As companies today are forced to act in a complex and changeable environment, much focus has come to lie upon their possibility to react and adapt to changes. It is therefore of great importance that companies are able to follow up on and analyze their economic progress on a regular basis. Due to this the interest for forecasting has increased significantly, and even though several authors describe the downsides of forecasting the subject itself does not seem to become less important. In cases where forecasts are used under the right circumstances they can be a highly useful analytic aid, especially when combined with computerized control systems.In light of the importance of forecasting the focus of this study has partly been to investigate how adaption and test of a set of predefined forecast models can be made, and the aim is to give recommendations about which model is best suited for use in an application for economic control and forecasting. In the study, forecasting was made based on a time series curve representing the difference between real results and budget, and the models were evaluated using MAPE (Mean Absolute Percentage Error, i.e. calculations of the error margin between real data and forecast data). Overall the adaptive model performed better during test. Based on this the conclusion was that evaluation of the model category should continue, to see if better results can be achieved with a more advanced adaptive forecast model. The gist of the model evaluation however was that forecasts should depend on the situations where they are to be used as this affects the choice of forecast model. It can be said that two scenarios should lie as groundwork, and that the company itself should decide where they stand and which model is best suited for their purposes. Depending on whether the forecast is to be used for long term or short term purposes the choice of model will vary, resulting in different models being useful. Performed tests also showed that historical data highly affects how well the forecast model adapts to a time series. With a large amount of historical data the forecast model is suited for long term forecasts, as the model adapts slower to changes. If instead a smaller amount of data is used the model is more adaptive, hence giving better circumstances for short term forecasts.The model that best corresponded to the company’s demands was later implemented in an application for economic control and forecasting, which was developed in union with a local IT consulting company. In light of this, this study also aims to show how implementation of an information system can be done from a customer value perspective. Lean development has been the main groundwork used.