At this time of year, articles offering their predictions ‘for the year ahead’ are about as common as overdrafts, grim weather and soon-to-be abandoned diets. And while some commentators show admirable candour in highlighting those of their predictions from the previous year that were incorrect, these are not always the sort of predictions that business leaders find the most interesting.

That kind of forecast is much more detailed, much more demanding to get right – and much less often scrutinised when reality does not match expectation. The predictions that interest those responsible for the largest companies are both highly granular, and totally interconnected with the wider economy. That ought to imply a very sophisticated approach to creating them. Too often however, this is not the case, and forecasts are generated within the business from a simple combination of historical run-rate and the management’s growth targets.

A single inaccurate prediction within a company of many divisions may not have much overall impact but, when there is no formalised or developed process for making predictions, personal opinion and bias can create so many inaccuracies as to render the company-wide forecast functionally useless. Creating an effective framework is vital.

The first step is to check whether forecasts within the organisation have historically been accurate. If no formal process exists, then it is likely that they will not be – however, this retrospective analysis offers both a baseline for improvement in accuracy, and clear motivation for making that improvement.

The next step is to accept that the development of forecasting methods is an ongoing process, and must change as the organisation and its markets change – just as forecasts must be recalculated to take account of developments in the economy and within a company.

Once that is done, a framework can be put in place.  For companies with a sales cycle and forward order book, the foundation is to analyse historical data from the marketing funnel and sales pipeline in order to build an objective picture of likely business performance based on current data.

Companies that are more dependent on point-in-time transactional activity need to identify their key operational drivers and forecast these using a consistent set of assumptions about their internal business activities and the wider economic and market factors.

Whatever the type of business, it is important that forecasts can be routinely calculated from data collected within the business and, most crucial of all, the methodology should be easily comprehensible to the managers responsible for achieving performance.

Furthermore, analysing the variances between forecasts and actuals, and identifying the drivers of that variance, offers the opportunity to iteratively refine the forecasting process, as well as to identify how sales, marketing, customer management and operations can be made more effective.

To manage the future effectively, directors and their management teams need credible forward-looking information, focused on whether the firm is on track to meet its goals providing early warnings of where corrective action is likely to be needed.  Organisations that have this capability will be able to manage the future more effectively, both in 2016 and beyond.

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