Goal-oriented prioritization of project outputs: a stratified approach

Asadabadi, Mehdi Rajabi

doi:10.1080/09537287.2022.2101030

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…, 2011; Wu et al. , 2005; Asadabadi, 2016, 2017, 2022). This is to say, in many cases, what QFD suggests for planning future improvement processes may already be outdated as the customers may have new desires or an updated prioritised list of needs.…”

Section: Discussionmentioning

confidence: 99%

“…A key problem in the field of quality management is that customers' opinions may change from the time that the information is gathered until the time that QFD is employed to compute a prioritised list of ERs. This shortcoming has been identified frequently by researchers applying QFD in planning improvement processes (see Raharjo et al, 2011;Wu et al, 2005;Asadabadi, 2016Asadabadi, , 2017Asadabadi, , 2022. This is to say, in many cases, what QFD suggests for planning future improvement processes may already be outdated as the customers may have new desires or an updated prioritised list of needs.…”

Section: Discussionmentioning

confidence: 99%

“…The common purpose of applying QFD to the area of production research in the literature is to improve the quality of products or services and achieve a higher level of customer satisfaction (Ramanathan and Yunfeng, 2009). The key input to QFD is VOC or a prioritised list of customer requirements (Asadabadi, 2022). This list is important due to its key role in identifying the priorities of ERs.…”

Section: Product Features (Customer Requirements) In Qfdmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment

Asadabadi

Saberi

Sadghiani

et al. 2022

JEIM

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PurposeThe purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.Design/methodology/approachThis 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.FindingsThe 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/valueOnline 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.

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