What is forecasting

What is forecasting, and how does it work?

What is forecasting, and why do we need it in business?

Forecasting is the act of predicting future events based on current data.  Companies can use it to identify business opportunities, risks, or threats.

Forecasting doodle

The first thing we do when we visit a new city is ask for directions.  Our goal is to reach the destination without getting lost or encountering any obstacles along the way.  The route map is our forecast.

The different types of Forecasting Methods.

There are four types of forecasting:

1. The Straight-line method

The straight line is the most basic kind of forecast.  It is where we assume that nothing will change and then stick with our plans.  This approach assumes that we will get the same results if we do the same thing every day.

2. The Moving average method

This method is where we average our data over time.  It can be done daily, weekly or monthly, and it shows how your customers react to the various business activities (ex: sales promotions and advertising campaigns).

3. Simple linear regression method

This method is about identifying patterns between present data and previous data.  For instance, what are the sales for this month compared to last month, same-store sales versus online sales, or client versus competitor’s market share?

4. Multiple linear regression method

Use this method when it’s needed to predict future results with more than two variables.  For instance, how will the length of time customers utilize our products impact their purchases?  Or what are the factors that influence demand for new housing units in a city?

If we consider that healthy forests require both thinning and natural wildfires to remain healthy, we could assume that a forest without any of these factors would eventually become unhealthy.

How to create your own forecasts?  

The first step is to collect past data.  Make sure you have accurate values; think about the scale, units (ex: monthly or annually), and timing.  If you are using previous forecasts as your base, make sure they are realistic and unbiased.

Forecasts can be created by hand, but information technologies provide more efficient tools.  The most popular forecasting tool is Crystal Ball by Oracle, which has an intuitive interface and allows the forecasting process to be automated.

Five steps to creating your forecasts.

1. Design your project with critical questions in mind.

This is the most important step in forecasting.  What are you trying to achieve?  Specifically, what are you planning to forecast?  

2. Collect past data.  

This is where you figure out what information you have available and how useful it will be for your future forecasts.

3. Create the forecast model.

This is where you decide how to use your data to create the forecast.  The model project might include customer, competitor, or market-size information used when developing forecasts.

4. Calculate the results.

Using the modeling tools, you can calculate forecasts with confidence intervals.  You can also include a variety of different scenarios and what-if analyses.  This step is where you decide whether to use your forecast as an input for decision-making or a performance metric.

5. Present the results and monitor progress over time.

This step is where you monitor your forecast over time and adjust it accordingly.  Forecasts can also be used as a performance metric for managers to monitor their staff’s progress in achieving specific goals.

How to Check and improve your forecasting model?  

If you have observed the following after a while, the forecasting model is probably not fit for purpose.

  • The forecast is either overly pessimistic or optimistic.  In this case, it means that your forecasting method relies too much on current trends and does not consider other factors that might affect your business (ex: regulatory changes).
  • The forecast is far from reality.  In this case, it means that your forecasting method does not consider all the available information.  

You can make improvements in three stages:

1) better data.

The first step when improving the forecasting model is to improve the data.  Here you can combine forecasting models, use different models for each data source, or apply a technique called independent forecasts.  A recent article in Harvard Business Review suggests that instead of using past performance to predict future outcomes, it is better to base your forecast on a set of external benchmarks from other companies in the same industry.  Another approach is to use expert opinion provided by subject-matter experts or managers familiar with the industry and its players.

2) change in methodology.

The second step when improving the forecasting model is to improve the methodology.   This means that first, you should decide how reliable and valid the collected data is and whether it is needed for creating forecasts.  Second, you should determine which factors are more important for your industry, customers, or competitors.  

To improve forecasting methodology, you can apply regressions techniques by controlling various external factors that could influence your company’s performance.  Examples of external factors could be industry trends, government regulations, macroeconomic factors, and others.  If you do not have a lot of data or unreliable data, you can use predictive modeling to project future outcomes.

3) combination of both

The third step when improving the forecasting model is to combine the first two steps.  In this case, you can use external benchmarks together with your historical data.  You can also use predictive modeling in case that the amount of available information is not sufficient for creating forecast models.

The forecasting model is most effective when considering all of these factors (data, methodology, and inputs) to create high-quality forecasts with confidence intervals.  

Benefits and drawbacks of using forecasters in your business strategy.

The benefits of using forecasters

  • to create a competitive advantage, you can use forecasters industries.  For example, if you are launching a new product, using forecasting models will allow you to predict market reaction and demand for your product.
  • Because forecasts inform decision-making processes, they will help managers make better decisions when allocating resources.
  • Forecasting models can help companies enter new markets by creating demand forecasts for their products in different regions.  It’s essential to have this information when planning the marketing strategy and allocating resources between other markets.
  • By understanding trends, businesses can predict customers’ future needs and adjust accordingly to meet market demands, which will increase customer satisfaction.
  • You can use forecasting methods to solve supply chain problems by creating production forecasts translated into inventory planning and procurement forecasts.

The drawbacks of using forecasters in your business strategy.

  • Typically forecasting models are not cheap, but their cost is justified if they increase business performance.  The main drawback of forecasting is that it requires massive time and resources to create high-quality forecasts.  Another downside of forecasting is that it does not work every time because the future cannot be predicted (and this information should also be considered when creating forecasts).
  • The main drawback of using forecasters in your business strategy is that they are subjective because forecasts rely on human interpretation.  It is also hard to find an expert who will deliver accurate forecasts based on personal opinions.
  • As with other methods, forecasting models are not always correct, and it can be hard to explain why certain things happened (i.e., why the demand for your product dropped).
  • Forecasting models rely on the available data, making them less effective when there is a lack of data.  
  • Forecasting models are not flexible, and they do not allow changes to demand or supply forecasts, which can pose problems for businesses that operate in dynamic environments.

Conclusion:

Forecasting models are an essential tool in developing business strategies.  But decision-makers must be cautious with their predictions because they’re never 100% accurate. So it’s always better to provide a range instead of an exact value.  This way, you can predict both success and failure while taking into account all possible outcomes.  You can also use forecasting in supply chain management, and it is a helpful tool for businesses that operate in dynamic environments.

Predictions are never 100% accurate. So it's always better to provide a range instead of an exact value. #forecasting Click To Tweet

Forecasting models are a helpful way to create strategies because they help you predict future market behavior or evaluate the effectiveness of your business strategy.  To improve forecasting models, it is crucial to combine forecasters’ data, methodology, and expertise.  Forecasts are most effective when they consider all these components to create high-quality forecasts with confidence intervals.