How can we measure the forecast

Take a look into the future with forecast models

Those who already know today what their customers will buy tomorrow have a clear advantage. Data science analyzes make such forecasts possible using forecast models. One of the basic assumptions is: the more we know about past behavior, the more precisely we can make statements about future events. Such forecasts are only possible scenarios that have a certain probability of occurring - but if they are based on meaningful data, the decision-making quality based on them can be significantly improved.

Put simply, even weather forecasts can be used to optimize profits: If a sunny weekend is expected, an ice cream producer will produce more ice cream in order to be able to meet the expected demand. The weather also affects other industries such as civil engineering.

You can also read our blog article, in which we deal with the effects of artificial intelligence on sales forecasts.

The situation is very similar with forecasts of the development of share prices, prices for seeds, food or oil on the world market, which have an impact on agriculture, industry or the public sector. In order to obtain a basis for data-based calculations, forecast models are based on company data, data from customer relationship management, machine data or transaction data.

Forecast models: basic functionality

The goal is Assess future events early and better than the competition. Such knowledge is a decisive advantage, especially in an increasingly complex business environment.

The basic functionality of forecast models is always similar. After the question in the company has been clarified in principle (business processes), three essential steps usually follow one another: After collecting and analyzing the data (reporting / analysis), it is compared with the data from the current status quo (monitoring) and finally the forecast of possible future developments (predictive analytics).

Forecast models are usually based on three main steps:

  1. Reporting / analysis
  2. Monitoring
  3. Predictive Analytics.

In essence, it is about finding patterns in predetermined sets of data that allow a deeper understanding of past events or past customer behavior. These findings are then used as the basis for simulating future behavior.

The more differentiated a forecast model works, the more precisely it can make predictions. That is why it is so important measure as much relevant data as possible and to be integrated into the forecast model. In order to obtain reliable forecasts, it is not always crucial to evaluate as much data as possible - more data does not automatically mean better forecast quality.

Difficulties in interpreting forecast models

When using forecast models, one thing must be clear: sometimes forecasts cannot be accurate, for example when unforeseen or difficult to measure factors bringing about an unexpected event, the best forecasting model will not help.

In a business context, however, it makes sense to rely on forecast models. You consider decisions based on heuristic assumptions, gut feeling or intuition. Even if the forecasts may not be 100 percent correct, they serve the purpose thatsecure your own decisions. In retail in particular, it is worth the effort to optimize material and warehouse stocks with the partial requirement forecast and to control the logistics in a cost-efficient manner.

Machine learning to improve forecast models

In order to obtain meaningful and realistic forecasts, forecast models must be regularly checked and, if necessary, expanded or adapted. Today, predictive models can learn thanks to advances in machine learning. Certain disturbances and system uncertainties can be identified and regulated in this way. The longer a forecasting model is developed and gained experience, the better it gets.

A common one The source of error is the data basis itself. No matter how good forecast models are, if the data basis is incorrect, the forecasts are also incorrect.

A current problem that makes this connection clear are the so-called social bots. These intelligent programs act on social media as if they were human users. As a result, they falsify social media data so much that predictions based on the analysis of social media behavior do not match reality. The creation and assurance of a high data quality and validity can therefore be of great importance for forecasts.

The growing importance of trends and forecasts in companies

In companies that work in a customer and market-oriented manner, it is of crucial importance to have a good picture of the status quo and future developments in the market. Even if the future cannot be precisely predicted, it is often a decisive advantage to be able to assess future developments better than the competition.

Forecast models are also an important tool when it comes to adapting products, offers and services according to trends and customer wishes. Forecast models can be used in a wide variety of industries and business areas.

In individual departments such as purchasing, warehouse and logistics, but also right up to the executive suite, prognostic knowledge Improve decisions and optimize processes.

On closer inspection, forecasting models have little to do with looking into a crystal ball. Rather, they are instruments that have a wide range of uses. They can serve to reduce or optimize inventories, they improve the entire supply chain, offer more security for decision-makers through data-supported principles or secure competitive advantages because trends are recognized at an early stage.