Whether you’re releasing a new product or trying to conquer a new sector of your market, the risks can be tremendous. To misquote Field of Dreams, you can build it but will they come? It would help to be able to look into the future but there’s no such thing as a crystal ball to guide your decision making.
But that doesn’t mean there’s no way to try and predict the likely outcome of your decisions, based on the information available to you.
This is where forecasting models come in. They can give you a peek into possible futures, so you can make informed decisions today. This guide will delve into five different types of forecasting models, explaining how they work and which situations they’re suited for.
By understanding these different forecasting methods, you’ll be able to identify the best one for you and look ahead with more confidence. Let’s get started.
What is a forecasting model?
A forecasting model is a system that makes use of historical data and statistics to help predict future outcomes or trends. These models vary in complexity and are widely used across various industries—such as finance, marketing, and supply chain management. Which one you use will depend on your goals.
You can use these models to help predict outcomes regarding sales, consumer behavior, and supply & demand so you can make informed decisions about everything from a product launch to employee scheduling.
5 common types of forecasting models
It’s clear different situations call for different types of forecasting models but in order to choose the most relevant one for you, you first need to understand some of the most commonly used types.
We’re covering the following five in more depth below:
- Time Series Model: good for analyzing historical data to predict future trends
- Econometric Model: uses economic indicators and relationships to forecast outcomes
- Judgmental Forecasting Model: leverages human intuition and expertise
- The Delphi Method: forms a consensus based on expert opinions
- Scenario Planning: more creative and exploratory, this focuses on the range and diversity of possible futures
Each of these forecasting models, whether quantitative (like Time Series and Econometric) or qualitative (like the other three), can serve different needs and offer unique insights, giving your business the best chance at success you can get (short of finding an actual crystal ball).
We’ve collected some steps for how to get started and provided an example of how each model could be applied. We’ve even suggested a relevant tool to get the job done for the two quantitative models.
Time Series Model – best for continuous data with clear trends
A time series model focuses on historical data and patterns to predict future trends. This is arguably the most straightforward type of forecasting model and is commonly used in stock market predictions, sales forecasting, and even weather forecasts.
How to use a Time Series Model
- Collect data: gather historical data that’s most relevant to the forecast, for example: the spending habits of your target market over a given period
- Identify patterns: look for trends, seasonal patterns, or cycles in the data
- Select a model: choose a specific type of time series model like ARIMA or Exponential Smoothing
- Apply the model: use statistical software to input the data and run the model
- Analyze your results: interpret the output to make informed decisions
Example of a Time Series Model
A retail store wants to prepare for the holiday season. By using a time series model, they can analyze sales data from previous years to forecast demand for various products. This enables them to manage inventory more efficiently, ensuring they neither run out of popular items nor overstock items that don’t sell well.
They could also look at time tracking data to ensure they aren’t caught off guard by seasonal staffing demands.
Best tool: Tableau
Tableau excels in data visualization and can easily handle large datasets, making it an ideal choice for time series analysis. Its drag-and-drop interface allows for seamless manipulation of data to identify trends, cycles, and patterns.
Econometric Model – most useful when economic variables are involved
While a time series model offers a statistical analysis of data measured over time, an econometric model applies statistical methods to answer economic questions. This model is widely used in economic forecasting and policy planning.
How to use an Econometric Model
- Specify the model: define the economic variables and relationships to be studied
- Gather your data: collect historical and current data for these variables
- Estimate parameters: use statistical methods to determine the strength of relationships between variables
- Test the model: run the model to see if it adequately represents the data
- Make predictions: use the model to forecast future economic conditions
Example of an Econometric Model
A real estate development firm is planning to build a new residential complex. Using an econometric model, they can incorporate various economic factors like interest rates, employment rates, and inflation to predict future real estate market conditions. This information helps them decide whether to go ahead with the project or wait for a more opportune time.
Best tool: Stata
Stata is a statistical software that offers a comprehensive suite of tools specifically designed for econometric analysis. Its capabilities include data management, statistical analysis, and graphics, making it well-suited for complex econometric models that require deep statistical analysis.
Judgmental Forecasting Model – good for new or unprecedented situations
Despite all the advancements we’ve made with technology, sometimes, human intuition is the best tool at our disposal. Judgmental forecasting relies on expert opinion and experience, making it particularly useful for new product launches or events with little historical data.
Unlike the two previously mentioned, this one is a qualitative method rather than quantitative.
How to use a Judgmental Forecasting Model
- Identify experts: select people with relevant expertise
- Collect opinions: ask these experts for their forecasts and reasoning
- Aggregate data: combine these insights into a single forecast
- Apply weights: give more weight to opinions from more reliable sources
- Analyze and use: summarize the findings for decision-making
Example of a Judgmental Forecasting Model
A tech startup is planning to launch a new type of wearable technology. Since there’s no historical data available for this specific product, they use judgmental forecasting. They consult experts in the tech industry and potential customers to gather opinions and make a more educated guess on how well the product will be received.
The Delphi Method – ideal for long-term forecasting with expert input
Another qualitative approach, the Delphi Method is a structured form of judgmental forecasting that collects opinions from multiple experts, for instance in a panel, to arrive at a consensus. It’s often used for long-term technological forecasts or medical research and many believe it’s more reliable (ie. less subject to biases) than trusting individual experts.
How to use The Delphi Method
- Select a panel: identify a panel of relevant experts
- Round of questions: pose a series of questions to the panel
- Aggregate and share: summarize the responses and share them with the panel
- Iterate: conduct additional rounds, refining questions and seeking consensus
- Final forecast: once consensus is reached, compile the final forecast
Example of The Delphi Method
A pharmaceutical company is considering investing in the research and development of a new drug. They assemble a panel of medical experts, pharmacologists, and economists to use the Delphi Method. After several rounds of questioning and discussion, they reach a consensus on the potential success and profitability of developing the new drug.
Scenario Planning – recommended for uncertain or volatile conditions
A bit different from the other four we’ve discussed here, this qualitative method explores various future conditions instead of projecting a single, most likely path. Scenario planning can be useful for preparing for various uncertain or volatile conditions. After all, failure to plan is planning to fail!
How to use Scenario Planning
- Identify factors and trends: recognize the factors that could influence future outcomes
- Develop scenarios: create multiple plausible future scenarios
- Analyze: evaluate each scenario for its likelihood and impact
- Strategize: develop action plans for the most likely or impactful scenarios
Example of Scenario Planning
A logistics company faces several uncertainties such as fluctuating fuel prices, international trade policies, and technological changes. They use scenario planning to create different possible future scenarios ranging from ‘best case’ to ‘worst case’. This enables them to develop various strategies for each scenario, helping them to adapt quickly when change occurs.
Navigating the future
While we still haven’t tracked down that elusive crystal ball, we’ve given you a few ideas for different forecasting methods. The key takeaway? Choose the model that aligns with your specific needs, and don’t shy away from combining methods for a more comprehensive view.
Remember: you don’t need to be launching a new product or reinventing the wheel to make use of these methods. You can begin with something as straightforward as your time clock data, to ensure you never miss a step with your employee scheduling.
Frequently asked questions about forecasting models
What are the 4 basic types of forecasting methods?
The four basic types are time series, causal methods (like econometric), judgmental forecasting, and qualitative methods (like Delphi and scenario planning).
What’s the difference between qualitative and quantitative forecasting methods?
Quantitative methods, like time series and econometric models, use numerical data and statistical techniques. Qualitative methods, like judgmental forecasting and Delphi, rely on expert opinions and are not strictly based on numerical data.
What’s an example of a forecasting method for management?
For management, a balanced scorecard approach combines quantitative data and qualitative insights for a rounded forecast, helping in decision-making across various departments.