The four elements of cash flow forecasting are:
1. Approach: The approach for building the forecast can be either art or science(subjective or objective.)
2. Data gathering: Data is extracted from a plethora of data sources and teams.
3. Modeling: Different models are used for forecasting different cash flow categories.
4. Variance analysis: It is performed by using appropriate tools to understand deviation between the forecasts and actuals.
The four stages of the cash forecasting maturity model are classified as Laggards, Proactive, Strategic, and Best-in-class. Their cash forecasting practices are as follows:
1. Laggards: Perform cash forecasting based on past experiences and last year’s data along with some adjustments to create recent forecasts.
2. Proactive: Build forecasts through proper collaboration between managers, controllers, etc.
3. Strategic: Create top-down forecasts from FP&A forecasts and set distribution rules.
4. Best-in-class: Create forecast bottom-up from entity-level up to global forecasts.
1. Laggards: Have a manual process and use previous years’ data as a basis for the current year.
2. Proactive: Have a manual process, but use the previous years’ data and add forward-looking adjustments by incorporating the latest data.
3. Strategic: Automate their data gathering process and use previous and current data for cash forecasting.
4. Best-in-class: Use APIs for connectivity between disparate systems to gather real-time information from FP&A, sales forecasts, ERPs, A/P, and A/R.
1. Laggards: Use spreadsheets for forecasting cash flow categories. Spreadsheet limits adding multiple variables, causing forecast inaccuracies in complex categories such as A/R and A/P.
2. Proactive: Use the average-based approach, which is helpful for categories like payroll, taxes but doesn’t give enough forecast accuracy for A/R and A/P.
3. Strategic: Use data from open ERP and banks, and adjust due dates to generate A/R and A/P forecasts.
4. Best-in-class: Use current and historical data, and use AI models for A/R and A/P and heuristic models for simple cash flow categories.
1. Laggards: Don’t perform variance analysis due to lack of adequate data or tools.
2. Proactive: Perform variance analysis at the global level but for a single duration.
3. Strategic: Perform variance analysis at entity and cash category level with proper tools.
4. Best-in-class: Use better tools and models to perform variance analysis at the entity, region, cash flow category level over multiple durations.
To become best-in-class in cash forecasting, it is important to understand the cash forecasting performance levers and how to minimize the challenges associated with the performance levers.
Cash forecasting depends on three key factors such as:
1. Visibility: It refers to the ability to track daily cash movements and view cash forecasts by categories, regions, and entities.
2. Accuracy: It refers to the efficacy of the forecasts required to make confident decisions.
3. Frequency: It refers to the forecasting cadence required to make timely decisions. The forecast frequency can be daily, weekly, monthly, quarterly, or yearly.
Visibility is lowered due to scattered data across various sources such as TMS, ERPs, bank portals, sales order systems, etc. Low visibility also stems from non-standard processes and currency fluctuations, which lead to forecast inaccuracy.
Forecast accuracy is lowered due to the following reasons:
1. Process inefficiencies such as:
2. Dynamic factors such as:
The frequency of forecasting is not timely enough due to the high turnaround time in generating those forecasts. The vicious cycle of cash forecasting leads to errors and data overload. As a result, treasurers rely on static and outdated reports for executing decisions.
Visibility is improved by building local forecasts from entity-level such as geographies, company codes, currencies, and cash flow categories and rolling them to global forecasts.
Accuracy is increased by following these best practices:
Forecast frequency is very subjective since each company has different sizes, systems, technologies, and needs.
These are the 10 ways to deliver best-in-class cash forecasts:
The purpose of cash forecasting needs to be aligned with the company’s cash position and its priorities. Based on the cash position, companies fall under two categories:
Understand the key cash flow categories such as A/P, A/R, payroll, taxes, etc., and then determine what’s unique about the company and the category that needs to be focused on the most.
Understand if the company’s current situation demands re-forecasting or continuous variance analysis and determine the forecasting cadence.
Analyze where data is captured from if it is gathered straight from the source or manually. Understand how you can capture data faster and limit errors. Evaluate centralization against decentralization to understand what suits the company.
Identify if the forecasts are subpar or not timely enough, and identify the barriers.
The inherent barriers for inaccurate and untimely forecasts can be:
Determining the most suitable technology that aligns with the processes, systems, and requirements helps generate fruitful results.
The various treasury technologies and their use cases are:
There should be enough visibility into A/R, A/P, cash movements, FP&A activities, etc., to make effective decisions.
Find ways to collaborate effectively and easily and gather data from the right people.
Establish a standardized process and follow the best practices around cash forecasting across data gathering, modeling, variance analysis, and reporting.
Monitor time allocation, mitigate non-value-added activities and set the right incentives to enhance resources and processes.
The HighRadius™ Treasury Management Applications consist of AI-powered Cash Forecasting Cloud and Cash Management Cloud designed to support treasury teams from companies of all sizes and industries. Delivered as SaaS, our solutions seamlessly integrate with multiple systems including ERPs, TMS, accounting systems, and banks using sFTP or API. They help treasuries around the world achieve end-to-end automation in their forecasting and cash management processes to deliver accurate and insightful results with lesser manual effort.