Why Should Treasury Use Centralized Cash Forecasting

Importance of timely cash flow forecasting

Cash forecasting should be done frequently and timely because of the following reasons:

• The economy is prone to volatility
  Cash flows are prone to market fluctuations such as:

• Inflation, recession, and pandemic
• Currency fluctuations
• Seasonality in business cycles

• Sources and uses of credit are changing rapidly
  Frequent changes happen in:
  • Trade credit
  • Bank credit
  • Consumer credit
• Timely decisions are necessary
  Timely forecasts are required to make decisions on:
  • M&A, dividends
  • Investments and borrowing
  • Liquidity planning

How does the traditional way of cash forecasting work?

The traditional way of forecasting is subjective in nature and dependent on the instincts of treasurers. In the traditional way of forecasting:

• Data gathering is done manually by a treasury analyst from A/P, A/R, and accounting teams.
• Data entry is done in spreadsheets, making it prone to human errors.
• Human intelligence is applied to make manual adjustments to the forecast due to limited data visibility and accuracy.

Impact of traditional cash forecasting

The traditional way of forecasting doesn’t leave enough time to focus on strategic activities.  The impact of traditional forecasting are:

• Poor decisions: Inaccurate decisions are made for liquidity, mergers and acquisitions, and repatriation.
• Untimely decisions: Borrowing and investments are delayed, which leads to delayed returns and value depreciation.
• Misallocation of labor: Maximum bandwidth is wasted on manual tasks instead of focusing on strategic tasks such as risk management.
• Mismanagement of capital: High cash buffers are maintained to offset any unforeseen costs instead of maximizing returns of idle cash.

Four drivers for global cash forecast

The four drivers for the global cash forecasting to work optimally are:

1. Timeliness: Forecast close to real-time to drive timely business decisions.

2. Scalability: Standardize processes so that they can be easily replicated to other entities.

3. Granularity: Improve the ability to deep-dive into invoice-level details to drive course correction.

4. Accuracy: Achieve the baseline accuracy for the business to make confident decisions.

How to achieve timeliness

Timeliness can be achieved by:

• Gathering the best data available continuously.
• Updating forecasts frequently to provide up-to-date inputs for business decisions.

How to achieve scalability

Scalability can be achieved by:

• Making the process flexible so that it can’t break when a new system or bank is added.
• Making the process easy to be replicated or halted when a new company is acquired or divested.
• Ensuring that once the system is running, resources can focus on value-added activities.

How to achieve granularity

Granularity can be achieved by choosing centralized forecasting over decentralized forecasting.

Understanding centralized forecasting

Centralized or direct-method of forecasting:

• Approach: A bottom-up approach where local forecasts are rolled up to the central treasury.
• Granularity: It supports granular analysis with better visibility of cash flow.

Impact of centralized forecasting

The impact of centralized forecasting are:

• Better decision-making in terms of borrowing and investing.
• Continuous data visibility across all systems to improve strategic financing decisions.
• Better understanding of how transaction-level activities influence global forecasts.
• Drill-down capability to specific customer accounts or even transactions to determine sources of potential variance.

Understanding decentralized forecasting

Decentralized or indirect-method of forecasting:

• Approach: Local forecasts are created at the entity level by gathering data from internal teams.
• Granularity: There is limited granular visibility.

Impact of decentralized forecasting

The impact of doing indirect-method of forecasting are:

• No continuous data visibility or drill-down capability.
• High turnaround time for creating a consolidated central treasury forecast.

How to achieve accuracy

A certain baseline level of accuracy helps to better manage liquidity and make strategic decisions.

What is baseline accuracy?

Baseline accuracy is the minimum accuracy required for companies to make business decisions. It varies from business to business.

Why are most cash forecasts not accurate?

The reasons for the inaccuracy of cash forecasts are:

• Lacking the right variables related to customer behavior, historical data, etc.
• Using spreadsheets to forecast complex categories like A/P and A/R.
• Performing no variance analysis.

How can forecasting accuracy be improved?

Forecasting accuracy can be improved in the following ways:

Using emerging treasury technologies such as:

• • Robotic process automation (RPA):
    • Programmed to run a certain set of tasks with little to no human intervention.
    • Enabled to free up a lot of time for treasury teams from doing repetitive tasks like data gathering and consolidating, and focus more on higher-value duties like cash forecasting, which remains as the top priority for Treasuries.
    • Tweaked to perform a different or updated set of tasks.
  • Machine learning (ML):
    • Consists of data mining software and predictive analytics.
    • Discerns or embarks on an appropriate course of action.
    • Intervened by humans to define the environment.
  • Artificial intelligence (AI): 
    • Utilizes self-learning algorithms to identify patterns and changes.
    • Track difficulties in transactions and stop them or find shifting behaviors in operations and suggest better alternatives.
    • With complex computing power and process automation, the scope and utilization capabilities of AI are virtually limitless.

Using the right models for each cash flow category

AI is used for forecasting complex operational cash flow categories such as A/R and A/P. On the contrary, for more predictable and stable categories such as payroll, taxes, CAPEX, heuristic models are more suited.

Performing variance analysis for multiple durations 

Performing variance analysis frequently helps in:

• • • Identifying shortcomings within the forecasts.
    • Improving the accuracy of the forecasts.