DERECHOS DE PREFENCIA, ANTIGÜEDAD Y ASCENSO.

It is useful to be able to accurately forecast the effects that changes in variables can have on revenue streams in order to plan and budget for the future with sufficient certainty. This point is emphasised by Agostini (1991, p.13) when stating: “In public-sector budgeting, the availability of resources circumscribes decisions of all expenditure considerations. As these decisions intensify in the face of mounting fiscal duress, reliable and informative revenue forecasts become critical elements on the [effective] budget process”.

The maintenance of records to assess forecasting accuracy on a regular basis is not universally undertaken by organisations (Rothe, 1978) and many do not have systems and procedures for analysing forecasting errors (Drury, 1990). Formally assessing accuracy can lead to improvements (Mentzer et al., 1999; Cassar & Gibson, 2008), but the preparer needs to participate in the process (Mentzer & Cox, 1984b).

Forecasting consistency is of greater importance as firms “feel they can get along all right as long as their forecasts fall within familiar margins” (White, 1986, p.11). The latter is echoed by HEFCE in terms of forecasts submitted by universities. Although the funding body would prefer accuracy, it is still able to effectively analyse the likely outturn compared to the forecasts submitted in the Annual Accountability Return where there is past evidence of consistent favourable or adverse variances.

In considering acceptable tolerances for variances from forecast, Wheelwright and Makridakis (1980, p.9) explain that: “For some decision makers, anywhere between plus or minus 10% may be sufficient for their purposes, but in other cases a variation of as much as 5% could spell disaster for the company”. In general, the higher the investment in testing, refining and developing the

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forecasting methods, the greater the accuracy achieved (Doyle & Fenwick, 1976) and the more confidence users will have in the forecasting process.

Forecasting accuracy has been shown to be greater in larger and in older firms. This is most likely due to such firms having more resources to devote to the process, and a greater history of trends, market behaviour and knowledge of the business environment (Winklhofer, Diamantopoulos & Witt, 1996; Jelic, Saadouni & Briston, 1998; Diamantopoulos & Winklhofer, 1999; Cheng & Firth, 2000). However, some studies also show that an increase in market area can have an adverse effect on forecasting accuracy (Dalrymple, 1975; Rothe, 1978). Many universities have strategies to grow and to diversify their income streams, particularly in the current competitive environment, which may result in less accurate forecasting.

Furthermore, preparing forecasts at a higher level in the company hierarchy, better formal training of forecasters, seasonally adjusting forecasts, employing consultants and computers, and using forecasts for various applications have all been shown to increase accuracy (Mentzer & Cox, 1984a; Dalrymple, 1975; McHugh & Sparkes, 1983). Moreover, there appears to be some correlation between desired and achieved accuracy (Pan, Nichols & Joy, 1977).

Neeley, Sutcliffe and Heyns offer the view that:

If budgets and forecasts are built upon explicit and well-founded assumptions and assertions, and they, in turn, are regularly challenged and questioned, then the company is likely to make realistic forecasts that it can deliver against. Specific techniques such as rolling forecasts, are particularly important in this regard because more frequent planning and budgeting enable more accurate forecasts as do forecasts covering time periods shorter than the annual budgeting cycle (Neeley, Sutcliffe & Heyns, 2001, p.18).

A balance has to be struck between complexity and transparency. Disaggregated figures do not necessarily produce better forecasts than aggregated data (Bavnea & Lakonishok, 1980; CFO Research Services, 2011; Hoffelder, 2013). Too much detail can lead to the conclusion that the forecast is accurate simply because it is complex. As explained by Parmenter (2014, p.48): “Forecasts are rarely right, and forecasting at a detailed level does not lead to a better prediction of the future”.

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New technology can have an important part to play in ensuring accurate and timely budgets and forecasts. Standardising, automating and formalising processes can reduce budgetary slack (Chenhall, 2003). The use of a single data set can assist in aligning budgets and forecasts to operational plans and strategic objectives, increase the speed of preparation and accuracy, and reduce the instances of data ‘silos’ whereby some areas of the business are disconnected from others and need to be reconciled. However, universities tend to use multiple systems for retaining data.

A lack of a working knowledge of complex forecasting techniques does not appear to prevent effective forecasting (Makridakis, Wheelwright & McGee, 1983; Sparkes & McHugh, 1984) particularly as judgement is commonly incorporated within forecasting. Indeed, firms demonstrate a preference for judgemental and unsophisticated techniques (McHugh & Sparkes, 1983). Pant and Starbuck (1990, p.442) noted that: “A general law seems to be at work: More complex, subtle, or elegant techniques give no greater accuracy than simple, crude or naive ones”. However, a lack of market research data hinders the production of valid forecasts (Fildes & Hastings, 1994).

Whilst many of the factors which forecasters view as influencing the accuracy of their forecasts are outside of their control, such as instability in the economy (McHugh & Sparkes, 1983; Sanders & Manrodt, 1994), there are areas that can be addressed, including improved data quality, greater management support and better training (Sanders, 1992; Sanders & Manrodt, 1994). Using a combination of forecasts prepared under differing methods may also improve accuracy (Mahmoud, 1984).

The literature demonstrates that differing approaches are adopted in the pursuit of accuracy, but that there is no single approach that would ensure it is achieved.