecasting was just part of the budgeting process (38%) or a broader performance management tool (42%). Similar percentages, at 44% and 40%, were found for
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private sector organisations (Economist Intelligence Unit, 2007), suggesting similarities in thought processes despite differences in the type of organisation.
Many felt that their institution’s forecasts were reliable because operational functions were sufficiently involved in the process, but 56% agreed that improvements could still be made by acquiring a greater understanding of the various parts of the organisation. For the private sector, only 27% felt that the reliability of the organisation’s forecasts were compromised because operational functions were not sufficiently involved (Economist Intelligence Unit, 2007), indicating a more developed process.
Despite the recent significant changes affecting the sector, the majority believed that forecasting accuracy had not deteriorated in recent years. However, many agreed that forecasts quickly became obsolete or outdated and this could be a symptom of the pace of change in the sector. The majority also felt that any inaccuracy in their forecasting had not adversely affected the institution.
The final question in this section asked respondents to indicate which techniques were used for preparing income forecasts (Question E18).
Table 5.21 Techniques used to forecast income
Respondents Percentage of
total returns
Estimates based on knowledge of staff 81 96%
Trend projections 57 68%
Market research 24 29%
Simulation analysis 12 14%
Regression analysis 5 6%
Other 5 6%
The majority prepared estimates based on the knowledge of staff and the use of trend projections, although the weaknesses of trend analysis were recognised by interviewees. Claims to the use of more complex statistical techniques such as simulation and regression analysis were rare. Various studies of the private sector have also found subjective estimates to be the most popular and statistical techniques the least (Drury, Braund, Osborne & Tayles, 1993; Guilding, Lamminmaki & Drury, 1998; Ahmad, Sulaiman & Alwi, 2003).
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In a university context, Brinkman and McIntyre (1997) considered the alternative methods for enrolment forecasting in American universities and found that complex methods are rarely employed. Others explain that “mathematical trend extrapolation, time-series models, and probabilistic forecasts, are less familiar to higher education administrators” and are less likely to be employed (Morrison et al. 1984, p.14).
Key points
Participation in budgeting and forecasting is wide-ranging and may lead those outside of the central finance department to conclude that there is increasing financialisation of universities.
Forecasting is as cautious as budgeting despite the interest of the central finance function in achieving accuracy.
Simple forecasting techniques were preferred over more complex methods, similar to the approach taken to budgeting.
Rhetoric on the consequences of inaccurate forecasting may not be supported by evidence of significant adverse effects.
Interview findings
Despite future years forecasts being derived from the current year’s budget, in some cases the budget was considered to be of less value than forecasting. One interviewee commented that: “There is a desire to spend less time number crunching and more time on value added activities such as ensuring financial sustainability” beyond the period of the budget (OU6). Others explained that: “Lots of effort is put in to getting the budget right so this can be used as the baseline for forecasting in the following years. However, if faculties set unrealistic forecasts the figures are adjusted in the forecasts sent to HEFCE” (OU7). An interviewee of an institution where medium term forecasts were prepared as part of the overall budgeting cycle suggested that: “Future years’ forecasts are becoming increasingly important and there is a greater tendency to hold departments to what they say” (NU14).
A number of the interviewees stated that their institution maintained five year forecasts. One commented that: “This is undertaken for sustainability purposes to go beyond the ‘steady state’ period where tuition fee income has replaced HEFCE grant” (NU5). In a few instances the period
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of the forecast was dictated by the bank from which funds had been borrowed. Others adopted the principle of ‘2+2’ (i.e. two years’ budgets, representing the current year’s outturn and the following year’s budget, and two years’ forecasts), in-line with Funding Council reporting requirements at the time.
However, some were sceptical of the forecasting exercise given the current uncertain environment, with one stating that: The final two years of the HEFCE Annual Accountability Return forecasts is “just a box ticking exercise. It has no meaningful use” particularly given the regular changes in government policy (NU1). Uncertainty was a common theme in the discussions with interviewees (even prior to the UK’s decision to leave the European Union following the referendum in June 2016) as well as the influence of banks.
In terms of reviewing the accuracy of forecasts, one interviewee explained that: “There have been exercises to track forecast evolution by faculty to see which areas have the largest variances” (OU19). Another commented that: “Forecasts can vary as a result of unexpected changes in the environment. This doesn’t mean that the forecast was inaccurate based on the information available at the time” (NU20). Adverse or favourable variances were reviewed in context, i.e. additional income may have resulted in adverse variances on expenditure. An interviewee suggested that they might look at benchmarking their forecasting accuracy against others in 5 years’ time, but the processes being put in place and the market changes were too new at the moment to draw any useful information from a benchmarking exercise (OU5).
Unlike other questionnaire responses where there was consistency with the interviewee comments, some indicated the use of sophisticated forecasting techniques when responding to the questionnaire, but this was not confirmed at the interview stage. For example, one respondent (NU14) used the TRAC return data to ‘simulate’ costs going forward by assuming that unit costs would be incurred on a similar basis in future years. A simulation model had not actually been developed despite the respondent indicating on the questionnaire that it was used. However, there appeared to be some movement away from reliance on past trends, with one interviewee explaining that as income was now more uncertain less emphasis was placed on trend analysis and more time was spent on assessing the future. Market research had also been undertaken in areas such as projecting international fee income (NU19). Another explained the uncertainties they
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faced: “Overseas income has been growing fast, but the International Director is always nervous about how things will work out” (NU17). Forecasting overseas income tends to be based on a combination of current and past experience and target setting to ensure growth in income, but HEFCE has suggested that forecasting in this area may be overly optimistic (HEFCE, 2017a).
In explaining the process of creating forecasts, it was stated that: “Budgeting tends to be scientific, but forecasting depends on assumptions which are invariably wrong”. However, it was noted that the benefit was that it “gives you a feel for the risks and helps in getting the story right. It’s important to see where the assumptions are taking you” (OU6). Other interviewees commented that different investment decisions could have been made if a less cautious approach had been taken, but that “there is not much evidence that better decisions would have been the result” (OU16) and that “we know we’re being cautions and will plan capital projects with this in mind” (NU18).
Views gathered from HEFCE representatives when interviewed showed that the Funding Council appeared to have a good understanding of the forecasting accuracy of individual institutions, based on knowledge built up over many years. They know which are likely to submit prudent forecasts and those which are less likely to do so. The Funding Council also recognises that the culture of an institution and changes to it can affect the prudence of forecasts. Interestingly, they have commented that a change in Finance Director can sometimes result in changes to the cautiousness of forecasting. However, this may not be an intentional change in prudence as those interviewees new to the role of Finance Director did not indicate that they specifically sought to change forecasting practices. HEFCE’s view is that whilst forecasting in the sector will never be perfect, it does give an indication of an institution’s strategy and plans.
In commenting on the sector, one Finance Director explained that the “budgeting and forecasting processes tend to be tailored to the circumstances of the individual institution” (NU8). Other interviewees noted that they tended “to do better than forecast, certainly in the last 8 out of 10 years. This is typical of the pessimistic nature of the sector” (OU20) and: “There is an underlying cautiousness built in to the culture of the sector” (NU18).
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