Liquidity planning is a cornerstone of a robust corporate treasury function. Its purpose is to ensure the company’s solvency at all times, identify risks or financial bottlenecks early and make optimal use of surplus liquidity. Especially in international corporate structures, liquidity flows must be coordinated and consolidated from numerous sources and regions, across various systems and time zones.1 Structured liquidity planning is therefore essential for any treasury department. It not only provides operational security but also lays the foundation for flexible strategic management and efficient use of funds.
In the dynamic and often unpredictable world of financial management, treasury departments face a wide range of challenges that go far beyond technical issues, making liquidity planning a complex and error-prone process.
Business Requirements
A central question is how predictable the company’s business model is. Companies with stable, recurring revenues typically have reliable cash flow patterns, while project-driven, seasonal or highly volatile business models naturally exhibit greater fluctuations. The nature of the business model thus largely determines the quality and reliability of liquidity planning.
Equally important is the availability and quality of historical time series data. Treasury departments need sufficiently long and consistent datasets to distinguish between trends, seasonality and one-off effects. In many organizations, such time series are only available in fragments or are maintained using different logics, making comparisons difficult.
For precise liquidity management, a clear understanding of the main cash flow drivers is also required. On the operational side, these include receivables and payables terms, inventory movements, seasonal sales fluctuations, commodity price dependencies, tax payments or capex cycles – factors that influence each other and can significantly shift working capital. Just as important are the financing and structural drivers, meaning all payment streams related to the company’s capital structure. These include interest and repayment schedules for existing financing, the availability and utilization of credit lines, changes in liquidity reserves as well as special factors such as dividends, bond maturities or margin calls from hedging transactions. This information is often less structured than operational data but is essential for AI models to create a complete and realistic picture of future liquidity.
Data Integration and Consolidation
Companies often work with a variety of ERP systems, booking logics and data formats, ranging from local accounting systems to central finance platforms and specialized solutions for individual business units. This heterogeneity makes it much harder to consolidate payment flows. Even minor differences in booking logic or currency conventions can prevent direct data comparability. Without a unified data structure and automated interfaces, data integration remains a complex and time-consuming process that threatens the transparency and accuracy of financial reporting.
Forecasting Uncertainties
In addition to data integration, forecasting is one of the greatest challenges in treasury. Fluctuating revenues, seasonal differences, project-dependent payment schedules and late customer payments often result in inaccurate planning figures. These uncertainties make it even more difficult to reliably predict both short- and long-term liquidity needs.2
For the field, this means not only measuring uncertainties but also understanding the cause-and-effect relationships of relevant influencing factors. For example, a high volume of new orders may initially lead to higher material costs and thus a short-term cash outflow, while the corresponding payments are received later. If such interactions are not considered, treasury departments risk suboptimal actions – such as unnecessary loans or inefficient capital allocation.
The quality of historical data directly impacts the accuracy of forecasts. If time series are fragmented, incomplete or inconsistent, it becomes harder to analyze trends, seasonality or special effects. In addition to understanding the economic relationships behind cash flows, transparency regarding the structure and significance of the available data is needed. AP/AR booking data reliably reflects the actual status quo but contains no information about upcoming invoices, progress of services or expected deviations. Controlling data such as forecasts, budgets or order intake add a forward-looking perspective – provided they are clearly defined, consistently structured and can be cleanly linked to operational booking data. The better these data worlds are integrated, the more robust the forecasts and the earlier potential liquidity bottlenecks can be identified.
A recurring challenge in integrating the controlling and treasury perspectives is that treasury forecasts are based on entities, value dates and transaction currencies, while controlling focuses on business units, periods and functional currencies. Harmonization requires a methodologically sound yet sometimes individualized approach.
Manual Processes in Treasury Departments
Many treasury departments still rely heavily on manual processes. This starts with collecting and reconciling payment flows from sales, procurement, production or controlling and extends to consolidating data for forecasts and reports. Different business logics and internal triggers must be manually combined, such as receivables terms, inventory movements or capex cash flows. Any deviation or delay can obscure the correlation between influencing factors, making it harder to interpret cash flow drivers.
Manual analysis of causal relationships between different items is particularly challenging. For example, a high order intake may initially trigger increased material usage and thus a short-term cash outflow, while the corresponding payments are received later. Treasury teams must recognize and accurately map such interactions, which is extremely time-consuming and error-prone without automated processes. Manually consolidating data from various sources ties up significant resources and makes it difficult to keep liquidity planning flexible and precise.
Lack of Transparency
Visibility into global accounts and liquidity reserves is often incomplete, increasing the risk of bottlenecks or inefficient capital allocation. These gaps not only hinder short-term liquidity management but also make it harder to identify potential bottlenecks in time and allocate available funds efficiently. In day-to-day treasury practice, this means decisions are often made based on incomplete information – for example, when prioritizing payments, optimizing working capital or planning credit lines.
The increased use of AI-based forecasting models brings additional transparency challenges. While AI can efficiently analyze historical data for patterns, outliers or early changes in payment behavior, it is often unclear which factors actually drive the results. Best practices show that models are only reliably interpretable if the data has been clearly structured beforehand – for example, by entity, currency, category or maturity – and additional sources such as forecasts, contract data or production and inventory metrics are integrated. Equally important are explainable methods that show which variables significantly influence the forecast and how sensitive the results are to changes. Without a solid understanding of the model logic, relevant triggers and underlying assumptions, there is a risk of misinterpreting analysis results. Using unsuitable models or poorly prepared input data can lead to forecasts that seem plausible in the short term but encourage poor decisions in the long run.
Against this backdrop, it is essential for treasury not only to map financial flows within the company transparently but also to understand the functionality and limitations of the forecasting models used. Only then can insights from analyses be reliably integrated into decisions on liquidity planning, risk management and capital allocation. Artificial intelligence is not a universal solution but a tool whose value depends largely on the quality of the data used, the selection of appropriate models and the expertise applied in interpreting results.
External Shocks
Economic crises, interest rate changes and new regulations require rapid adaptation and increase uncertainty in financial management. For treasury, it is no longer enough to prepare only short-term cash flow forecasts – what matters is how sensitive the business model is to different scenarios: rising interest rates, currency fluctuations and new regulations directly impact financing costs, liquidity and international positions. These factors often interact, demanding flexible and forward-looking management to limit risks. Rising commodity prices, seasonal fluctuations or political risks also make it essential to analyze causes and effects on cash flow in detail.
Excel Dependency
In many treasury departments, Excel remains the primary tool for liquidity planning. While it initially impresses with its flexibility, limitations quickly become apparent: version control, traceability of assumptions, data consistency and mapping of complex causal relationships are only possible to a limited extent. Manual maintenance of Excel files also makes it difficult to adapt quickly to changes in the business model or to external influences such as seasonal sales fluctuations or increased market volatility.
Switching from Excel to integrated digital solutions offers treasury departments the opportunity to manage complex requirements efficiently. Automated interfaces enable consistent consolidation of data from various sources. Transparent documentation of assumptions improves the quality of cash flow driver analysis, while scenario analyses can be implemented more easily. This allows treasury professionals to apply their expertise more effectively, make informed decisions and respond more agilely to internal and external changes.
