A Framework Based on Natural Language Processing for Risk Management in Engineering

Risk management (RM) is crucial in product development processes in the engineering domain since mitigating risks ensures satisfactory product performance. Existing RM approaches in engineering require numerical inputs converted from textual data, which are manually collected from risk reports and converted into numerical inputs by human risk experts via their experiences. The manual process of doing so is laborious. Since natural language processing (NLP) techniques can process textual data in a similar way that humans comprehend textual data, NLP techniques can potentially automate the process of obtaining numerical inputs from textual data. Therefore, we experimented with multiple NLP techniques to automate the process of collecting numerical data from risk reports that serve as the inputs to RM approaches. Our method performed risk identification and analysis, during which textual data from risk reports were converted into numerical data via NLP techniques like generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). Parts of risk identification and analysis were successfully performed, but some results are not accurate due to NLP techniques not being able to understand causal relationships.