Detection of the need for a model update in steel manufacturing

When new data are obtained or simply when time goes by, the prediction accuracy of models in use may decrease. However, the question is when prediction accuracy has dropped to a level where the model can be considered out of date and in need of updating. This article describes a method that was developed for detecting the need for a model update. The method is applied in the steel industry, and the model whose need of updating is under study is a regression model developed to model the yield strength of steel plates. It is used to plan process settings in steel plate product manufacturing. To decide on the need for updating, information from similar past cases was utilized by introducing a limit called an exception limit. The limit was used to indicate when a new observation was from an area of the model input space where the prediction errors of the model have been too high. Moreover, an additional limit was formed to indicate when too many exceedings of the exception limit have occurred within a certain time scale. These two limits were then used to decide when to update the model.