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Mastering the Challenges of Liquidity Management

How to use simulation tools to boost operational resilience

The coronavirus pandemic, the collapse of the supply chain, runaway inflation, the current energy crisis – these are stormy times for companies and, among other things, their survival depends on their ability to manage liquidity professionally. The previous article looked at the use of simulation tools in technical areas (see article starting on page 42), but simulation tools can also provide timely pointers on liquidity problems and help companies introduce appropriate countermeasures. Professor Dr. Alexander Baumeister, Steinbeis Entrepreneur at the Saarbrücken Institute for Controlling Innovations, a Steinbeis Innovation Center, and a professor of business administration at Saarland University, provides TRANSFER magazine with an overview of the advantages of such instruments.

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The safety net introduced by the last German government offered a package of measures aimed at safeguarding the ongoing viability of companies during the coronavirus pandemic by, among other things, helping with liquidity. The package (called a “protection shield” in German) included more flexible treatment of short-time allowances, help with the flow of tax payments, and easier access to credit. Another package of measures is currently being launched – a “defense shield” against exploding energy prices that promises companies help with liquidity.

These are tense times. In August 2022, the year-on-year rise in the selling prices of industrial products hit an all-time high of 45.8%. As a result, many companies are feeling the squeeze on their liquidity. Their procurement costs are exploding and they are unable to pass costs on to customers quickly enough in their selling prices. It is essential that firms plan their liquidity carefully and identify any bottlenecks in cash flows posing any potential threats to their very existence – and that they do this early enough to introduce countermeasures. Help is now available, including for small and medium-sized enterprises, in the form of simulated numbers. These were the subject of a project called InsoKURZ, which was funded by the Saarland State Chancellery and implemented by experts at Saarland University and the Saarbrücken Institute for Controlling Innovations. The project looked at the development of a simulation tool for use in insolvency forecasting.

Anticipating liquidity issues early helps bolster business resilience

Increasing operating cash flows usually go hand in hand with (opportunity) costs. This is because, to name just some examples, shortening the payment terms of customer liabilities can put a strain on turnover; offering discounts as an incentive to settle invoices more quickly automatically cuts revenues; and sale-and-lease-back arrangements, or selling receivables, result in corresponding leasing or factoring fees. Conversely, under Section 17 (18) of the German Insolvency Code, planned cash flows must be managed such that (as far as possible) there is no threat of (imminent) insolvency. It is therefore crucial to match the scheduled stock of payment instruments as carefully as possible to the patterns and timings of due payments. The earlier it can be anticipated when there will be potential shortfalls in operational liquidity, the more lead time a company has to take measures aimed at making necessary adjustments. Any help with the planning process will therefore have a direct impact on enhancing operational resilience.

Simulation tools help with liquidity planning

Future incoming and outgoing cash flows are always subject to risks. These may, for example, be the result of exchange rate fluctuations, changes in commodity prices, or changes in customer payment patterns. Planning liquidity according to the anticipated or most probable figures, a common approach in small and medium-sized enterprises for resource reasons, can be problematic because this generally ignores risk, thus depriving firms of information that is valuable for management control purposes. Although companies are required to assess impending insolvency under Section 18 (2) of the German Insolvency Code (InsO), and this entails checking whether liquidity reserves and surpluses arising from operations are mostly likely to cover existing payment obligations [1], future obligations and the probability of possible shortfalls should also be included in liquidity planning in order to be in a position to decide whether potentially positive outcomes should be abandoned in favor of securing liquidity. Simulation results can provide a useful foundation of information for doing this, by allowing different assumptions to be made regarding forecast operational cash flows. These can be recorded, as required, and can even be evaluated using common software. Such calculations provide users with a representation of liquidity over time underpinned by probabilities. By merging simulation results, judgments can be made regarding impending payment bottlenecks and necessary changes to planning.[2]

Using simulation to assess liquidity measures

Companies would only be advised to forego profit contributions for higher liquidity reserves if this can be offset by a corresponding lower probability of experiencing liquidity bottlenecks. Similar to any form of insurance, decision-makers must contrast the price of hedging with risk reduction in order to judge measures according to their willingness to take risks. To do this, however, they will need detailed probability profiles of adjustment alternatives, which even small and medium-sized enterprises can generate with simulation models, for example by using an Excel spreadsheet.[3]

Classic liquidity plans or financial plans can be used as a basis for designing simulation models, and these should be available at the company anyway. If – as is often the case in small and medium-sized enterprises – there are no sophisticated risk management processes in place, particular attention should be given to carefully identifying relevant risk factors. Sometimes this will involve compromises regarding how realistic modeling can be made and the resources a firm can afford to invest in calculations. Significant improvements can already be made to liquidity planning based purely on deterministic methods if, for example, instead of basing forecasts specifically on procured materials, at least one aggregated forecast is drafted for individual groups of materials or total material expenditures. When setting up the simulation model, it should be ensured that there is sufficient flexibility to capture adjustments – that need to be checked – in liquidity developments, such as taking advantage of payment deadlines by accepting a lost discount. Even standard software such as Excel allows to compare values needed to simulate the results of different adjustments, and these can be used to select the ones that are suited to the risk propensity of decision-makers.

The figure above shows an example of simulated liquidity over time for an SME, showing a one-year horizon broken down by month. Plotting net maximum and minimum monthly liquidity, as calculated by the simulation, produces diverging scenarios that, in the worst-case scenario, point to the risk of a shortfall starting in August. Since the expected value is positive, it depends on risk propensity whether anticipatory countermeasures should be taken.

This example shows the effect of making full use of payment deadlines at the cost of lost cash discounts. Positive changes to liquidity shortfalls, brought about by such adjustments, are offset by lower levels of anticipated liquidity. Decision-makers can only judge whether this makes sense by assessing the risk profiles generated by the simulation. The one-off expense of introducing simulation-based liquidity planning is thus offset by the many benefits enjoyed in the long term.


Univ.-Prof. Dr. Alexander Baumeister (author)
Steinbeis Entrepreneur
Steinbeis Innovation Center Saarbrücken Institute for Controlling Innovations (saar#cinnovaton) (Saarbrücken)

[1] A. Baumeister, F. Britz und T. Kochems: Drohende Zahlungsunfähigkeit im Insolvenzreife-Monitoring. In: Controlling – Zeitschrift für erfolgsorientierte Unternehmenssteuerung (32), Heft 6/2020, S. 44-47
[2] A. Baumeister, F. Britz und T. Kochems: Gestaltungsfragen der simulationsgestützten Abschätzung drohender Zahlungsunfähigkeit nach § 18 InsO. In: Der Betrieb (74) 2021, S. 1349-1354
[3] A. Baumeister, T. Kochems und F. Britz: Resilienz in der Krise – Fallbeispiel zum Einsatz von Simulationstechnik in der Insolvenzprognose. In: Der Betrieb (74) 2021, S. 1417-1421