Weighing Customer and Technical Requirements in the House of Quality
Weighing Customer and Technical Requirements in the House of Quality
The House of Quality (HoQ) is the primary tool of Quality Function Deployment (QFD), which is a management approach for developing customer-oriented products. The HoQ starts by identifying the customers’ requirements (CRs) for a given product, their weights, and the technical requirements (TRs) that contribute to product improvement. The CRs’ weights and the intensities of the relationships between CRs and TRs identified for the relationship matrix are used to weight the TRs. The main objective of this paper is to present a new approach to weight TRs, which are essential parameters in product design. The TRs’ weights result from the aggregation of the associated CRs’ weights and the corresponding relationship matrix parameters that have been subject to several misunderstandings. Namely, in weighting CRs and measuring the intensities between CRs and TRs ineffective analytical procedures or improper measurement scales are applied. To deal with these deficiencies, we propose the MACBETH method to weigh CRs, according to specific improvements in their performance ranges, and the concept of swing for the relationship matrix that indicates how a defined improvement in the performance of a TR impacts the performance of a CR. We describe the new approach with an illustrative example of a computer server.
Customer requirements, House of Quality, technical requirements, weighting
João Carlos Lourenço has received a PhD in Industrial Engineering and Management from Instituto Superior Técnico (IST), Universidade de Lisboa, Portugal. He is a researcher at the Center for Management Studies of IST (CEG-IST). His research interests are related to decision analysis, portfolio resource allocation, decision support systems, and business analytics.
Isabel M. João has received a PhD in Industrial Engineering and Management from the Instituto Superior Técnico (IST), Universidade de Lisboa, Portugal. She is currently Coordinator of the Quality and Environmental Engineering Master Course at Instituto Superior de Engenharia de Lisboa (ISEL) and researcher at the Center for Management Studies of IST (CEG-IST). Her research interests are in the areas of Quality Management, Process Management and Product and Process Design and Improvement.
Sanaz Azarnoosh is a PhD student at Instituto Superior Técnico, Universidade de Lisboa, Portugal. Her research interests are in the areas of product design, House of Quality and portfolio analysis.
Bana e Costa, C. A., De Corte, J.-M., & Vansnick, J.-C. (2012). MACBETH. International Journal of Information Technology & Decision Making, 11(2), 359–387. https://doi.org/10.1142/S0219622012400068
Burke, E., Kloeber, J., & Deckro, R. (2007). Using and abusing QFD scores. Quality Engineering, 15(1), 9–21. https://doi.org/10.1081/QEN-120006707
Chen, C.-C., & Chuan, M.-C. (2010). An extended Kano model for deciding improvement priority of product attributes. The 40th International Conference on Computers & Indutrial Engineering, 1–6. https://doi.org/10.1109/ICCIE.2010.5668252
Hauser, J. (1988). The House of Quality. Harvard Business Review, 63–73.
Hauser, J., & Clausing, D. (1988). The House of Quality. Harvard Business Review, 10(3), 63–73.
Lee, Y.-T., Wu, W.-W., Tzeng, G.-H., Chair, D., & Tzeng, H. (2008). An effective decision-making method using a combined QFD and ANP approach. WSEAS Transactions on Business and Economics, 5(12), 541–551.
Li, Y., Tang, J., Luo, X., & Xu, J. (2009). An integrated method of rough set, Kano’s model and AHP for rating customer requirements’ final importance. Expert Systems with Applications, 36(3), 7045–7053. https://doi.org/10.1016/j.eswa.2008.08.036
Maritan, D. (2015). Practical Manual of Quality Function Deployment. Springer.
Sharma, & Rawani. (2009). Quality function development: a new paradigm for involving customers in product development process. International Journal of Quality and Innovation, 16–36.
Wang, Y.-M., & Chin, K.-S. (2011). Technical importance ratings in fuzzy QFD by integrating fuzzy normalization and fuzzy weighted average. Computers & Mathematics with Applications, 62(11), 4207–4221. https://doi.org/10.1016/j.camwa.2011.10.005