Cash forecasting is often described as an arduous task. However, it is a critical process that shows how much cash a business will generate and what cash will be needed to fund future working capital and expansion. In most organisations cash forecasting has a bad name due to the amount of time it takes to do it properly and the poor results it often produces. As with forecasting of any type the results calculated will never be 100% accurate and, in a world where absolute precision is demanded at all times, this can be disheartening.
The flip side of lack of confidence is over confidence. Forecasting with 99% accuracy the day before an event isn’t very impressive and ultimately provides little decision making value. Somewhere in between this false confidence and the valley of despair lies the point of most value for your company.
In this post we look at three key elements to cashflow forecasting best practice. A process that combines direct human input with cash flow information stored in systems and the projections generated by forecasting models will produce the most accurate and reliable results. The mistake most companies make is to focus too intently on one element, ignoring the others, in the hope that it will produce a respectable forecast. In many cases it will but it won’t produce something that you can use for decision making or strategic planning on a daily basis.
Forecasting user input
It could be argued that humans sit behind each of the three elements we discuss, which they do, but for this discussion we will treat them as the source of information that doesn’t sit in a system or can’t be modelled. Generally this information relates to special projects, acquisitions or seasonal events.
In many cases domain or situational knowledge, such as knowing a specific customer’s payment patterns, allows adjustments to be made to a forecast that will dramatically improve its accuracy. This critical thinking is the essential ingredient in any planning based discipline.
Cash forecasting systems
Many companies fall into the ‘over-automation’ trap when designing a forecasting process by assuming that they can fully automate their way to an accurate forecast. This often leads to huge amount of time and money being spent on projects that will only be judged successful if 100% automation is achieved.
Systems don’t provide a silver bullet solution but are important for two reasons. Firstly they contain information necessary to complete the forecast itself and secondly they help with the automation of manual processes and analysis of data. As such they play a very important role but only as part of an overall solution.
Cash forecast modelling
Modelling your way to an accurate cash forecast is almost as difficult as full scale automation. It’s convenient to think that a sophisticated spreadsheet model will provide the right answer as this would save a lot of time and effort. Modelling can produce a believable projection but it is unreliable for short and medium term forecasting where a greater level of timing precision is required.
Modelling is necessary however for certain components of a cash flow. Revenue and input prices are typically dependent on external factors such as customer demand, raw material costs and FX movements which can be modelled and then tweaked using the latest available information.
Understand what’s needed in a cash forecasting process
A cash forecasting process that captures information from humans, systems and models will produce the most accurate projections of future cash flow. No two companies will have the same cash forecasting requirements but an understanding of what each element can provide as well as its limitations will allow you design a process that provides maximum value to your organisation.
Bank reporting connectivity: the key to live cash flow visibility
Many people working in corporate treasury and finance teams struggle to gain visibility over current cash flow levels due to an unwieldy number of accounts. The vast array of different accounts means that the manual, slow, and administratively heavy task of accessing bank account information prevents any kind of real-time, ...John Champion - April 11, 2019
Product Updates: March 2019
Continuing our monthly Product Update series, this blog post offers a retrospective look at software upgrades made by CashAnalytics over the previous month. Below, you’ll find a close look at a couple of key improvements, which demonstrate our commitment to client lead product innovation. New: Updated User Interface Over ...John Champion - April 05, 2019
Big Data in Finance
As “Big Data” and analytics facilitate the finance team’s transition from cost-centre to strategic business partner, new opportunities are opening up for individuals willing to acquire the necessary skills. Why is this in focus now? In recent years, new technologies and lower computing costs have made it possible for ...John Champion - March 26, 2019