Purpose

The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.

Design/methodology/approach

This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.

Findings

The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.

Originality/value

Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

中文翻译：

---

加强在线产品评论分析以支持产品改进：集成文本挖掘与质量功能部署

目的

本文的目的是开发一种有效的方法来支持和指导利用在线产品评论的生产改进过程。

设计/方法/途径

本文结合了两种方法：(1) 自然语言处理 (NLP) 支持高级文本挖掘，以提高从产品评论中提取信息的准确性；(2) 质量功能部署 (QFD)，利用提取的信息指导产品改进过程。

发现

本文提出了一种方法，通过对可用的在线产品评论执行文本挖掘，同时考虑评论时间和评论有用性等关键因素，来自动化获取客户意见 (VOC) 的过程。本文增强了组织中的质量管理流程，并推进了有关以客户为导向的产品改进流程的文献。

原创性/价值

在线产品评论是公司获取真实 VOC 的宝贵信息来源。VOC 随后被公司普遍用作 QFD 的主要输入，以加强质量管理和产品改进。然而，这个过程需要相当长的时间，在此期间 VOC 可能会发生变化，这可能会对 QFD 的输出产生负面影响。本文通过提供一种改进的方法来应对这一挑战。