Forecasting is part art, part science, part human intelligence, part machine intelligence. While forecasting is done differently across industries, in all cases, treasury first must understand the sources of data and how they impact corporate operations, and then apply technology to make meaning of the data.
Relevant information used for forecasting can be found within the siloes of an organisation and beyond the enterprise, creating complexity, limiting data sharing, and resulting in inefficient data gathering processes. Costly searches and manual sorting lead to data integrity problems, which compound as organisations expand and acquire businesses locally and globally.
Once sources of data are understood, the next step is to centralise and normalise the data into a single source of truth. This can be accomplished through imports/exports from many source applications to an open, cloud-based TMS platform, using APIs and robotics to automate this process seamlessly, without human intervention. System-to-system communication eliminates human errors, and by removing manual information gathering, teams have the time and the analytical power to perform strategically, rather than operationally. This is beneficial especially as market conditions change.
When data is centralised, it must be flexible enough to manipulate. By default, treasury reverts to using Excel because users can quickly change the data. However, spreadsheets do not allow management to audit or identify trends, and they only provide static views for manual comparison. While management wants to give their teams autonomy to amend data as their business changes, they must still be able to measure changes in forecasts and monitor patterns dynamically. Treasury systems today automate this process, and if they are SaaS-based, they allow everyone to work consistently off the same application, regardless of where they are globally.
After treasury has extracted relevant data, centralised it on a common treasury management platform, and has been able to amend, analyse and archive different versions of forecasts, the TMS can perform the heavy lifting. Treasury management systems should be able to apply sophisticated simulations to predict financial viability over time using historical events. Advanced calculations, AI and machine learning is the science behind the art of accurate and efficient forecasting. Treasury management systems that can identify data patterns can bring organisations one step closer to predicting the future. Once implemented, statistical analytics become any organisation’s true competitive advantage.