How Technology Can Optimize Working Capital To Free Up More Cash

Henner Schliebs

Part 1 in a three-part series. Read Part 2 and Part 3.

According to PricewaterhouseCoopers, CFOs have an opportunity to recover cash that adds up to a whopping €1.2 trillion. CFOs know the money is there – but may not have the time and resources to go after it.

Fortunately, technology is making it easier to rectify the situation. AI and machine learning can automate parts of working capital management, to help identify opportunities, streamline processes, and make data-driven business decisions.

We asked finance experts Dr. Aswath Damodaran, professor for the NYU School of Business, and Christopher Argent, BI and Analytics program lead at Vodafone, to share how they see technology transforming working capital optimization.

Expanding your data world

Real-time data solutions and financial analytics allow for greater insight into the current state of your working capital. The wealth of data available extends beyond the office of finance; there is valuable, relevant data to be had from functions across the organization.

“You have this world of internal data that you can use for performance monitoring,” Argent says. “You’re not just focusing on financial data, you’re looking at HR, work resource planning, retention rights. You’re looking at trying to drive down recruitment costs by keeping people. And then there’s behavior analytics that you bring in for your HR teams to support an overall initiative to keep HR costs down. That’s the sort of the out-of-the-box thinking that we need in finance now.”

Of course, Argent’s HR example is just one of many potential opportunities. By examining organizational data outside of the finance department, CFOs can find new efficiencies and areas for improving business performance across the organization.

Boosting data relevancy

It’s not just the quantity of data that matters, however. AI offers the potential to surface the most relevant data to help streamline processes and identify opportunities.

“We need a way of taking that data that’s out there and crystallizing it in a way that decision-making is made easier. We need better ways of collecting the data and presenting the data so that people can make better decisions,” Damodaran says. “If I were a CFO, I’m not asking people to send me a hundred reports. I’m saying take those reports, crystallize them into a set of numbers that I can use to make big decisions because that’s what I need to do on a day-to-day basis.”

Automated reporting and analytics can collect data, break it down by region and line of business, and surface insights that CFOs can act on.

Improving data quality

We like to think of financial data as neutral and free of bias. But human interaction with data always involves some degree of interpretation, which can introduce error. Part of making better, more strategic working capital decisions is knowing when the data could be clouded by bias or human error.

For example, when you know your historical and real-time error rate for invoices, how many invoices involve exception processing, or when each invoice has been approved and paid, you can make smarter decisions about how to improve the invoicing process.

Damodaran believes artificial intelligence can help in identifying (and eventually correcting for) data bias, for improved insights.

“Artificial intelligence’s job will be to gauge the bias at each stage in the process,” Damodaran told us. “Because I can tell when there’s a bias in the analysis, I know what numbers to check. AI might not fix the bias but at least you’ll be aware of where the bias is and how much it is.”

Making forecasting faster and more efficient

Forecasting and budgeting are ideal candidates for machine learning optimization. Machines can process data, identify trends, and make predictions more efficiently and accurately than humans can.

At Vodafone, Argent has seen the advantage firsthand. “One area that we’ve worked on that’s really successful is in replacing the budget and possible rolling forecasts with machine learning forecasts or predictions,” He says. “We are in a position now where the machine learning forecast is more accurate than the manual forecasts.”

With increased accuracy and reduced manual work, more working capital becomes available for more productive expenses like acquisitions, new hires, and more.

Welcome to intelligent finance

It’s easy to see how technology changes the equation. Advanced technology and financial management systems are making it easier to increase insight into your current cash flow, streamline manual processes, and improve team efficiency. To learn more, visit The CFO’s Guide to the New Era of Intelligent Finance.

Why have AI, machine learning, predictive insights, and digital assistants become the must-have new tools of forward-thinking CFOs to drive business performance? Watch the webcast Tuesday, Nov. 6. 

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About Henner Schliebs

Henner Schliebs is global vice president Audience Marketing for SAP S/4HANA and Finance at SAP. He is a progressive sales/marketing executive with 15+ years of experience in business software solutions focused on corporate functions. He has strong marketing and go-to-market skills and a proven track record in enterprise software solutions, along with significant experience in solution management and customer engagement.