Improvement to quality function deployment methodology : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Applied Statistics at Massey University, Palmerston North, New Zealand

Abstract

Content removed from thesis due to copyright restrictions: Iqbal, Z., Grigg, N. P., Govindaraju, K., & Campbell-Allen, N. M. (2016). A distance-based methodology for increased extraction of information from the roof matrices in QFD studies. International Journal of Production Research, 54(11), 3277-3293. doi:10.1080/00207543.2015.1094585 Iqbal, Z., Grigg, N. P., & Govindaraju. (2017). Performing competitive analysis in QFD studies using multipole moments and bootstrap sampling. Quality Engineering, 29(2), 311-321. doi:10.1080/08982112.2016.1181181 Iqbal, Z., Grigg, N. P., Govindaraju, K., & Campbell-Allen, N. (2014). Statistical comparison of final weight scores in quality function deployment (QFD) studies. International Journal of Quality & Reliability Management, 31(2), 184-204. doi:10.1108/IJQRM-06-2013-0092 Iqbal, Z., Grigg, N. P., Govindaraju, K., & Campbell-Allen, N. M. (2015). Enhancing prioritisation of technical attributes in quality function deployment, 64(3), 398-415. doi.10.1108/IJPPM-10-2014-0156Quality Function Deployment (QFD) is a quality improvement methodology which translates true customer requirements into technical solutions. The major purposes in implementing QFD are enhancing quality, decreasing production cycle time, and lowering costs. QFD methodology utilises a system of matrix like structures known as the House of Quality (HOQ) which work collectively to determine final weightings of the technical characteristics. The derivation of final weights of the technical characteristics and their prioritisation is the final key in QFD process. One of the main theoretical difficulties in employing QFD is that it deals with multidimensional categorical (ordinal) data variables. The rating data of these categorical variables varies from person to person and case study to case study. In prioritising the technical characteristics, QFD practitioners often fail to fully integrate the diverse information extractable from ordinal data and ignore some sections of QFD, House of Quality (HOQ). It is also observed that in each matrix of QFD-HOQ, numerous heuristics have been introduced to suppress the variation, uncertainty and vagueness. During the QFD process, any mistakes such as selection and interpretation of rating scales, application of methods, or integration of various matrices can fail the whole process. In this project with the rationale to improve QFD methodology, a systematic emphasis is placed on the following issues i) Application of methods, procedures, techniques for the appropriate selection of likert scales within each matrix of QFD-HOQ. ii) Application to each matrix, data and their integration towards statistically valid conclusions. iii) Close observation and interpretation of the final prioritisation of technical characteristics (TCs), and its enhancement