Understanding Cash Flow Forecasting Methods: Short Term vs. Long Term

What you’ll learn


Using proper cash flow forecasting techniques is essential for proper cash management.
Learn about long-term & short-term cash flow forecasting.


Short term forecasting

Short-term forecasting refers to planning and budgeting cash for a short period. The short period is less than a year, with a span of one to six months. This includes:

  • Minimizing short-term debt, idle cash, and cash buffers.
  • Optimizing short-term lending/borrowing decisions.
  • Planning adjustments for seasonal sales fluctuations.

What are the benefits of short-term forecasting?

What insights could short-term forecasting provide for your organization? The benefits are:

  • Reducing working capital costs
    Staying ahead of the curve by effectively managing the working capital by minimizing credit borrowed for funding operating expenses.
  • Measuring financial health
    Providing a fair analysis of the financial health of a company by determining the cash deficits or surpluses by tracking the cash flows.
  • Performing scenario analysis to take proactive measures
    Stress testing what-if scenarios to proactively avoid scenarios such as bankruptcy and prepare for a recession.

What are the disadvantages of short-term forecasting?

The pitfalls of short-term forecasting include:

  • High upfront costs
    Initial costs of leveraging technology are high, so cash-deficit companies often refrain from doing a regular forecast.
  • The complexity of Accounts Receivable and Accounts Payable
    A/R and A/P are hard to predict for the long term because of the sheer volume of invoices, trade cycles, disputes, discounts, etc.
  • Requires accuracy at a granular level
    Minor errors in data increase the variance in the forecast. If there are errors in the underlying data, it creates variance in the forecast at a higher level.
  • Delayed decisions due to delayed collaboration
    Time required for gathering data from different sources like banks, TMS, etc., and different teams like AR, AP, etc. result in delayed decision making.

How are short-term forecasts typically prepared?

Depending on the budget and complexity of data of a company, and the motive of their cash forecasts, various methods could be used in areas of businesses for their preferred purposes. The methods for preparing short-term forecasts are:

    • Manual projections
    • Receipts / Disbursements method
    • Rolling average, and allocation method

Long term forecasting

Long-term forecasting is done for a period ranging from six months to five years. It provides a bird’s eye view of a firm’s financial needs and availability of investible surplus in the future.

What are the benefits of long-term forecasting?

Long-term forecasting helps in avoiding last-minute hurdles. The advantages are:

  • Multiplying gains by maintaining cash reserves
    Planning outlays on capital expenditure projects in advance helps in staying economically secure. Taking actions towards FP&A goal increases profitability.
  • Evaluating variances to track performance
    Understanding variances in forecasts and digging into the focal point of error to make corrections and mitigate risks.
  • Better asset management
    Reviewing the quality, update, or replacement dates of assets lets you save for new acquisitions, and find buyers for depreciating assets.
  • Orderly deleveraging and leveraging
    Eliminating the need to maintain a higher cash buffer saves interest costs.

What are the disadvantages of long-term forecasting?

As the duration of the forecast increases, the accuracy decreases. Here are some disadvantages of long-term forecasting:

  • Siloed data on excel serve as bottlenecks
    Data crunching and consolidation into a single master sheet from various excel sheets is labor-intensive and reduces visibility.
  • Unpredictable nature of the economy
    There is always a lack of reliability in long-term forecasts due to unforeseen economic downturns.
  • Lack of historic information
    This might create havoc for businesses that don’t have sufficient historic data. If the projections fall short, the firm can suffer monetary repercussions.
  • Lack of right set of technologies
    Ideal forecast development boils down to selecting the best-fit forecasting method, but it’s difficult to choose a technology that is most suitable for your business.

How are long-term forecasts typically prepared?

Generally, the adjusted net income method is used for long-term forecasts. The data required for preparing the adjusted net income forecast is acquired from the corporate budgets. The net income method monitors working capital changes and foretells financial requirements. The major downside to this method is that it does not allow tracing individual cash flows despite it being a great tool in the arsenal for showing the aggregate impact of fund flows.

Frequently asked questions

What is Cash Flow Forecasting?

Cash Flow Forecasting measures an organization’s future financial position and determines its liquidity position. Cash forecasting is important for making informed decisions for investing and borrowing. It helps in handling a company’s capital structure, financial and interest rate risks, and making adjustments to the budget.

The two types of forecasting are short-term forecasting and long-term forecasting, which may also be known as the direct method and indirect method of forecasting.

What are the best metrics for forecasting cash flow?

Using data from accounting statements, these metrics should be monitored regularly:

  • Debt-to-equity ratio: Total liabilities / Shareholder’s equity = Debt-to-equity. The debt-to-equity ratio determines a company’s vulnerability to market changes. Liabilities should be less than equity.
  • Days Sales Outstanding (DSO): It’s the average number of days to collect receivables. A shorter DSO ensures better liquidity.
  • Days Payables Outstanding (DPO): It’s the average number of days to pay the suppliers. DSO should always be shorter than DPO, which ensures enough money in hand to pay bills.
  • Days of Inventory Outstanding (DIO): It’s the rate at which the inventory turns over. Shorter DIO makes a positive impact on working capital.

What are the best practices for cash flow forecasting?

Treasurers should focus on both short-term and long-term forecasting to offset potential losses. While short-term cash forecasting projects when money is going to hit your bank account; long-term forecasts support plans for expansion and hedge maturities.

Suitable variables must be used for forecasting. Selecting the correlated variables and finding the right model for performing the forecast offers better results.

What are the best tools for cash flow forecasting?

Cloud computing is a win-win solution for forecasting since all the data are stored in one place. It eliminates the need for manual data aggregation and consolidation, thus minimizing the scope for errors.

According to a 2017 survey by PwC, 50% of companies mentioned a lack of automated tools as a challenge for forecasting.

Automation serves as the right hand of the CFO. The presence of RPA, ML, and AI increases the accuracy of the forecasts, hence saving time for value-added activities. Appropriate models provide great assistance when there is a surge in complexity. This increases confidence and makes decision-making and reporting coherent.

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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.