One would imagine that in a world where smart cities and virtual reality are becoming a part of daily life, treasury and finance departments would have perfected their cash forecasting by now, but that isn’t always the case. Both PwC’s & Deloitte’s recent Global Benchmarking studies highlighted managing cash and liquidity risk as the most important treasury challenge.
But managing and forecasting the cash flows of a complex system of multiple ERPs, FX exposures and geographic entities, combined with increased global uncertainty, tax changes, interest rate rises, and regulatory change, is not easy. Still, having an accurate cash flow forecast and understanding the underlying drivers is essential to a company’s wellbeing, as it can help identify potential future problems. Many companies are therefore trying to improve their cashflow forecasting, but with variable results and accuracy.
Three key factors can transform bad cash forecasting (not transparent, inaccurate and time-consuming) into good (accurate and efficient).
1. Drill down into your actual cash flow drivers with transaction-level / granular data
Many corporate treasurers aim at an accurate cash forecast through a combination of effective cash flow drivers and assumptions. But to what extent do they have a good view of these cash flow drivers? Do they know what is really eating and feeding their cash? The classic TMS will typically consolidate basic forecasted flows from the different OpCos but such forecasts are consolidated from the underlying business transactions. This blurs the insight in the real cash flow drivers and is no guarantee of the quality of the data.
It is important to have clear and error-free access to the underlying business transactions. In a recent PwC study, only 6% of respondents said they made use of the inputs at the transactional level. But thanks to advances in technology, particularly big data analytics, treasurers can have instant access to the details of the underlying cash movements and are able to drill down to the transaction level. In the gif below, you can see what this means in practice:
The graphic demonstrates how easy it is to identify exactly what drives your cash flow in a certain period when using the right platform. A click-through interface provides insights per month, quarter, week and day including access to transaction level details.
2: Applying the right forecasting logic
Cash flow forecasting is often associated with spreadsheets and manual work. Treasurers have to use Excel to calculate their forecasts, because TMS do not typically offer the required flexibility.
Combining all one’s data sources and applying the right rules to generate a good forecast is complex. Vendor payment behaviour is an example. It makes sense to enrich invoices and sales orders details with data on when vendors actually pay, but many companies struggle to take this data into account, usually because they haven’t set up the appropriate algorithms so their forecasts prove inaccurate and they have to spend a lot of time explaining why. A set of smart logic algorithms is invaluable. Progressive companies are using technology-driven, smart engines to calculate and automate their cash forecasts, take over the manually intensive work and propose logic to improve future forecasting.
Above you can see how a smart engine works in practice. Cash flows are projected into the future (blue line) using forecasting logic. The dotted orange line represents a scenario with one or more of the underlying assumptions changed and immediately shows the impact relative to the blue line.
3: A good forecast is one that is used to drive action
However good your forecast is, it might be underused, or not used at all. To make a real impact, actions should be derived from the forecast results. There is a lot of potential in accurately predicting what might happen in the future and this potential should be translated into value.
There is even more value in considering multiple scenarios by changing some of the underlying assumptions (e.g. changing the day or frequency of your payment runs). When working in Excel or a TMS, changing assumptions can trigger much additional manual work and is often avoided. It makes sense to build multiple forecasts and assess the impact of each scenario on cash optimisation. Together these can transform finance departments into business partners for fuelling a company’s growth.
The orange line reflects a scenario, built by the user. This provides a comparison between the current forecast (full blue line) and a different scenario (based on assumptions made by the user). A simulation engine immediately shows the impact of different scenarios, a powerful tool for a finance department.
Mark O’Toole heads up the Americas for Cashforce, a big data analytics & TMS technology provider focused on cash management, forecasting and working capital. www.cashforce.com