While a crystal ball that accurately predicted cash inflows would be a fabulous addition to any AR department, we are stuck with more mundane methods. It is possible, however, savvy credit pros to make accurate forecasts of cash receipts without the benefits of any hocus-pocus. Since many organizations struggle with forecasting cash flow, the credit function has an opportunity to provide additional insights regarding the cash receipts side of the equation.
The working capital division of The Hackett Group, a global strategic advisory firm, recently conducted a survey that found four out of five of the world’s largest companies cannot forecast their cash flow 2-3 months out within five percent accuracy. If you can’t forecast cash flow, planning goes out the window. When cash projections aren’t accurate, there will likely be missed business opportunities related to both capital expenditures and working capital investment not to mention a marginally higher cost of capital.
The Hackett Group survey did find some similarities between best-of-breed organizations that were successfully predicting their mid-term cash flow – the companies were doing it via automated or ERP-related systems and completing their forecasts in less time than their peers. These companies’ accounts receivable teams aren’t sitting with a simple spreadsheet, inputting data and attempting to accurately forecast cash that way. Forecasting cash flow requires a variety of analytics including best/worst case and what-if scenarios as well as working with other departments who have an impact on incoming cash. Though working capital management falls under the responsibility of a controller or CFO, accounts receivable performance is the credit department’s domain.
The key is to build more accuracy into your cash receipt forecasting models. For one thing, stop assuming all promised payments will arrive when promised. We all know promises can be broken, so look at the percentage of payments that haven’t shown up in the past and the amount of time it has taken for those payments to arrive then work that information into your forecasting system. Moreover, there may be a difference between collectors, one realizing a higher percentage of kept promises than another.
With an automated collection system, all payment data can be translated into intelligence concerning customer payment habits. This will provide some indications of what percentage of your incoming cash can be expected on time and how much may be late. Also, having close relationships with customers and tracking their performance patterns helps top-performing credit teams to better understand their clients’ own financial situations.
As with any major undertaking, set benchmarks into your system to help you judge the effectiveness of your collection activities and forecasts. These should include performance incentives for your collection staff to get very specific payment promises from customers, identify changing trends in customer performance and address problems before they affect collections. Also, don’t be afraid to make adjustments to your collection goals – in the short term the occasional tactical adjustment is to be expected and will be seen as a positive development when it serves to achieve preset collection goals.
Always make time for review, both from a strategic collection perspective as well as looking at individual customer performance and modifying your collection tactics based on those results.
Have you been successful in improving your cash forecasting performance? If so, what improvement(s) did you implement to become successful?
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HighRadius Cash Forecasting Cloud – an advanced forecasting system – leverages the proven RivanaTM Artificial Intelligence (AI) platform to provide the most accurate cash flow forecasts – right from a ledger account level and rolling up to the organizational level. Delivered as a Software as a Service (SaaS), the solution seamlessly integrates with your company’s ERPs, accounting systems, banks and order management systems. Multiple AI and Machine Learning algorithms process datasets including bank statement inflows/outflows, sales orders/customers invoices, purchase orders/vendor invoices and expense reimbursements for comprehensive as well as accurate cash flow forecasts. The closed-loop, machine learning feedback system ensures that the forecast models become more accurate with time.