Cash & Liquidity Management

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Redesigning Pearson's Cash Flow Forecasting Using Artificial Intelligence A variety of organisational factors make cash flow forecasting challenging in any large multinational organisation. Pearson's new artificial intelligence based solution aims to provide a detailed cash flow forecast in a single dashboard.

Redesigning Pearson's Cash Flow Forecasting Using Artificial Intelligence

Redesigning Pearson’s Cash Flow Forecasting Using Artificial Intelligence  

By James Kelly, SVP, Group Treasurer, Pearson plc

 

To improve Pearson’s cash flow forecasting, the London based learning company is rolling out a new, artificial intelligence based solution. It interfaces with the company’s key finance and ordering systems to provide a detailed cash flow forecast in a single dashboard. Treasury is now leveraging the dashboard as the backbone for its iterative forecasting process, with an eye on constant improvement.


A variety of organisational factors make cash flow forecasting challenging in any large multinational organisation such as Pearson. 

Our process previously involved local teams preparing forecasts to feedback into group treasury on a monthly basis, which they obtained from a variety of sources. The local business partners involved already had a wide variety of other demands on their time and we felt that the process was more time consuming for them than it needed to be.

The implementation of an enterprise-wide ERP system provided a great opportunity to look again at how we could re-imagine the process – simplify and make it more efficient, to allow for more time to be spent on insightful analysis.


Defining the parameters

We began by producing a business case for a change. We set out the business benefit that we hoped to achieve and estimated the time and cost required.  

The starting point was to question the timeframe we should forecast using a bottom-up approach. We decided that a two-month rolling forecast would be sufficient to allow us to make informed decisions about the time periods we could invest cash or would need to borrow for. We aimed for a very high level of accuracy for the first month of the cash flow, since most customers and suppliers have payment terms of 30 days or more, with a little more tolerance for the second month where the underlying sales or purchases may also need to be forecast.

Once we had determined the forecasting timeframe, we carefully examined the drivers behind redesigning the cash flow forecast, as a way of determining the value it could add to the business.

The key question was how important the project was to our ability to maximise the value of treasury to the organisation. The group has an operating cost saving target of £300m to be achieved between 2017 and the end of 2019 and we were concerned that, although the return we expected to achieve on the project was good in percentage terms, it was too small to make a meaningful contribution to this target and may divert resources from other projects which would make a larger contribution to the target.

We felt there needed to be greater motivation than just the direct financial return for undertaking the project and we concluded that the project would provide a facilitator for almost all of our treasury processes including liquidity, foreign exchange, interest rate and counterparty risk management. 

In order to avoid diverting scarce resource from other projects, we considered whether the process could be reformed by using staff dedicated solely to cash forecasting, but concluded that this would be labour-intensive, time-consuming to scale, and expensive. We therefore started to look for technology solutions using partners that could deliver similar benefits, with minimal upheaval and a relatively light resource requirement. 

 

Fig. 1 - High Level Overview of Solution

Fig 1  High Level Overview of Solution

 

Finding the right fit

When evaluating solution providers, we were clear with our expectations. We wanted a solution that would:

  • Interface effortlessly with all of the company’s key systems, extract the required data and use it to forecast.
  • Require little help from the Pearson IT team to implement, since the IT department was busy with customer-facing projects and rolling out a new ERP globally.
  • Drive synergies between the knowledge of Pearson staff and the artificial intelligence (AI) in the tool.
  • Offer the ability to include multiple rules and behaviours to reflect the differing behaviour of different customers and customer groups.
  • Enable treasury to drill down into the forecast by operating company or process to help drive behavioural change.
  • Form part of an iterative process to continuously improve forecasting.

To meet our requirements it was important that the teams understood what we were trying to achieve, why and how. We have worked with leading providers in this space to develop a model that meets our criteria. Our solution provides a dynamic dashboard that brings together all the required information in a single window (see Figure 1) and uses a variety of data cleansing and analysis techniques to try to ensure the accuracy of our forecasts.

 

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