Work Orders, Computerized Maintenance Management Systems, and Other Internal Documentation
You may never thought of your company’s internal documentation as a source of data for analysis. However, NLP can help automate the processing of these documents for insights, anomaly detection, and operational efficiency.
Work orders often contain valuable, but unstructured text data that can be analyzed using Natural Language Processing (NLP). What can you learn from work orders?
- Automated Categorization: NLP can help automatically classify or categorize work orders based on the description of the problem or request. This can help route the work order to the right team or personnel, improving efficiency.
- Trend Analysis: By analyzing the language used in work orders, NLP can help identify trends, root causes, or recurring problems, assisting in problem-solving and decision-making.
- Predictive Maintenance: By processing work order data alongside other sources of information, NLP can help detect patterns that predict future maintenance needs, enabling preventive action.
- Anomaly Detection: By understanding the normal language pattern of work orders, NLP can flag unusual or suspicious requests for further investigation.
In the era of Industry 4.0, operating and equipment logs are increasingly being automated and digitized, allowing for real-time monitoring and analysis, predictive maintenance, and more efficient operations.