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Sales revenues of enterprises are often subject to seasonal fluctuation. This leads to high or low utilized resources and this in turn to revenue losses. Hence, the enterprises invest a high effort to improve long and short-term resource utilization. In this context, disregarding future capacity utilization within the process of quotation leads to short-timed capacity adjustments for instance, additional work hours across seasons. This paper presents an approach which focuses on dependencies between costs and capacity by linking cost pricing and production scheduling. A first evaluation at an MTO supplier shows that order delays can be reduced by up to 95% and total costs by 21% compared to using the most appropriate priority rule.

Unforeseen machine tool failures due to technical issues can cause downtimes leading to delays during production. To reduce delays, rescheduling of the production is, in most cases, necessary. However, warranting such a change requires reliable knowledge about the duration of the failure. This article presents a method to provide this knowledge by estimating the duration of a machine tool failure based on previous failure durations. Using the cross-industry standard process for data mining (CRISP-DM) and statistical methods, the embedded model for failure classification and duration is continuously improved. The method is thoroughly tested using multiple distributions, parameters and a practical use case. The results show high potential for predicting the duration of machine tool failures, which consequently could lead to improved quality of rescheduling.