Why is it Challenging to Forecast Accounts Receivables?

What are the common methods for A/R forecasting?

The common methods to forecast A/R are:

• Using average DSO to predict A/R:
  The first step is to calculate the Days Sales Outstanding using the formula:
  
  Days Sales Outstanding = (Accounts Receivable/ Total Credit Sales) x Number of days
  
  

  You have to determine the credit sales expected during a specific period by analyzing the historical data. Then calculate the A/R forecast using the formula:

  
  
  Accounts Receivable Forecast = Days Sales Outstanding x (Credit Sales Forecast/ Time Period)
  
  
• The ADP (Average Days to Pay) approach:
  It is used widely across organizations to forecast A/R where
  
  the Predicted Payment Date is calculated as Invoice Date + ADP.
  
  

  Since the ADP varies for different customers and different invoice amounts, it increases the complexity to be calculated using Excel.

  
  
• Aging the accounts receivable:
  This method helps in analyzing the extent to which each of the customers is overdue on their payments, and determines their payment patterns over a timeframe. Investigating each debt-owing customer helps in estimating the likelihood of them paying the amounts owed. The traditional approach provides limited information and lowers the confidence for long-term forecasts. In some situations, this may increase the need to maintain high cash buffers for emergency purposes.

Forecasting A/R with direct and indirect forecasting

A/R forecasting can be carried out by using either direct method or indirect method of forecasting depending on the needs of an organization.

A/R forecasting using the direct method

Direct A/R forecasting is suitable for short-term liquidity management purposes and it follows the bottom-up approach where transaction-level data is gathered from different teams and rolled up to the central treasury to create a global forecast. The direct method is preferred when there is a need for more detailed information at a granular level for drawing insights. Direct forecasting provides high accuracy within a short-term period.

A/R forecasting using the indirect method

Indirect A/R forecasting follows the top-down approach for forecasting changes in accounts receivable and cash as line items in the projected cash flow statement and as ending balances in a projected balance sheet It uses the three statement method with pro forma balance sheets, cash flow statements, and profit and loss statements. This method is mapped to the longer-term planning needs of a company.

Why is it so challenging to accurately forecast A/R?

Forecasting accounts receivable encounters various challenges due to the following reasons:

• Data scattered across various data sources:
  Manual data mining from different sources and cross-departmental collaboration takes up sufficient time and effort and lacks fine-grained data visibility.
• Inflexible and error-prone tool:
  Spreadsheets, while fed with the sheer volume of transactions, make room for manual errors, and allow only static forecasting methods which lower the accuracy.
• Dissimilar customer payment behavior:
  Each customer has different payment patterns, and each company has a different market size. Therefore payments might not be made at the expected time.
• Economic volatility:
  The major concern for longer-term A/R forecasting is the unpredictability of market dynamics that causes inaccurate projections due to macroeconomic factors.

The factors that may cause variability in A/R include:

• Different customer payment behavior/patterns
• Discounts and disputes
• Business cycle

The above complications limit forecast accuracy, which hinders efficient financial and resource planning. Furthermore, reporting such inaccurate forecasts decreases credibility in and beyond the company. Thus it is very important to select an optimal process and/or tools for forecasting A/R accurately.

What are the specific factors to keep in mind when accurately forecasting A/R?

There is no one-size-fits-all method to accurately forecast A/R. But, there are some best practices that leading-edge companies follow to overcome A/R forecasting challenges. Here are few tips to improveA/R forecasts:

• Split the A/R  into various subcategories such as regions, customers, companies. Drill down into the historic data to analyze customer payment terms and credit scores. This will help in detecting bottom-line concerns and slow-paying customers.
• Add more variables to predict invoice payment dates accurately. If you add 50+ variables, it will provide a more realistic and holistic view. But, it is difficult to tweak as many as four variables manually, let alone track 50+ variables. This is where automation can help to generate reliable forecasts.
• Adjust your forecasts according to the seasonality at the micro and macro level and adjust your assumptions accordingly. Monitor the variance between forecast vs. actuals more regularly.
• Leverage advanced analytics, centralized systems, and automated models to consolidate all the A/R forecasts into a global forecast to run forecasts frequently and easily. The use of Artificial Intelligence in cash forecasts saves time and improves prediction accuracy.
  Here is how AI automates the A/R forecasting process:

An AI-based cash forecasting solution provides real-time information, improves visibility with dashboards, and refines forecasts with time.

Incorporating richer data sets and robust tools provides deeper insights into A/R cash forecasting. This helps CFOs and treasurers to carefully assess volatile components and implement corrective measures, hence enabling confident business decision-making around liquidity and revenue growth.

Frequently Asked Questions

Why is accurate A/R forecasting so important?

Accounts receivable is one of the drivers that helps in assessing the liquidity and financial health of a firm. Not keeping track of accounts receivable leads to high DSO, thus negatively impacting the financial status. In order to maintain sufficient liquidity, forecasting accounts receivable at a granular level is a proper way to go ahead. However, an inaccurate A/R forecast leads to repercussions such as:

• Shortsighted business decisions:

If the A/R forecasts are inaccurate, the false base data could lead to poor decision-making on investments, or dividends.

• Poor creditworthiness: 

If your past dues are high, you might be offered low credit lines from the credit agencies.

• Increased interest rates:

Relying on an unclear or inaccurate view of your current balances would position you to borrow loans at high interest rates.

What are accounts receivable?

Accounts receivable or A/R represent the amount of invoiced sales that have not yet been received as cash.

The difference between Accounts Receivable and Accounts Payable is that A/R is the amount that customers have to pay to a company, whereas A/P is the amount that a company has to pay to its suppliers. Moreover, A/R falls under the bracket of assets whereas A/P falls under liabilities. Accounts receivable is often confused with collection, but collection refers to the amount still owed past the due date. Collection is a source of income/revenue and profitability for the company, but unlike A/R, it doesn’t fall under assets or liquidity.