Abstract: For providing recommendations for bill of materials revision, a database is storing bills of materials for a set of products. A pattern learning module processes the stored bills of materials to learn patterns. Embodiments for learning structural and temporal patterns are provided. A pattern application module applies the patterns to a current bill of materials for a product of interest and forecasts recommendations, with each recommendation indicating how the current bill of materials should be updated. A user interface outputs the recommendations along with the applied patterns and their confidence values. The method and system provide an automized framework that forecasts revision for products. That framework helps to boost product quality by avoiding late recognition of change needs that would most likely negatively impact product and cost performance. Automatically assessing the change needs reduces hours spent by domain experts on these tasks, which saves internal costs.

Abstract: A method for providing recommendations concerning a project configuration to configure an industrial system includes: receiving via an interface a project query related to re-configuring the industrial system, represented by a knowledge graph, wherein components of a set of components are represented by graph nodes and relations between two components which are represented by edges between the corresponding nodes; automatically mining logical rules from the knowledge graph which are assigned to confidence values, whereby the confidence value of each rule is estimated by determining the frequency of occurrence of the rule body in the knowledge graph and by validating if the rule head holds; predicting candidate components of the project query for each logical rule by calculating a score for each candidate component; generating at least one recommendation; and—outputting the generated at least one recommendation to a user via a user interface or executing the generated recommendations.

Abstract: Production logs and industrial ontologies are processed with an inductive logic program performing class expression learning in order to create class expressions, with each class expression representing a constraint or property of a skill of a production module. The resulting class expressions are ordered by a metric to form an ordered recommender list and displayed to a user for postprocessing. The user selects suitable class expressions from the ordered recommender list, so that the system can build a machine-readable skill description with the selected class expressions. This approach to generating formal, machine-readable skill descriptions minimizes the labor time and domain expertise needed to equip production modules with their skill description. Selecting the correct class expression from the automatically generated ordered recommender list is a much lower effort than manual labeling from scratch.