| Summary: | ABSTRACTKnowledge extraction and reuse are critical topics for manufacturing companies willing to strengthen their Product-Service Systems (PSS) offerings. In manufacturing’s maintenance processes, effectiveness and efficiency depend on the ability to learn from past field interventions. Dealing with unstructured descriptions of maintenance activities has prevented manufacturing companies from analyzing them, causing the loss of useful information. Natural Language Processing (NLP) demonstrated high potential, allowing simplified text knowledge extraction and summarization. Besides, the literature presents only a few applications of topic modeling for maintenance improvement in the manufacturing domain. Using a case study, the paper demonstrates the potentialities of NLP adoption to improve not only the maintenance management and execution but also the asset design and management, impacting the whole PSS. In other words, implications will have effects on the operational (e.g. maintenance execution), managerial (e.g. maintenance management), and business levels (e.g. PSS offering definition) of manufacturing firms.
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