What is forecasting?
Forecasting is the systematic prediction of future developments based on historical data, statistical methods, and qualitative assessments. Its aim is to give companies greater planning certainty, identify opportunities and risks at an early stage, and enable well-founded decisions. Bissantz enables AI-supported forecasts for all business-relevant KPIs, allowing you to simulate important decisions in advance.
| Feature | Details |
| Category | Controlling / financial planning / business intelligence |
| Application | Systematic prediction of future developments to support planning and decision-making |
| Typical areas of application | Financial planning, sales planning, production planning, workforce planning, liquidity management |
| Related terms | Predictive analytics, rolling forecast, budgeting, integrated planning, KPI, scenario planning |
| Benefits | Planning certainty, early risk detection, flexible corporate management, better resource allocation |
At a glance
Forecasts help identify opportunities and risks at an early stage and derive appropriate measures.
Rolling forecasts continuously update predictions and regularly extend the planning horizon.
Used in financial planning, sales, production, workforce, and resource planning.
Forecast meaning
The term forecast describes the systematic prediction of future developments based on historical data and statistical analyses. In a business context, forecasting is used to estimate trends and KPIs such as revenue, costs, cash flow, or demand in order to make well-founded statements about likely future scenarios as part of short-term planning.
In controlling and financial management, forecasting is a key management tool: it helps companies identify opportunities and risks at an early stage, allocate budgets effectively, and adapt corporate planning flexibly. Comparing actual and target values makes it possible to identify variances and initiate corrective measures.
Modern forecasting methods increasingly use artificial intelligence (AI) and machine learning to identify patterns in large datasets and make forecasts more precise and dynamic. Forecasts nevertheless remain estimates whose reliability depends on data quality, the models used, and the time horizon under consideration.
What are common forecasting methods?
Companies use various forecasting methods to predict future developments as accurately as possible. A basic distinction is made between quantitative and qualitative methods. In practice, the two approaches are often combined to produce robust and realistic forecasts.
Quantitative forecasting methods
Quantitative methods rely on historical data and mathematical models to calculate forecasts. They are particularly suitable when extensive and reliable datasets are available.
Time series analysis (time series forecasting): Analyzes data ordered chronologically to identify trends, cycles, and seasonal fluctuations. It is ideal for forecasting recurring patterns, such as those found in revenue or sales data.
Regression analysis: Examines the relationship between a dependent variable (e.g. revenue) and independent variables (e.g. marketing expenditure or price development). This makes it possible to model cause-and-effect relationships.
Machine learning: AI-supported models and neural networks identify complex patterns in large datasets. They can continuously improve forecasts by automatically adapting to new information.
Monte Carlo simulation: Performs a large number of random calculations to simulate possible future scenarios. This method helps quantify uncertainty and assess risks in a targeted manner.
Qualitative forecasting methods
Qualitative methods are based on experience, market observations, and expert assessments. They are primarily used when historical data is unavailable or when the future depends on factors that are difficult to quantify.
Expert surveys: Systematically collect assessments from experienced specialists to estimate trends and developments—particularly useful for new products or markets.
Scenario planning: Develops several future scenarios (e.g. best case, worst case, trend scenario) to assess potential impacts on corporate strategy or market position.
Delphi method: A multi-stage survey process in which experts anonymously submit their forecasts. After each round, the results are consolidated until a common consensus is reached.
What is a rolling forecast?
A rolling forecast is a dynamic form of forecasting in which forecasts are updated regularly rather than being prepared only when needed. The period under consideration is continuously shifted forward—for example, the next twelve months are forecast each month or quarter. This enables companies to respond more quickly to changes in the market, customer behavior, or supply chain. Rolling forecasts improve planning certainty, support flexible budget management, and are particularly important in controlling and business intelligence in dynamic markets.
| Term | Description | Feature |
| Rolling forecast | Continuously updated forecast with a rolling horizon | Dynamic, ongoing |
| Year-end forecast | One-time or infrequently updated estimate of the expected annual result | Static, periodic |
| Projection | Projection of actual data available up to a certain point to the year-end value | Simple, less dynamic |
A year-end forecast, by contrast, is a one-time or infrequently updated estimate of the expected annual result, usually prepared during the financial year to estimate the expected year-end result. A projection, on the other hand, is generally based on actual data available up to a specific point in time and projects it linearly or using simple assumptions to the year-end value. It is therefore less dynamic and detailed than a rolling forecast.
Where do companies use forecasts?
Companies can use forecasts in numerous areas to make data-driven decisions and minimize risks. Typical areas of application include:
Financial planning and controlling: Forecasts support sound budgeting, liquidity planning, and the management of costs and investments. Targeted cash flow forecasting, for example, enables companies to plan their cash flows accurately, identify liquidity bottlenecks at an early stage, and safeguard financial stability.
Revenue and sales planning: Sales forecasting and revenue forecasting help companies monitor sales developments and plan capacities efficiently.
Demand and production planning: Demand forecasting supports the management of inventory levels, resources, and production capacities.
Workforce and resource planning: Companies can optimally manage workforce requirements and material usage based on expected developments.
What is the difference between forecasting and predictive analytics?
Forecasting and predictive analytics both aim to predict future developments based on data, but they differ in methodology and focus.
| Feature | Forecasting | Predictive analytics |
| Goal | Predict specific KPIs | Identify complex patterns and probabilities |
| Methodology | Historical data, statistical models | AI, machine learning, multivariate analyses |
| Output | Specific forecast (e.g. revenue, cash flow) | Probabilities, recommendations for action |
| Time horizon | Short to medium term | Short to long term |
| Typical application | Budget planning, liquidity management | Customer behavior, early risk detection, churn prediction |
In classical forecasting, historical data is analyzed to predict trends, patterns, and KPIs. Forecasting produces concrete predictions, for example for revenue, sales, cash flow, or budgets, and supports planning.
Predictive analytics goes beyond this by aiming to identify complex relationships and calculate probabilities for future events. In addition to pure forecasting, predictive analytics also provides recommendations for action and supports strategic decisions.
Example: Forecasting shows that employee turnover will increase in the coming quarter. Predictive analytics additionally identifies which employees are particularly at risk of leaving—for example, those who have not received a recent promotion or training opportunity. This enables the company not only to react but to take proactive action.
Practical example: forecasting with DeltaMaster
A retail company currently prepares its revenue forecast quarterly in Excel—based on manually compiled actual data and the experience of sales managers. The result: the forecast is already outdated when it is prepared, variances only become visible in the following quarter, and corrective measures come too late.
With DeltaMaster, the forecasting process is fundamentally modernized: current actual data flows automatically into the model, while AI-supported functions generate rolling forecast values based on historical patterns and current developments. Deviations from the expected trajectory are flagged immediately, with automatically generated commentary and drill-down capabilities down to product group and regional level.
Bissantz and forecasting
With DeltaMaster from Bissantz, forecasting can be established as a continuous process, closely integrated with planning, simulation, and variance analysis on a shared data foundation. AI-supported functions help automatically generate forecast values, identify patterns in historical data, and flag deviations from the expected trajectory at an early stage. As part of its consulting services, Bissantz supports companies in the technical and functional implementation of modern forecasting solutions—from methodology and data modeling to productive use in controlling.
FAQ – frequently asked questions
Forecasting means taking a structured look into the future—based on data, not gut feeling. Think of it as a weather forecast for a company: it can be wrong, but it is better than being unprepared. A good forecast makes uncertainty manageable.
A budget is a predefined target—what the company wants to achieve. A forecast is a prediction—what the company is likely to achieve. Both are necessary: the budget sets the direction, while the forecast shows whether the company is on track.
The basis is generally historical data such as revenue, costs, sales volumes, external market data, and current trends and developments. Data quality is critical: incorrect or incomplete data leads to incorrect forecasts. Expert assessments and qualitative factors can also be incorporated.
Forecasting provides the most likely trajectory based on the available data. Scenario planning complements this with alternative scenarios—what happens if key assumptions do not materialize? Together, the two methods give companies a comprehensive picture of opportunities and risks.
DeltaMaster automatically integrates actual data, generates AI-supported forecast values based on historical patterns and current developments, and immediately flags deviations. Bissantz also supports companies with the functional design of forecasting processes, including methodology selection, data modeling, and integration of rolling forecasts into the planning process.
Summary
Forecasting is the navigation instrument of modern corporate management: it makes uncertainty manageable, risks visible, and decisions more informed. The methodological spectrum ranges from simple time series analysis to AI-supported rolling forecasts. The key is that forecasts are up to date, data-driven, and integrated into the overall planning process. With DeltaMaster and its consulting expertise, Bissantz makes forecasting a continuous, automated part of controlling—for companies that want to manage proactively rather than merely react.
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