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Enhancing financial precision: A deep dive into cash forecasting techniques

 

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The art and science of cash forecasting serves as the linchpin of corporate financial stability, enabling organisations to navigate the unpredictable waters of global commerce with greater assurance.

The 2024 EuroFinance Global Treasury Americas West Coast conference, brought together treasury professionals from the travel technology company, Expedia, enterprise software provider, Cloud Software Group, and the IT behemoth, HP, to share deep insights into the complexities of cash forecasting, offering a look at the evolution of practices, the impact of technology, and the collaborative efforts necessary to refine this essential financial function.

The strategic imperative of cash forecasting 

With over 25 years in the field of treasury management in many organisations, Jim Scurlock, senior director and assistant treasurer at Expedia, has witnessed first-hand the universal importance of forecasting. He emphasised that regardless of the industry or company size, cash forecasting remains a perennial top priority for treasury operations. He mentioned that the ongoing accuracy of these forecasts is crucial not just for routine financial operations but also for strategic planning and risk management.

At Expedia, where cash flows are highly seasonal due to the nature of the travel industry, accurate cash forecasting is indispensable. Scurlock elaborated on the intricacies of managing liquidity amidst these seasonal fluctuations, highlighting that Expedia’s forecasts often achieve variances as low as one or two percent for projections extending from one month up to two months. This precision is achieved through meticulous collaboration with various departments such as credit and collections, tax, and payroll. Regular one-on-one sessions with these teams ensure that the treasury department has a comprehensive understanding of the financial landscape, allowing for proactive adjustments to forecasts.

Moreover, Scurlock underscored the importance of data centralisation and cleanliness. Expedia is actively developing a centralised data lake to consolidate financial data from multiple sources, ensuring a single source of truth for cash forecasting. This initiative is coupled with the implementation of advanced technologies like machine learning and AI to refine the forecasting process further. Scurlock’s previous experience at the tech giant, Microsoft demonstrated the potential of AI to predict foreign currency collections with high accuracy. 

However, Scurlock acknowledged the ongoing challenge of forecasting cash outflows due to transaction complexity. Specifically, while inflows such as customer payments could be tracked with accuracy, outflows presented difficulties. These complexities arise because outflows can include a mix of accounts payable, capital expenditures, tax payments, and other expenses that are often lumped together in bank statements. Additionally, the timing of these transactions can vary significantly, making it harder to predict exact outflows. This variability necessitates a nuanced approach to forecasting, where constant adjustments and close monitoring are crucial to maintaining accuracy.

Transforming cash management in private equity-owned companies

Private equity ownership, characterised by complex debt structures and stringent reporting requirements, necessitates a more sophisticated approach to cash forecasting. 

Bruce Edlund, group director and assistant treasurer at the Cloud Software Group shared insights from his early days at Citrix through its evolution into the Cloud Software Group. Initially, Citrix, an enterprise software company with high margins and robust cash flows, had minimal focus on detailed cash forecasting. However, as the company transitioned to private ownership, the necessity for precise cash management became paramount. 

Edlund recounted the dramatic shift from a relatively relaxed approach to cash forecasting to one where day-to-day liquidity management became critical. He detailed the implementation of basic but effective forecasting tools that tracked weekly collections, accounts payable, payroll, and interest payments.

The need for detailed cash visibility grew, prompting the adoption of more advanced systems. These tools enabled the automation of cash flow actuals, providing real-time insights into collections and expenditures. Edlund emphasised that in the private equity context, where debt servicing is critical, the ability to accurately predict cash flows directly impacts the company’s strategic decisions, such as acquisitions, interest payments and debt repayments. His insights underscore the significant evolution in cash management practices required to navigate the complexities of private equity ownership.

Navigating negative working capital with advanced forecasting techniques

Andrea Noseda, global treasurer at HP brought another dimension to the discussion. With extensive experience across multiple global markets, Noseda has tackled the unique challenges at HP, a company with significant scale and a business model that features negative working capital. Negative working capital is when a business’s current liabilities exceed its current income and assets and thus, this condition necessitates extremely accurate daily cash forecasts to manage the substantial liquidity swings that characterise HP’s operations.

Noseda detailed the dual methodology HP employs for cash forecasting: indirect forecasting, closely linked to the company’s P&L and managed by the financial planning and analysis group, and direct forecasting, handled by the treasury department. The indirect method is preferred during the first two months of the quarter due to its alignment with broader financial planning. However, as the quarter progresses, the direct method takes precedence, allowing for precise tracking of outstanding invoices, payroll, taxes, and other movements. Noseda highlighted the integration of machine learning and AI in enhancing forecast accuracy, although she noted that the involvement of key business partners such as credit and collections, payables, and payroll teams remains essential.

Collaborative dynamics 

Noseda stressed the importance of constant communication and alignment with these business partners to mitigate forecast variances. By fostering close communication and cooperation, the treasury team can gather real-time insights and data from these departments, ensuring that cash forecasts reflect the most current and accurate financial picture. This alignment allows the treasury to anticipate and respond to changes in the business environment promptly, thereby facilitating more accurate financial planning.

It also helps in identifying potential discrepancies early and making necessary adjustments before they impact the overall financial health of the company. For instance, regular meetings and data sharing between treasury and credit and collections teams can provide early warnings for delayed payments or unexpected changes in receivables, allowing the treasury to adjust their forecasts accordingly. Similarly, close coordination with the payroll department ensures that large outflows, such as payroll disbursements, are accurately timed and factored into the cash flow projections.

Meanwhile, the future outlook for cash forecasting, as discussed by the panel, involves leveraging advanced technologies like AI and machine learning. These technologies can further enhance the accuracy and efficiency of cash forecasting by analysing vast amounts of data and identifying patterns that might not be immediately apparent to human analysts. The integration of these technologies requires a foundation of clean, centralised data, which can only be achieved through robust inter-departmental collaboration.

Overall, the panellists agreed that the future of cash forecasting will continue to rely heavily on collaborative dynamics within the organisation. By maintaining strong lines of communication and cooperation across various departments, companies can enhance their forecasting accuracy, better manage liquidity, and support strategic decision-making processes.