An intelligent approach to analysing its financial data has enabled educational publishing and assessment service provider to optimise its working capital.

James Kelly, SVP Treasury, Risk Management and Insurance at Pearson is frank in his assessment of the options available for analysing cash flow when he joined the company five years ago, acknowledging that they were insufficiently robust to provide detailed guidance on cash flow drivers and challenges.

This situation was exacerbated by the fact that the company had around 30 separate businesses running their own treasury centres and cash management operations with very little information being fed into the central treasury team.

Unsurprisingly, he recalls that making sense of working capital requirements when cash was spread across approximately 600 accounts in 60 different currencies was far from easy – which is why his initial focus was on simplifying the process and centralising forecasting and analysis without completely eradicating decentralised decision making.

“One of the early objectives was to stop asking the operating companies to produce their own forecasts and automate as much of the activity as possible so we could produce forecasts from the centre that would act as a base line,” he says.

This enabled the treasury team to produce medium-term forecasts with much greater visibility of working capital, allowing for better FX risk management.

When the pandemic struck, the artificial intelligence models used for payroll and accounts payable were operating pretty smoothly and this remained the case as governments across the world started to impose restrictions to limit the spread of the virus.

“Where it got more challenging was on the receipt side where we suddenly had much larger volumes of refunds,” says Kelly. “We moved to considering a range of possible outcomes and used Cashforce to help us identify quickly which of those outcomes looked the most likely.”

Pearson now has complete consistency across the group, which means it doesn’t need to consider whether a specific part of the business is very conservative (or not) when it comes to revenue forecasts.

“This has allowed us to right size our balance sheets so we don’t end up raising more cash than we need to,” adds Kelly. “We were able to see how things changed as a result of Covid, what the initial impacts of lockdowns were and the points where things started to turn around and our cash flow started to normalise.”

Pearson uses Oracle to tie together the various strands of the business. So if the group tells Kelly that operating cash flow was ‘x’ million in a specific month, he can tie that back to the total value of purchase invoices that were approved during the period and what was paid out.

“We saw a big reduction in the working capital we generated by getting much closer to where the pinch points were in our business and identifying where we had specific issues with customers and working with teams to improve those,” he says.

When asked how he built the business case, Kelly acknowledges that basing it on the ability to reduce interest payments is a hard sell in a low rate environment and that it is not the type of project that any company could realistically expect to implement in a matter of hours – even if it already had significant cash flow visibility.

“The spend we are looking at here isn’t enormous but it is significant – the equivalent of one or maybe two team members,” he concludes. “What it allows, however, is much more granular understanding of what is going on through a large and complex data set that two people working exclusively on data mining would not be able to analyse to a comparable standard.”