Development of a Framework for the Systematic Identification of AI Application Patterns in the Manufacturing Industry

In any industrial sector an increasing number of interconnected objects along with more sensors relying on shortened query rates cause large data volumes that can be utilized for product and process improvement. Methods from the Artificial Intelligence (AI) technology spectrum have the potential to uncover complex interdependencies in data sets instantly, improve analysis results steadily and adjust to changing external factors dynamically. AI is a heterogeneous technology bundle mainly originating from statistics, advanced analytics and machine learning (ML), which is built up in different layers. Current research is lacking a comprehensive analysis of these different AI technology layers and their corresponding characteristics that can serve as an orientation guideline especially for manufacturing companies. This research paper derives a nomenclature for the AI technology ecosystem in order to facilitate the discussion of this topic. Moreover, a systematic framework (morphology) is derived in order to classify current AI applications and to identify crucial AI technology composition patterns that might be helpful for future AI application development. Potentially promising scopes for the derivation of AI technology composition patterns are discussed and exemplary settings for employment of the proposed method are evaluated.