Calculation and Allocation of Complexity Costs Using Process Data Mining Over the last years, manufacturing companies extended their product portfolio and derived numerous variants of their products. To make strategic decisions regarding the product portfolio, it is essential to know the costs for each product and each product variant. The exact allocation of the indirect costs in a company to the products that cause them is thereby often not possible. Existing methods, such as activitybased costing or process costing, aim for a causerelated allocation of costs, particularly indirect and overhead costs, to products. Because required data for the cost model has to be collected manually, existing approaches are extremely time consuming, costly and do not represent the current cost status. The increasing digitization and use of business information systems in companies provide new capabilities to get costrelevant data faster and increase the timeliness of the cost calculation. In this paper, a method to calculate and allocate the complexity costs to related products is provided. The method makes use of data in different information systems which is analyzed and structured by process data mining. Thus, the effort for the calculation of complexity costs should be decreased and the transparency and timeliness should be increased.

CostBased Text Understanding to Improve Maintenance Knowledge Intelligence in Manufacturing Enterprises Abstract   Improving maintenance knowledge intelligence using text data has not been largely explored in the literature of production and engineering management. The stateoftheart approaches and solutions mainly focus on either clustering and classification of maintenance logs, or extracting additional (meta)data e.g. failure time data from maintenance text reports, operatorsxe2x80x99 workbooks and digital logbook. Knowledge Discovery from Text (KDT) enables finding undetected causalities, hidden patterns, frequencies, associative relations, and sentiments in maintenance text repositories. Applying KDT may enhance understanding the content of text data syntactically and semantically. However, advanced KDT approaches do not significantly provide meaningful and explainable outcomes, due to certain barriers in manufacturing enterprises, namely availability and quality of (longitudinal) maintenance text data.  To overcome these barriers in real world industrial maintenance, generate added value in industrial maintenance, and lay the ground for autonomous maintenance decisionsupport in the context of Industry 4.0, the first step is to adopt KDT methods and accordingly provide maintenancespecific solutions considering practical challenges and possibilities.  This paper discusses the lack of understanding maintenance text data and examines its effect on maintenance knowledge intelligence in manufacturing enterprises. A compositional framework for text understanding (TextPlan) is introduced. TextPlan explores quantification of text data in both syntax and semantic levels, i.e. how to vectorize an annotated maintenance report into numeric values, which represent cost data, hidden associations and sentiments. A prominent feature of TextPlan is costbased text analysis, which decomposes a maintenance text report into separate cost items, and then (re)composes the findings to estimate the total maintenance cost associated with the given report. Finally yet importantly, TextPlan consolidates the findings into a Text Understanding Map for assisting maintenance planner, based on three proposed measures of text comprehension, namely Association Measuring Index (AMI), Opinion Index (OI) and Cost Vector (CV).