Necessities of Cash Flow Forecasting Model

Cash management excellence is possible with accurate cash flow forecasting. Cash is, as the saying goes, the lifeblood of a business and will always be so. The most recent cash flow forecasting tools are powered by intelligent technology to produce accurate cash forecasts that reduce manual effort and increase operational excellence.

Cash flow forecasting involves accurately predicting cash outflows and inflows over time. For short-term borrowing optimization and normal investing, accurate cash flow forecasting is essential. Cash forecasting models are more important during economic uncertainty. When cash resources are not sufficient to finance current or future operations, major actions such as staff reductions, delayed payments to suppliers, and increased borrowing may be necessary.

Payroll, disbursements and dividends, debt repayment, collection of receivables, and royalties are all major cash flows. Like many business processes, cash flow forecasting can be improved by using digital technologies like machine learning (ML), artificial intelligence (AI), and

These are the essential elements of cash forecasting using digital technologies:

• Consolidation of account receivables data between sections and ERPs
• Predictive Modeling
• Simulation
• Monitoring and Tracking

Let’s take a look at each one

Consolidation of AR data between divisions and ERPsA company’s cash inflow forecasting must include all cash. It is crucial to automate the consolidation of all data across different units within the company, which may have different ERPs. It is essential to have a global view that displays different currencies. Finance staff will need to create the forecast semi-manually by using Excel cash flow forecasting. This can be time-consuming and takes a lot of time.

Predictive Modeling

This is the engine for cash forecasting using AI/ML. It is best practice to have multi-dimensional forecasts. Forecasts should be made using multiple methods, with the expectation that they will all converge on a narrow range. These are the key dimensions:

• “Ideal” predicts cash receipts if each invoice is paid by the due date. This is more of a forecast than an upper limit.
• Predictive payment behavior that forecasts invoice and customer receipts based on historical payments performance and predictive analytics. The historical average days until payment is used to calculate about 80% of cash flow forecasts. Artificial intelligence is used to predict the payment date. Both forecasts use machine learning to continually improve their forecasts. This results in high forecast accuracy, but it requires the power of digital technology.
• Collector forecasts are an accumulation of all Promises to Pay logged in the collections module. Accuracy is dependent on the quality of input from collectors. Another alternative is to use the monthly aggregate cash collection targets.

Analysis Simulation

This capability can be used to confirm the accuracy of forecasts and to plan for cash shortfalls. The key capabilities include extensive drill-down (to invoice level and customer level) to get details about the forecast and a robust simulation called “What If” to see the consequences of incorrect forecasts.

Tracking and Monitoring

Cash flow forecast accuracy can be improved by looking at variances between actual and expected, understanding the cause, and correcting data sources, methods, etc. This is an iterative process that can prove to be a powerful tool. Again, drill-down capabilities are a key enabler of improvements.

Forecasting cash is crucial in this uncertain economy. It is essential to forecast cash accurately and efficiently. Semi-manual forecasting might not be accurate enough and could divert staff from driving cash flow to predicting.

Digital cash forecasting allows you to quickly analyze large amounts of data and make predictions with greater accuracy.