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This paper aims to provide a quantitative decision approach to the service quality management, developed on the basis of Kano's theory of attractive quality. The proposed approach aims at exploiting contacts with service made by “mystery guests” rather than traditional surveys on customer opinions.

A specific probabilistic model of the process of serving quality is the adopted basic tool. Multiple comparison tests aimed at controlling the service quality are the core of the proposed decision approach. In order to collect the needed sampling data, a few mystery guests who experience many customer‐service contacts are employed.

A quantitative decision methodology which both allows one to evaluate the actual service quality level and provides, via comparison tests, a tool to highlight the weak and strong points of the service delivery process.

The proposed quality map is an original graphical tool, which enables one to pin‐point strengths and failings in service quality, prioritize corrective actions and recognize improvements, if any. The operative value of the whole methodology is tested through a real application to the hotel service industry.

Higher efficiency and effectiveness of Research & Development phases can be attained using advanced statistical methodologies. In this work statistical methodologies are combined with a deterministic approach to engineering design. In order to show the potentiality of such integration, a simple but effective example is presented. It concerns the problem of optimising the performance of a paper helicopter. The desgin of this simple device is not new in quality engineering literature and has been mainly used for educational purposes. Taking full advantage of fundamental engineering knowledge, an aerodynamic model is originally formulated in order to describe the fligh of the helicopter. Screening experiments were necessary to get first estimates of model parameters. Subsquently, deterministic evaluations based on this model were necessary to set up further experimental phases needed to search for a better design. Thanks to this integration of statistical and deterministic phases, a significant performance improvement is obtained. Moreover, the engineering knowledge turns out to be developed since an explanation of the “why” of better performances, although approximate, is achieved. The final design solution is robust in a broader sense, being both validated by experimental evidence and closely examined by engineering knoweldge.