Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

产品开发公司以工程变更请求的形式收集数据，以记录设计问题，测试和产品迭代。这些文档包含大量非结构化数据（例如，自由文本）。先前的研究证实，产品开发人员发现当前的IT系统缺乏准确检索具有非结构化数据的相关文档的功能。在这项研究中，我们演示了一种使用自然语言处理和文档聚类算法的方法，可以从包含“工程变更请求”文档的数据库中查找结构或上下文相关的文档。目的是通过利用自然语言处理算法，从根本上减少有效搜索相关工程文档，组织搜索结果以及从这些文档创建带标签的簇所需的时间。