All large, multinational companies need to have a good cash and liquidity forecasting process. However, cash forecasting can be an extremely challenging task.
Companies of all sizes struggle with accounting for unpredictable cashflows and unexpected expenditures in their forecasts (we address these concerns in this article on the power of clean data). This article, however, reviews the key challenges that are unique to larger businesses, and offers practical advice for how to overcome them.
To get straight to the root of the difficulties in managing a forecasting process, we have categorised these challenges into five main areas.
- Reporting and analytics
- Intercompany reconciliation
One of the key challenges of cash flow forecasting in a large organisation arises because of the sheer number of people on whom there is a data reliance. In multinational companies these people are often spread across different departments and in different business units across the globe.
The wide array of data sources means it can be difficult to ensure quality from all sources. For example, when a data request is submitted to person or department who misinterprets that request, the data that is returned will not reflect the question that was asked.
Compromised data inputs mean poor quality forecast output. “Rubbish in, rubbish out”, as the saying goes.
To address this issue there are two key steps that you can take:
Step one: Conduct an in-depth review
Before a new cash forecasting process is implemented, reviewing the company’s historic cash flows can provide a significant level of insight into the nature of key cash movements. Recording and understanding these movements helps to inform future cash flow projections.
Step two: Maintain a continuous feedback loop
This element of communication is key. When the actual data flows are captured, a feedback mechanism must be included to update those who input data on their varying degrees of accuracy. This feedback mechanism should click in as further forecast version are submitted (and further actuals captured) to drive continuous improvements in the overall output data quality.
Another problem that stems from the large number of people that feed into the process, is ensuring ongoing and continuous engagement from all parties. The problems presented by a lack of engagement include inaccurate data being submitted, missed reporting deadlines, and therefore poor output quality.
These issues can be mitigated by following the below strategies:
Gain key executive sponsorship of the process
Getting buy-in from senior management (usually the CFO or finance director) is key to getting people to engage with the forecasting process. Ideally, a clear communication should be sent by the CFO which signals his or her commitment to and support of the process. This should be sent at the start of the process to ensure everyone is aware of the importance of their contributions, and keep them sufficiently committed from the outset.
Reward effort with value
The business units that contribute data can sometimes perceive the cash flow forecasting process as a one-way street. In other words, they sometimes take the view that they spend significant amounts of time to compile and collate the required information but see little value in return.
Finding a way to reward all of this effort with some value back to the business units can help to shift this perception. Further information on how to do this is noted in the solutions section below.
Reporting and analytics
At the end of the data consolidation process, once the forecasts are produced, the focus switches to reporting and analytics. Depending on how manual and spreadsheet reliant the process is, improving this reporting and analytics side of the process may prove difficult. However, it is important to note that this is the stage that uncovers the insights which add real value back to the business.
With that in mind, making improvements in the areas below can yield the greatest benefit:
- Historical trends vs forecast analysis. Analysing the way cash has trended in the past can inform the forecast by offering suggestions as to projected future cash movements. Also, making a comparison of historical trends versus projected cash moves offers an intuitive, quick and easy way to sense-check the forecast and identify and potential problems.
- Actual data vs forecast analysis. This is most straightforward analysis and is conducted by measuring the variance between forecast and actual data. Improvements can be made by analysing the drivers behind this variance.
- Forecast vs forecast (forecast performance) analysis. This can offer greater insight than an actual versus forecast analysis. Measuring multiple forecast versions against one another as the actual closing date approaches can provide early warning signals and help to anticipate changes in cash movements.
All types of analysis, including those listed above, can be improved by deploying various data visualisation techniques. We explore how this can be achieved in this article on cash forecasting data visualisations.
The size and scale of large, multinational organisations mean that intercompany cash movements can cause significant distortions for head office teams trying to produce forecasts for the business as a whole. While intercompany movements should always net to zero, the potential that the move may not be captured on both sides of the transaction simultaneously, gives the risk that significant sums of cash may be counted twice (or discounted altogether).
The most effective step to take to overcome the challenges posed by intercompany reconciliation is to use a dedicated counterparty driven reconciliation tool.
The main issue caused by the number of sources that feed into the process is the level of manual administration required to compile, collate, and process all of that information. Often all or much of this data is stored, processed, and managed in spreadsheets. The vast amount of data means that these bloated spreadsheets are unwieldy and vulnerable to crashing and slow load times.
Additionally, for multinational corporates, a comprehensive cash flow forecast includes multiple currencies, adding a further layer of complexity and administration. The burden of checking all of this source and output material is a hugely time-consuming process if done manually.
Moreover, the levels of manual intervention required can compromise the integrity of the output due to the increased risk of human error.
The burden of administration, and the other challenges of cash forecasting specific to large, multinational companies, are best addressed with the use of a specialised cash flow forecasting software tool.
To outline the practical ways software addresses these issues, let’s review:
- Communication. The required continuous feedback loop can be built into a process that is constructed around software. This ensures that all parties receive an automated notification when actuals are captured and informs them of the variances.
- Engagement. The easiest way to offer value back to the business units that are contributing data, is to use a system that can pull the precise insights that are relevant to that particular business unit. With no further administration from head office, the system can then package these insights and send them back to the business in an intuitive format that adds real value directly to them.
- Reporting and analytics. By using a software tool that specialises in forecasting and analytics, the side of the process that offers the most value can be improved dramatically. In a practical sense, this is because the use of software can uncover insights that might have gone unnoticed by manual analysis.
- Intercompany reconciliation. As noted above, using a dedicated counterparty driven intercompany reconciliation tool ensures that intercompany movements always net to zero. This means that as soon as cash moves are captured on one size of a business (in any department in any part of the world) they are simultaneously reflected in the other.
- Administration. Arguably the biggest benefit of using software to manage a cash forecasting process is that of automation. Automation removes almost all of the manual administration from the process, and therefore offers tangible, practical advantages to a company. Most notably these are saving time, and reducing the risk of human error.