Following on from our recent article on overcoming cash forecasting challenges, this piece focuses on the area that is often the most challenging, accounts receivable forecasting. For large corporates, forecasting accounts receivables is particularly challenging as the forecast figure is consolidated from a wide variety of inputs. In addition, as accounts receivable (AR) is often
Following on from our recent article on overcoming cash forecasting challenges, this piece focuses on the area that is often the most challenging, accounts receivable forecasting.
For large corporates, forecasting accounts receivables is particularly challenging as the forecast figure is consolidated from a wide variety of inputs.
In addition, as accounts receivable (AR) is often largely made up of customer receipts, the timing element is hard to predict as it is out of the forecasting company’s hands. Though payment terms may be contractually mandated, they might not always be adhered to, thus adding a significant element of unpredictability to the process.
It is useful to break AR forecasting into two parts, long-term forecasting and short-term forecasting. Long-term forecasting of accounts receivable tends to be easier because customers will, generally, pay their bills eventually.
Short-term forecasting, on the other hand, tends to be considerably more challenging. This is because payments may be withheld and delayed for a number of reasons (difficulties in the supply chain, or holding cash for reporting purposes, for example).
The following steps are built on our experience in helping large, multinational companies to set-up and maintain forecasting processes. Following each of these should help to increase overall forecast accuracy.
While a historical view will not perfectly match a forward looking one, a good understanding of the previous AR behaviours can help identify trends that are likely to continue into the future.
A good analysis of AR data should highlight:
Once these areas are highlighted, you will be able to focus attention where it will have the greatest impact. Following the 80/20 rule (i.e. spending 80% of the improvement efforts on the 20% of biggest contributing factors) will both speed up the improvements and make them more effective.
Following on from the analysis of historic data, accounts receivable can be broken down into further sub-categories. The purpose of the sub-categorization exercise is again to shift improvement efforts to the areas that will have the greatest impact.
Sub categories can include (but are not limited to):
Once any sub-categorizations have been made, applying the 80/20 rule can help to focus efforts on the areas that will have the greatest effect.
As with all elements of cash forecasting, improving AR forecasting accuracy is a continuous process. While the steps above provide a solid foundation for a good forecasting process, it is important to continually monitor and adjust assumptions as part of an ongoing drive for improvements.
The key analysis that will need by be conducted on an ongoing basis is a variance analysis between the forecast and actual figures. This analysis should reveal how any assumptions made in the process have performed, enabling them to be tuned and tweaked for the next forecast version.
Following a process of “monitor, update, iterate” will help to move a forecast from low quality to high quality after a relatively small number of forecast versions. (We discuss this in greater detail in our whitepaper discussing cash forecasting accuracy measurement.)
As with all elements of the cash forecasting process, the biggest improvements in terms of accuracy and time savings can only be achieved by automating the process with specialised cash flow forecasting software.
Automation removes the administrative burden from the process and reduces the risks of human error. We discuss how automation can aid the entire process in greater detail in this article: Cash forecasting automation: a practical guide.