This session presents lessons learned from a real-world manufacturing company that saved over 3,600 hours of manual labor in one year. This was done by applying Spark NLP to automate root cause analysis of service trip reports – using a combination of automated summarization, ngrams, named entity recognition, and sentiment analysis in one scalable NLP pipeline. We’ll cover the end-to-end process from raw documents to communicating root cause of failures by machine type, and how this can be extended into a predictive maintenance model that predicts incident types given a work order problem statement. The initial load of all history included 2,000+ documents that were between 5 and 12 pages long each with multiple tables. Whenever a new service trip report is entered into a SharePoint site, within 10 minutes, the document is processed and available in the Power BI dashboard. Previously, this manufacturer had human beings in a conference room for 3 weeks reading groups of reports. Now reports are processed timely with no human effort and processed accurately. The automation of the service trip reports is in production. Currently, this work is continuing with developing a predictive maintenance model that includes Spark NLP’s word embeddings, sentence embeddings, and deep learning text classification models.