TY - JOUR
T1 - Developing Data-Driven QFD
T2 - A Systemic Approach to Employing Product Manuals and Customer Reviews
AU - Park, Gamunnarbi
AU - Kim, Shinho
AU - Geum, Youngjung
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2025
Y1 - 2025
N2 - Despite the importance of data analytics, quality function deployment (QFD) development remains both qualitative and expert-driven. Although some studies have been conducted on data-driven QFD, most have relied solely on quantifying customer requirements, neglecting the quantification of engineering characteristics from a data-driven approach. This study aims to develop a new approach to data-driven QFD using customer reviews and product manuals. Through an in-depth investigation of the product manual structure, this study suggests a systematic method for extracting engineering characteristics and interpreting data-driven QFD. The results are expected to provide practical guidelines for the QFD literature as well as product planning practice by suggesting a systematic framework for developing a data-driven approach and holistic approach. Furthermore, this study aims to ensure a more comprehensive understanding of customer needs and engineering capabilities, thereby enhancing the overall effectiveness of QFD in product development.
AB - Despite the importance of data analytics, quality function deployment (QFD) development remains both qualitative and expert-driven. Although some studies have been conducted on data-driven QFD, most have relied solely on quantifying customer requirements, neglecting the quantification of engineering characteristics from a data-driven approach. This study aims to develop a new approach to data-driven QFD using customer reviews and product manuals. Through an in-depth investigation of the product manual structure, this study suggests a systematic method for extracting engineering characteristics and interpreting data-driven QFD. The results are expected to provide practical guidelines for the QFD literature as well as product planning practice by suggesting a systematic framework for developing a data-driven approach and holistic approach. Furthermore, this study aims to ensure a more comprehensive understanding of customer needs and engineering capabilities, thereby enhancing the overall effectiveness of QFD in product development.
KW - QFD
KW - co-occurrence analysis
KW - customer review
KW - data-driven QFD
KW - engineering characteristics
KW - topic model
UR - http://www.scopus.com/inward/record.url?scp=85216405678&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2025.3532658
DO - 10.1109/ACCESS.2025.3532658
M3 - Article
AN - SCOPUS:85216405678
SN - 2169-3536
VL - 13
SP - 22380
EP - 22394
JO - IEEE Access
JF - IEEE Access
ER -