What is a forecast?

The most important points at a glance:

• Forecasting enables proactive, data-based management of financial development.
• It supplements classic budgets with continuously updated, realistic projections.
• Rolling forecasts help identify trends at an early stage and adjust decisions flexibly.
• A solid data basis, clear responsibilities, and regular review are crucial.
• Legal requirements from the Swiss Code of Obligations (OR), Swiss GAAP FER and the Data Protection Act (DSG) must be taken into account.
• Digital tools and structured processes create efficiency and transparency.

What is forecasting in controlling and why is it central to corporate management?

Forecasting is the forward-looking planning of future business developments based on current data. In controlling, it serves as a strategic management instrument to realistically assess liquidity, costs and income, and to make well-founded decisions.

Forecasts connect operational data with strategic goals. They show where a company is heading under current conditions – and make it possible to react in time.
In contrast to the budget, which is prepared once a year and rarely adjusted, a forecast is dynamic: it is continuously reviewed and adapted to new information.

This flexibility makes forecasting an important instrument for CFOs and fiduciary advisors. It shifts the focus from merely controlling past figures to actively managing the future. This allows liquidity bottlenecks, cost increases or market risks to be identified and addressed at an early stage.

“A forecast does not replace a budget, but it gives planning agility and a sense of reality”, says Leif Roth, an expert in accounting and controlling.

What advantages does forecasting offer over classic budget planning?

Forecasting creates responsiveness and management intelligence. Instead of explaining past deviations, developments are identified early and actively managed. As a result, forecasting also influences profit appropriation and distribution planning – especially when decisions are based on realistic future scenarios.

This forward-looking perspective improves planning quality, strengthens management and investor confidence in financial planning, and makes it easier to prioritize resources.

The focus is no longer on control but on the ability to act – a decisive advantage over classic budget planning.

How does a modern forecast process work in practice?

A professional forecast process follows a clearly defined sequence – from setting objectives through data collection and modelling to continuous review. Only if all steps are systematically interlinked will a projection emerge that provides decision-making certainty.

A modern forecast typically comprises five phases:

1. Definition of objectives

• Define which metrics are to be forecast (e.g. revenue, cash flow, payroll costs, taxes).
• Define the forecast period (usually 12 to 18 months).

2. Data collection and preparation

• Use of current actual data from ERP or accounting systems.
• Check for completeness, plausibility and consistency.
• Integration of additional drivers such as exchange rates, interest rates or energy prices.

3. Modelling and calculation

• Selection of appropriate methods (e.g. trend analysis, regression model or AI methods).
• Documentation of assumptions and parameters.
• Use of tools such as Abacus, Bexio, Sage or Power BI for calculation and visualization.

4. Validation and control

• Comparison with historical values (backtesting).
• Measurement of accuracy using KPIs such as MAPE (Mean Absolute Percentage Error) or MAE (Mean Absolute Error).
• Readjustment in the case of systematic deviations.

5. Communication and review

• Preparation in clear, audience-appropriate reports.
• Regular reviews with management and controlling.
• Clear responsibilities, e.g. via a RACI model (Responsible, Accountable, Consulted, Informed).

“A forecast is not a calculation experiment, but a management process. When data quality, responsibilities and communication are right, true management intelligence emerges.” -  Leif Roth

A monthly or quarterly rhythm (cadence) helps keep forecasts up to date and identify deviations early on. This turns forecasting into a living management system – not a one-off planning exercise.

How does data quality affect forecast accuracy?

The quality of a forecast depends directly on the quality of the underlying data. Incomplete, outdated or inconsistent information inevitably leads to inaccurate results – regardless of the method used. A frequently underestimated factor is the correct period allocation – in particular the treatment of accrued liabilities, which ensures that income and expenses are allocated to the correct period.

“Without a clean data basis, every forecast remains a blindfolded estimate,” says Leif Roth.

Common problems and suitable measures can be clearly summarized:

By combining automated systems and regular data reviews, forecast accuracy improves significantly.
In addition, trust is created – both among internal decision-makers and external stakeholders such as banks or auditors.

Which methods and KPIs ensure high forecast quality?

The choice of the right method determines whether a forecast is precise or random. It depends on the data situation, planning frequency and the objective of the forecast.

There are three central methodological approaches. Qualitative methods are based on experience and market knowledge – for example for new products or uncertain markets. Quantitative models use historical time series, regression analyses or moving averages to extrapolate trends mathematically. And data-driven methods using machine learning extend these approaches by automatically detecting patterns and improving adaptively.

Many companies combine these methods: a classic example is the combination of statistical sales models with sales team assessments. This creates a balance between objectivity and practical relevance.

An additional success factor is the choice of the appropriate forecast horizon:

• short-term forecasts (e.g. monthly) serve liquidity management,
• medium-term forecasts (quarter to year) support budget planning,
• long-term forecasts (> 1 year) are strategic early-warning instruments.

This staggering makes forecasting more robust and strengthens the management decision-making basis.

How do you measure forecast quality?

Forecasts should be reviewed regularly – not only at the results level, but also methodologically. Accuracy is reflected in how much projections deviate from the actual development and whether errors occur systematically.

Key steps for quality assurance:

1. Variance analysis: Comparison between planned and actual values, ideally per cost center or segment.
2. Backtesting: Historical forecasts are compared with the actual results of past periods.
3. Learning loop: Insights flow into the next model iteration to refine assumptions.
4. Monitoring over time: The development of forecast quality is measured itself – e.g. whether the error decreases over several months.

KPIs such as MAPE, MAE or Bias are helpful, but must be interpreted. A low MAPE does not automatically mean a good forecast if the data basis is weak or external factors are missing.
More important is the consistency of measurement over time – only this really allows you to assess whether a model is improving.

A reliable forecast emerges when controlling, IT and business units share responsibility. The process thus becomes a learning system that improves with every planning round.

How are rolling forecasts implemented and maintained?

A rolling forecast is an ongoing, periodically updated financial projection. Instead of planning only once a year, the forecast is regularly extended – for example monthly or quarterly – and supplemented by the next period. This creates a continuous, dynamic view of the future.

Rolling forecasts follow three basic principles:

• Continuity: Each new period replaces an old one – the time horizon remains constant.
• Timeliness: New information continuously flows in, such as changed market prices or personnel expenses.
• Management benefit: The focus shifts from control to active management – decisions are based on real-time data.

This form of planning supports proactive financial management: liquidity bottlenecks, tax deadlines or investment peaks become visible early and can be corrected in good time.

Table: Comparison – Static forecast vs. rolling forecast

Rolling forecasts are particularly suitable for volatile markets or companies with seasonal fluctuations. When properly implemented, they also serve as an early-warning system to identify risks such as over-indebtedness or capital loss at an early stage and take countermeasures. Implementation, however, requires clear responsibilities and automated data flows to ensure timeliness and efficiency.

How does a rolling forecast remain reliable in the long term?

To prevent a rolling forecast from becoming a mere number-spinning exercise, clear process discipline and governance are needed. Successful models are characterized by three features:

1. Fixed cadence: Monthly or quarterly updates with a fixed reporting date.
2. Data responsibility: Each department delivers defined data points – e.g. personnel planning, revenue, cost centers.
3. Transparent communication: Results are not only reported, but interpreted jointly.

Technical implementation usually takes place via ERP or BI systems with automated imports. Tools such as Abacus, Bexio or Power BI make it possible to continuously update and visually analyze forecast models.

A rolling forecast is therefore not just a tool but a process: it fosters learning ability and agility – key success factors of modern corporate management.

What role do liquidity, tax and scenario forecasts play?

Liquidity, tax and scenario forecasts are among the most effective instruments in financial controlling. They create transparency regarding cash flows, tax obligations and possible developments under different conditions.

Liquidity forecasts show whether there are sufficient funds available to cover ongoing obligations such as salaries, social security contributions or supplier invoices. They help identify bottlenecks early and plan financing needs in good time.

Tax forecasts calculate the expected burden from direct taxes and levies – such as VAT, AHV/ALV/BVG contributions or corporate taxes.
They enable targeted cash planning and prevent liquidity surprises when quarterly or annual settlements are due.

Scenario forecasts simulate different future paths:

• Best case: optimistic development with revenue growth or cost efficiency,
• Base case: realistic main assumption,
• Worst case: decline in income or rising expenses (e.g. due to energy prices, exchange rates or wages).

Combining these three forecast types creates a complete picture of financial resilience.

How can scenarios and liquidity forecasts be combined effectively?

A practical forecast integrates liquidity and scenario models in a single data flow. This allows management to see at a glance how much cash is available and how different market conditions would affect it.

An example:
A company forecasts its liquidity for the next 12 months. While the base case assumes stable revenue, the worst case shows how a 10% decline would affect cash holdings. The best case, on the other hand, illustrates the scope for investments.

This approach offers several advantages:

• Clarity about stress limits: How long will the liquidity buffer last if income declines?
• Transparency regarding tax outflows: When are VAT or withholding tax payments due, and how do they affect cash flow?
• Sound decision-making basis: Investments, salary increases or distributions can be planned based on robust data.

Regularly updating the scenarios creates an early-warning system that goes beyond pure planning – forecasting thus becomes an integral part of risk management.

How does forecasting remain legally compliant, efficient and digital?

Forecasting touches on key legal requirements, as financial projections are part of management’s duty of care. Legal compliance is achieved when processes are documented, data is traceable and data protection requirements are met. Efficiency and digitalization ensure that these obligations are fulfilled with minimal effort.

Legal and regulatory frameworks to consider in forecasting:

A legally compliant forecast is therefore not just a set of figures, but a traceable process: every assumption, calculation and change must be documented – ideally directly in the ERP or BI system.
This creates an audit trail that proves in internal or external audits how decisions were made.

What role does digitalization play in efficient forecasting?

Digitalization significantly reduces manual effort while increasing accuracy. Modern tools take over data collection, consolidation and calculation almost automatically.

Efficient forecasting systems are characterized by the following features:

• Automated data flows: ERP systems such as Abacus, Bexio or Sage deliver current figures directly to BI tools such as Power BI or Tableau.
• Standardized workflows: Recurring processes – e.g. monthly or quarterly updates – follow predefined checklists.
• Audit reliability: Changes to assumptions are logged, making every adjustment traceable.
• Data protection compliance: Access to sensitive information (e.g. salaries, personnel expenses) is regulated on a role-based basis.

The combination of automation and governance creates transparency, reduces error risks and strengthens the reliability of financial management.
This makes forecasting not only more efficient, but also future-proof – in line with Swiss compliance requirements.

How does Auditrium support the implementation and optimization of forecast processes?

Digital development today offers ideal conditions for integrating forecast processes into day-to-day operations efficiently, precisely and transparently. Auditrium helps companies to make targeted use of these opportunities – from automated data collection to robust decision support.

In practical implementation, the focus is on five key areas:

• Consistent data basis: With Bill Bucher, current accounting data is available automatically. This allows forecasts to be built on reliable figures.
• Structured workflows: Clear responsibilities, fixed planning rhythms and transparent communication ensure traceability and trust.
• Analysis and visualization: Via Analise Franci, forecast data is evaluated, trends are made visible and deviations are presented in an understandable way.
• System integration: Forecasting is part of existing financial and decision-making processes and is not treated as a separate instrument.
• Learning-based management: Every planning round provides insights for further developing models and assumptions.

This creates a forecast system that is based on real-time data, remains flexible and at the same time meets the legal and organizational requirements of your company. At Auditrium, we understand forecasting as a continuous process that gives you transparency and a reliable basis for well-founded decisions.