Forecast

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.

Forecast Definition: What is Forecast Planning?

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.

What is the Aim of Forecasting?

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:

Support decision-making
- Strategic planning: A forecast helps companies to develop long-term plans by providing insights into future market trends and business developments.
- Operational decisions: Short-term forecasts support day-to-day decision-making and help to utilize resources efficiently.
Risk Management
- Identification and assessment of risks: By predicting possible future developments, companies can identify risks at an early stage and take appropriate measures to minimize them.
- Contingency planning: Forecasts make it possible to prepare for different scenarios, which increases a company's flexibility and resilience.
Resource planning and allocation
- Efficient use of resources: Forecasts help to precisely plan and allocate the required resources (e.g. personnel, materials, capital).
- Capacity planning: Companies can better adapt their production capacities to expected demand in order to avoid bottlenecks and overproduction.
Financial Planning and Budgeting
- Sales and cost forecasts: More accurate forecasts enable realistic budget planning and help to set financial targets.
- Liquidity management: By forecasting income and expenditure, companies can better manage their liquidity and avoid bottlenecks.
Market and competition analysis
- Identification of market trends: The forecast helps to recognize emerging trends and changes in the market at an early stage.
- Competitive advantage: Companies can adapt their strategies to stay ahead of the competition and take advantage of market opportunities.
Improving customer relationships
- Demand forecasting: More accurate forecasting enables better anticipation of customer needs and the provision of appropriate products or services.
- Customer satisfaction: The timely and needs-based delivery of products and services can increase customer satisfaction.

Find Similar Financial Lexicon Topics here:

Back to the Financial Lexicon

Difference: Forecast Budget

This table shows the main differences between forecast and budget and illustrates how both instruments are used in corporate planning.

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

Create Forecast: Forecast Calculation

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:

Objective
- Determine the purpose of the forecast: Clarify what exactly is to be predicted (e.g. sales, demand, costs).
- Define the time horizon: Determine the period for which the forecast should apply (e.g. next quarter, next year).
Collect data
- Historical data: Collect relevant historical data required for the forecast (e.g. sales data from recent years).
- External data sources: Consider external factors such as market trends, economic indicators and industry data.
- Current data: Use current information and trends that could influence future development.
Data analysis
- Data cleansing: Ensure that the data is complete and error-free.
- Trend analysis: Identify patterns and trends in historical data.
- Seasonal adjustments: Take into account seasonal fluctuations and other cyclical effects.
Selection of forecasting methods
- Qualitative methods: Expert opinions, Delphi method, market surveys (useful when data availability is limited).
- Quantitative methods: Time series analysis, regression models, exponential smoothing, ARIMA models.
Modeling and calculation
- Model construction: Choose the appropriate forecasting model based on the data and the objective.
- Perform calculations: Apply the chosen methods to calculate the forecast values.
- Validation : Check the accuracy of the model by comparison with known data or by cross-validation.
Interpretation and adjustment
- Interpret results: Analyze the forecast results and understand the underlying assumptions and influencing factors.
- Make adjustments: Adjust the model if necessary to increase accuracy or incorporate new information.
Creating the forecast report
- Documentation: Create a report that clearly presents the methods, assumptions, results and recommendations.
- Visualization : Use charts and graphs to clearly present the forecast results.
Communication and monitoring
- Communicate results: Share the forecast results with relevant stakeholders.
- Regular review : Monitor actual developments and compare them with the forecast values to identify deviations and make adjustments if necessary.

By carefully applying these steps, you can create well-founded and reliable forecasts that serve as a valuable basis for strategic decisions.

Forecast terms

Forecast terms

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.

This list does not claim to be exhaustive, but provides a general overview of the advantages and disadvantages of a mortgage. The individual circumstances and needs of each borrower should be taken into account when deciding for or against a mortgage.

Key figures in forecast controlling

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:

Forecast Variance

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.

Lead Time

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.

Relative Capacity Share

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.

What Forecast Methods are there?

There are numerous forecasting methods that can be used depending on the objective and data situation. Here is an overview of the most important forecasting methods:

Overview: The different Forecast Methods

The following diagram provides an overview of the different forecast methods that exist and are used.

Explanation: The different Forecast Methods

Choosing the right forecast method depends on various factors, including the available data, the complexity of the process to be forecast and the accuracy requirements. It often makes sense to combine several methods in order to improve the accuracy of the forecast.

Time series-based Methods

Simple movingaverage: Average of the data over a certain period of time.
Weighted moving average : Similar to the simple moving average, but with differently weighted data points.
Exponential smoothing : Weights the most recent data more heavily to capture trends and seasonal patterns.
Holt-Winters method: Extension of exponential smoothing that takes both trend and seasonality into account.
ARIMA (AutoRegressive Integrated Moving Average): Combines autoregression, integration and moving averages to model complex time series.

Causal Methods

Simple linear regression: Models the relationship between two variables.
Multiple regression: Models the relationship between a dependent variable and several independent variables.
Econometric models: Complex models that combine economic theories and statistical methods.

Qualitative Methods

Delphi method: Expert opinions are collected and evaluated iteratively until a consensus is reached.
Market surveys: Collection of data by interviewing customers or experts.
Jury of Executive Opinion: A group of executives give their assessment.

Seasonal Methods

Seasonal moving averages: Averages that take seasonal patterns into account.
Seasonal decomposition of time series: Decomposes the time series into trend, seasonal and random components.

Combined Methods

Weighted average forecasts : Combines several forecasts into a single forecast.
Ensemble methods: Uses a combination of different models to increase forecast accuracy.

Machine learning and AI Methods

Neural networks: Complex models that can recognize patterns in large amounts of data.
Support Vector Machines (SVM): Models that classify data points into categories and make predictions.
Random Forests: Ensemble method consisting of many decision trees.

Specific Methods for specific Applications

Life cycle analysis : Predicts the course of a product life cycle.
Bottleneck analysis: Identifies bottlenecks in production processes and predicts their effects.
Technology life cycle analysis: Evaluates the development and spread of new technologies.

Types of Forecast

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

Forecast Example: Sales Forecast for a Retail Company

Scenario

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.

Step-by-step Guide

Goal setting
- Objective: Forecast monthly sales for the coming year.
- Time horizon: 12 months.
Collect and prepare data
- Historical data: Collect monthly sales data for the last five years.
- Cleanse data: Ensure that the data is complete and correct. Missing data points may be interpolated or replaced by estimates.
Data analysis
- Trend analysis: Examine long-term sales trends, e.g. whether sales are trending upwards or downwards.
- Seasonal effects: Analysis of seasonal patterns, e.g. higher sales during the vacation season or in certain months.
Selection of the forecasting model
- Model: Exponential smoothing, as the model is well suited to taking both trends and seasonal effects into account.
- Parameters: Determination of smoothing parameters (伪 for trend and 纬 for seasonality) based on historical data.
Model calculation and validation
- Implementation: Application of the exponential smoothing model to the historical sales data.
- Calculation: Performing the calculations to forecast monthly sales for the coming year.
- Validation: Comparing the forecast results with a portion of the historical data that was not used for modeling in order to check the accuracy.
Interpret and adjust results
- Analysis: Reviewing the forecast sales and interpreting the results in the context of known market conditions and company strategies.
- Adjustment: If necessary, adjust the model parameters or select an alternative model if the forecast inaccuracies are too high.
Preparation of the forecast report
- Documentation: Preparation of a report containing the methods, assumptions, results and recommendations.
- Visualization: Presentation of the forecast results in diagrams, e.g. monthly sales compared to historical data.
Communication and monitoring
- Sharing: Presentation of forecast results to the management team and relevant departments.
- Monitoring: Regular review of actual sales compared to the forecast to identify deviations and adjust the model if necessary.

Example Results

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 鈧�)
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

These forecasts help the company to plan its stock levels, adapt marketing strategies and deploy resources efficiently in order to meet expected demand and make the most of sales opportunities.

Find Similar Financial Lexicon Topics here:

Back to the Financial Lexicon

FAQ

What belongs in a Forecast?

A forecast includes historical data, current trends, assumptions about future developments and the models or methods used for forecasting.

What is the difference between Planning and 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.

What is a Rolling Forecast?

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.

Why Rolling Forecast?

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.