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Analysis of Kano‐model‐based customer needs for product development
Author(s) -
Sharif Ullah A. M. M.,
Tamaki Jun'ichi
Publication year - 2011
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.20168
Subject(s) - kano model , voice of the customer , computer science , product (mathematics) , process (computing) , customer intelligence , new product development , customer satisfaction , psychographic , measure (data warehouse) , operations research , customer retention , marketing , data mining , business , mathematics , service quality , service (business) , geometry , operating system
The Kano model is a useful model for integrating the Voice of the Customer into the subsequent processes of product development. This paper presents a method for analyzing customers' preferences obtained by using the Kano model. In the Kano model, customers' preferences are obtained by using a prescribed form to know whether or not a given product attribute is a Must‐be, Attractive, One‐dimensional, Indifferent, or Reverse attribute for a given product. Since the preference may vary a lot from customer to customer due to the demographic and psychographic factors, a quantitative aggregation process is needed to compute the information content of all customer answers and finally to decide the aforementioned status of each product attribute. Most of the quantitative methods developed so far deal mainly with the imprecision in the customer answers and do not address the uncertain (unknown or missing) customer answers. In fact, a significant number of customers might not be able to submit their answers on time. As a result, a significant number of answers remain unknown that manifests a great deal of uncertainty. This paper shows an approach to simulate the unknown customer answers. In addition, an approach is developed to measure the information content of customer answers integrating the real and simulated customer answers. This is helpful for identifying the correct status (Must‐be, Attractive, One‐dimensional, Indifferent, or Reverse) of each product attribute. Moreover, it is shown that the weighted average type of quantitative analysis is not suitable for quantifying the inherent complexity of customer answers obtained in accordance with the Kano model. © 2010 Wiley Periodicals, Inc. Syst Eng 14: 154–172, 2011