Dynamic benchmarking methodology for quality function deployment

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PurposeThe purpose of this paper is to provide a methodology to integrate both the dynamics of competitors' performance and the dynamics of customer preference, along with their interaction, into a quality function deployment (QFD) analysis.Design/methodology/approachA systematic dynamic benchmarking methodology is proposed with an illustrative example.FindingsThe analytic hierarchy process's (AHP's) relative measurement might serve as a better way to elicit the customer's judgment over time in the QFD, not only in the importance rating part, but also in the competitive benchmarking part. It is also possible to quantitatively model the AHP priorities' change over time, and incorporate it in the QFD decision‐making process.Research limitations/implicationsIt might take a certain amount of time and efforts to collect the necessary data over time. However, it might be justified considering the improved accuracy of the QFD results. It is also important that the data collection should be carried out in a specific customer segment.Practical implicationsQFD practitioners may find a more systematic method to continually evaluate the current performance, identify areas for improvement, and eventually set goals for the future.Originality/valueThere are two novel approaches used in the methodology. First, it is the use of an exponential smoothing‐based forecasting technique to model the trend of the AHP‐based importance rating values and the competitive benchmarking information. Second, it is a strength‐weakness‐opportunity‐threat‐based competitive weighting scheme, which serves as a more systematic way to substitute the traditional QFD customer competitive target setting and sales point value determination.