Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In most industrial processing scenarios the value of a product increases over time in the value chain. To avoid unnecessary processing steps, it is of immense importance to detect defects as early as possible in the value creating process. These situations of interest can be distinguished as specified and unspecified situations, dependent on whether the cause-effect relation is known and defined or not. In this article  describe ongoing work on a proactive disruption management system for manufacturing environments, which helps being prepared for the unexpected by applying a combination of unsupervised and supervised machine learning for the identification and prediction of unspecified situations and adopting data mining techniques to derive predictive patterns for specified situations.  also introduce a real-world use case from the field of semiconductor manufacturing and present first preliminary results.
['Suad Sejdovic', 'Yvonne Hegenbarth', 'Gerald H. Ristow', 'Roland Schmidt']
I am writing a research paper on a idea Proactive disruption management system: how not to be surprised by upcoming situations
[{"from": "human", "value": "I am writing a research paper on a idea Proactive disruption management system: how not to be surprised by upcoming situations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In most industrial processing scenarios the value of a product increases over time in the value chain. To avoid unnecessary processing steps, it is of immense importance to detect defects as early as possible in the value creating process. These situations of interest can be distinguished as specified and unspecified situations, dependent on whether the cause-effect relation is known and defined or not. In this article  describe ongoing work on a proactive disruption management system for manufacturing environments, which helps being prepared for the unexpected by applying a combination of unsupervised and supervised machine learning for the identification and prediction of unspecified situations and adopting data mining techniques to derive predictive patterns for specified situations.  also introduce a real-world use case from the field of semiconductor manufacturing and present first preliminary results."}]