In an ever-changing global economy, the ability to predict future developments is crucial. Economic forecasts, often simply referred to as forecasts, play a central role in planning and decision-making in companies and public institutions. They help to reduce uncertainties and make well-founded strategic decisions.
A forecast, or prognosis, is a systematic estimate of future events or conditions based on the analysis of historical data, current trends and other relevant information. In a business context, a forecast often refers to the prediction of variables such as sales, demand, costs, market trends and financial results.
Forecast planning involves the collection and evaluation of past data, the application of statistical and mathematical models and the consideration of expert opinions and external factors. The aim of a forecast is to reduce uncertainty and support decision-making processes by providing the most accurate assessment of future developments.
Effective forecast planning enables companies to prepare for future challenges, identify opportunities and take timely action to secure competitive advantages and ensure long-term success.
The main objective of forecasting is to make well-founded and reliable predictions about future developments and events. Here are the specific objectives in detail:
| Criterion | Forecast | Budget |
| Definition | Forecast of future developments | Planned financial framework for a specific period |
| Time horizon | Short to medium term (often quarterly) | Medium to long term (usually annual) |
| Data basis | Historical data, current trends, external factors | Strategic goals, plans, internal targets |
| Purpose | Support for decision-making and planning | Determination of financial targets and control |
| Flexibility | Dynamic, regularly updated | Static, infrequent adjustments |
| Use | Adaptation to market changes and current conditions | Benchmarking, performance monitoring |
| Accuracy | Estimates based on available information | Fixed figures based on strategic planning |
| Example | Sales forecast for the next quarter | Planned annual sales |
| Frequency of preparation | Regular, often monthly or quarterly | Once a year, occasionally half-yearly budgets |
Creating a forecast is a structured process that involves several steps to make well-founded predictions about future developments. Here is a step-by-step guide to creating a forecast:
By carefully applying these steps, you can create well-founded and reliable forecasts that serve as a valuable basis for strategic decisions.
| Term | Meaning |
| Forecast | Prediction of future events or developments based on historical data. |
| Time series analysis | Analysis of data points collected over regular time intervals. |
| Trend | General direction in which a variable moves over time. |
| Seasonal effects | Recurring patterns or fluctuations in a time series that occur regularly within a year. |
| Causal model | Model that uses relationships between variables to make predictions. |
| Qualitative methods | Forecasting methods based on expert opinions and subjective assessments. |
| Quantitative methods | Forecasting methods based on statistical and mathematical models. |
| Exponential smoothing | Method of forecasting that gives more weight to more recent data points. |
| ARIMA | AutoRegressive Integrated Moving Average, a complex statistical model for time series analysis. |
| Regression analysis | Method for investigating the relationship between a dependent variable and one or more independent variables. |
| Delphi method | Structured communication method in which experts are interviewed iteratively. |
| Bias | Average deviation of the predictions from the actual values, shows the direction of the error. |
| Tracking signal | Measure for recognizing systematic errors in a forecast. |
| Theil's U statistic | Comparison of forecast accuracy with a naive method (no change). |
| Scenario analysis | Analysis that considers various possible future events and their effects. |
| Capacity planning | Planning production capacity based on forecasted demand. |
| Financial forecasting | Prediction of a company's future key financial figures. |
| Demand forecasting | Forecasting future demand for products or services. |
| Economic Forecast | Prediction of economic indicators such as GDP, inflation or unemployment. |
| Technology forecast | Prediction of the development and introduction of new technologies. |
| Life cycle analysis | Evaluation of the entire life cycle of a product or technology. |
Various key figures are important in Forecast Controlling in order to assess the accuracy and reliability of forecasts and to control the planning process. Here are some key figures that are used in Forecast Controlling:
This key figure measures the difference between the forecast and actual values. It shows how accurate the forecasts are and helps to assess the reliability of the forecasting process.
This is the period of time between the creation of the forecast and the time at which the forecast events occur. A shorter lead time enables faster adjustments to market changes and improves the company's ability to react.
This key figure shows what proportion of the available capacity is required to meet the forecast demand. It helps to plan capacity utilization and ensure that sufficient resources are available to meet the expected demand.
There are numerous forecasting methods that can be used depending on the objective and data available. Selecting the right forecasting method depends on various factors, including the available data, the complexity of the process to be forecasted, and the accuracy requirements. It often makes sense to combine several methods to improve forecasting accuracy.
There are different types of forecasts that are used depending on the area of application and objective. The choice of the right type of forecast depends on the specific requirements and objectives. Each method has its strengths and weaknesses, and it often makes sense to combine several methods in order to obtain the most accurate forecast possible. Here are some of the most important types of forecasts:
| Type of Forecast | Definition | Methods |
| Time Series Forecasts | Forecasts based on historical data at regular time intervals. | Simple moving averages, exponential smoothing, ARIMA |
| Causal Forecasts | Forecasts that use relationships between different variables. | Regression analysis, econometric models |
| Qualitative Forecasts | Forecasts based on subjective assessments and expert opinions. | Delphi method, market surveys, expert surveys |
| Seasonal Forecasts | Forecasts that take into account seasonal patterns and fluctuations. | Seasonal moving averages, seasonal decomposition |
| Combination Forecasts | Use of several forecasting methods to increase accuracy. | Weighted average forecasts, ensemble methods |
| Sales Forecasts | Forecasting the future sales figures of a company. | Sales data analysis, customer surveys, competitive analysis |
| Demand Forecasts | Forecasts of future demand for products or services. | Trend analyses, customer behavior analyses, market segmentation |
| Economic Forecasts | Forecasts of economic indicators such as GDP, inflation or unemployment. | Macroeconomic models, leading indicator analysis |
| Financial Forecasts | Forecasts of a company's key financial figures. | Financial modeling, scenario analysis |
| Technology Forecasts | Forecasts of the development and introduction of new technologies. | Technology lifecycle analysis, roadmapping |
| Capacity Forecasts | Forecasts of future production capacity and utilization. | Production data analysis, bottleneck analysis |
A retail company wants to forecast sales for the coming year in order to optimize its production and inventory strategies. The company sells seasonal products and has historical sales data for the last five years.
Assume that the historical data analysis shows a steady increase in sales of 5% per year on average and a strong seasonality with peak sales in December and a decline in January and February. Exponential smoothing produces the following forecasts for the coming year:
| Month | Forecasted Sales (in €) |
| January | 50,000 |
| February | 45,000 |
| March | 55,000 |
| April | 60,000 |
| May | 65,000 |
| June | 70,000 |
| July | 75,000 |
| August | 80,000 |
| September | 85,000 |
| October | 90,000 |
| November | 95,000 |
| December | 120,000 |
A forecast includes historical data, current trends, assumptions about future developments and the models or methods used for forecasting.
Planning is the definition of goals and measures for the future, while a forecast is an estimate of future developments based on current data and trends. Planning is therefore more strategic and long-term, while forecasting is more short-term and adaptable.
A rolling forecast is a dynamic forecasting method in which the forecast period is continuously updated by regularly adding new data and extending the period so that you always have up-to-date and forward-looking planning.
A rolling forecast helps you to react to changes more flexibly and promptly, as it is continuously updated and always looks a fixed period into the future.
As a rule, a forecast should be prepared and reviewed on a monthly basis to ensure that forecasts are always up-to-date and accurate.
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