Planning is “the mental anticipation of future actions by weighing up different courses of action and deciding on the most favourable path”, as Günter Wöhe once wrote in his standard reference work “Introduction to General Business Administration”. This excellent definition can readily be applied to finance and liquidity planning.
In addition to the primary objective of safeguarding the company’s liquidity, the principles guiding the “most favourable path” may include requirements aimed at minimising interest, costs and risks. Examples include avoiding negative interest and custody fees or limiting interest rate and foreign exchange risks to an acceptable level.
Complete and accurate underlying data is essential for optimal decision-making. Compiling this data alone often presents a major challenge for cash managers. While recurring expenses such as wages and salaries, insurance premiums, tax payments, material costs and energy costs can generally be forecast with a reasonable degree of accuracy, expected cash inflows and certain types of expenditure are far more difficult to plan precisely.
Naturally, the quality and possible time horizon of a forecast depend heavily on the company’s industry. Energy suppliers, cities and municipalities can generally plan their income and expenditure relatively well under normal circumstances. However, unexpected events such as natural disasters, shortages of raw materials or the current pandemic can change the situation so dramatically that a rigid plan would also lead to incorrect decisions in these sectors.
For this reason, it is important to incorporate the most up-to-date data available and to keep financial planning flexible. Wherever possible, automated data imports should be used to complete the planning data. Where cash flows are generally predictable, a rolling forecast can be projected into the future relatively easily using a top-down approach based on previous values that have been classified as accurate, or “actual data”. Only a limited number of individual cash flows would then need to be added manually.
A sufficiently extensive history of high-quality data can subsequently serve as the basis for sophisticated algorithms or self-learning neural networks used to optimise forecasting as part of predictive analytics. Such data can also be used to calculate key indicators relating to the reliability of future cash inflows, such as “Cash Flow at Risk”, and apply them as management tools. However, in addition to substantial computing power and sufficient storage capacity, these evaluations require highly reliable data series. In most companies, such data is either not available over a sufficiently long period or can no longer be used as a reliable forecasting basis because of the effects of COVID-19.
For system-supported liquidity planning, it is therefore far more important to be able to apply the effects of different factors, such as revenue figures, interest rate changes, exchange rate movements and commodity price fluctuations, to existing forecasts and to simulate different developments. This makes it possible to model the extremes under particularly adverse and particularly favourable conditions as “worst-case and best-case scenarios” and then gradually narrow them down towards the most probable outcome, or “most likely” scenario. To continuously improve forecast quality, plan-to-actual variance analyses are performed, the causes of deviations are identified and appropriate optimisation measures are implemented for future forecasts. Data from electronic bank statements, for example, is suitable for populating forecasts with actual data. This data can be imported automatically, reconciled and assigned to the relevant planning categories. By incorporating transactions that have demonstrably taken place, cash managers gain an effective control mechanism without any significant additional effort.
Liquidity planning is made particularly difficult by fluctuating income, which prevents reliable top-down forecasting. This is partly because revenue is influenced by a wide range of factors, including trends, weather conditions, competition, and national and international regulations. More recently, additional uncertainty has arisen from pandemic-related disruptions in international supply chains, requiring flexible planning on both the income and expenditure sides.
While exchange rate fluctuations can be incorporated relatively easily into currency-specific planning software for exposure hedging purposes, the exact amounts and value dates of incoming payments often cannot be included in cash positioning before they appear in the bank account. If the planning department receives this information only through the previous day’s electronic bank statement, it is already too late to respond optimally from a liquidity management perspective. Intraday advices provided electronically by banks as SWIFT MT942 messages or, under the new ISO 20022 XML-based standard, as camt.052 files at least bring cash management up to date within the current day. However, a more forward-looking approach is required for proactive action that leaves sufficient time to assess alternative courses of action.
For this purpose, colleagues who are closest to the paying counterparty and therefore have access to better information are usually involved in the planning process. If the company preparing the forecast operates through numerous sales entities, potentially across the globe, bottom-up planning often makes sense. However, achieving a decision-making basis that is as complete and accurate as possible requires the willingness and discipline of everyone involved to maintain the planning data.
Treasury Management Systems such as Trinity therefore aim to make planning as straightforward as possible for all users. Data entry through the web-based tool resembles the familiar spreadsheet environment, while the automatically generated audit trail provides audit compliance and improved research capabilities. The previously cumbersome exchange of information by email and the associated transmission errors are eliminated. Instead, planning data is stored directly in the same database and is immediately available to other users for analysis. Without requiring duplicate data entry, the database also contains all cash flows arising from financial transactions managed in Trinity and concluded with external or internal counterparties, supplemented by credit facilities and converted into the group currency where required. The upload of planning and actual data can be automated both centrally and locally. Before the plans are consolidated, all data providers can confirm completion of their work, thereby avoiding process disruptions when the individual plans are combined. Naturally, such a planning system can also be used to record internal receivables and payables, for example as a preliminary stage for multilateral netting or for intercompany requests relating to financing, guarantees or foreign exchange hedging.
When everyone participates, a balanced combination of top-down requirements and bottom-up data contributions provides the company with a consistently strong and reliable basis for decision-making several days in advance. The increased transparency and timeliness give financial management greater confidence in actively managing liquidity in a targeted manner. Well-structured planning often also has a positive impact on external shareholders and the company’s creditworthiness, potentially resulting in better terms that can also benefit the supporting subsidiaries.
Incidentally, even groups that consistently maintain sufficient financial reserves occasionally use direct financial planning to navigate periods of crisis more effectively, as seen in recent months. Companies that introduce planning too late may find that it only benefits the restructuring adviser or insolvency administrator, which is certainly not your objective, is it?