For a CFO or treasurer, the cash-flow forecasting experience can be “jarring, uncomfortable, and almost always coming at a bad time,” according to John Kane, vice president of U.S. pre-sales at Treasury Intelligence Solutions (TIS).
That’s especially true if treasury lacks the systems, data, and processes to assemble the forecast. Not only could the organization’s operations be on the line, but so might someone’s credibility. The treasurer risks coming up short in the eyes of the CFO. The CFO risks looking unprepared in front of the board of directors.
It may be no fault of their own. The treasurer or CFO may be a new employee and inherited broken or nonexistent processes. Or finance lacks the IT systems to assemble a projection reliable enough for management to rely on.
Instead of having a good handle on cash coming in and going out, these finance leaders “may feel like they're playing in traffic,” said Kane, speaking at the New York Cash Exchange conference on September 12. ‘Waiting for something bad to happen because they can't see what's coming.”
But an organization can go from where a decent, timely cash forecast is impossible to one where the forecast can be generated regularly, understood, and useful to treasury for other functions.
For CFOs and treasurers who want to put the Holy Grail within their grasp, here are five tips to move you closer.
1. ERP System Data is Critical
A well-resourced company may have many systems — treasury management, billing, budgeting and planning, even business intelligence tools. But the enterprise resource planning systems (ERPs) are the critical ones from a forecasting perspective, Kane said.
The ERP system contains historical and current or projected accounts receivable and accounts payable data, including data points like invoice due dates, payment terms, and counterparty master data.
Avoid, however, trying to improve your cash forecast when the IT team is consolidating multiple ERPs or migrating the current systems to the “latest and greatest instance,” Kane said. In that case, “take cover during the migration process” and tackle the initiative when the dust settles, or “get a quick win on the forecasting side before the migration begins in earnest.”
2. Engage Those Who Have the Data
Who has the data that can contribute to the cash-flow forecast, and who is willing and able to share their data? This may not be clear at the outset.
“The only real success I've seen in dealing with [problems obtaining data] is when folks in treasury strongly engage those local teams or specific departments regularly."
VP of U.S. pre-sales, TIS
A treasurer or CFO who needs to collect data from plant managers, operations teams, and even local finance groups has to recognize that “forecasting might not be a high priority for them,” said Kane.
They might have issues collecting the data or collecting it when you want it, issues following your process, or filling out your template. “The point is that they have the leeway not to do everything you say,” said Kane.
“The only real success I've seen in dealing with this is when folks in treasury strongly engage those local teams or specific departments regularly — really work hard to win them over,” said Kane. The same may be true of AP or AR teams if there is “daylight between them and the finance organization,” he added.
3. Be Careful With Sales Numbers
Sales opportunities are at multiple stages at a given time — from qualification at the top of the sales funnel to the close at the bottom of the sales funnel. Sales have to share their forecasts weekly, biweekly, or monthly. “This is often what we call the ‘sunshine report,’” he said.
But reality “chips away” at sales’ revenue projections over time. (“I’ve never seen these sales projections go up,” said Kane.) That can have a “serious” trickle-down impact on the receivables forecast.
The point is “aspirational projections,” Kane said, can turn a long-term forecast “into a fairy tale.”
4. Categorize the Flows
Breaking out cash flows into effective categories is key to assessing the cash forecast, said Kane. If the cash flows are correctly categorized, an organization has “a better chance of tracking things like receivables to target, basically how it’s progressing with its goals on either a daily or maybe a weekly basis,” said Kane.
The level of detail depends on the organization. Still, it’s much easier to “roll up categories and the associated transactions to higher level categories for senior management reporting” and have more granular categorizations for treasury.
Categorization can also help with standardization as a company grows into a global concern and aspires to roll up competitors in its space, Kane said. Standardizing the categories and things like the frequency of updates and the time horizons for the cash forecast can help eliminate regional disparities that tend to arise.
5. Know What’s Under the Hood
Kane has seen many companies flirt with advanced regression algorithms and other sophisticated data science methods to analyze cash flow data and try to capitalize on developments in machine learning and artificial intelligence (AI).
“There’s nothing wrong with AI, but in the meantime, my advice would be don’t skip the basics."
John Kane, TIS
However, very few outside data scientists and AI programmers understand these tools at the requisite depth. Asked Kane: “Do you understand the parts of the algorithms and how they interact with each other? … Even if you have a good handle on the algorithms, do you know Python and Java well enough to understand how the formula is incorporated into the function or the code itself?”
Without that understanding, “you’re not going to trust the results” of the cash flow forecast, Kane said. “It’s a crystal ball.”
“There’s nothing wrong with AI, but in the meantime, my advice would be don't skip the basics,” Kane said — collecting the data, normalizing the data, standardizing how you talk about it. “And then use your own experience and judgment on what you think your company should do next,” he said.
“With forecasting, it's easy to focus too much on the data, the systems, the ideal processes, but don't lose sight of the people,” Kane said. “Even with sophisticated tools and processes ... you need the people as much as the data,” he said.