Research On Customer Requirement Elements Detection Based On Kano Model Integration Method

With the intensification of market competition and the rapid development of emerging technologies,the customer's role has changed dramatically,from passive buyers to value co-creators.Due to the diversified and dynamic characteristics of customer requirements,companies need to adjust market strategies in a timely manner,enhance the quality of relevant customer requirement elements,and ensure customer satisfaction,taking into account the importance and dynamics of customer requirements.The Kano model analyzes the non-linear relationship between customer satisfaction and product or service performance fulfillment level through a two-dimensional model,which can effectively identify and classify customer requirement elements.It has been widely applied in quality management,product research and development,service innovation,and customer requirement mining.However,the traditional Kano model also has some limitations,such as it is easy to ignore the importance of classification results and ignore the dynamics of requirement elements,etc.Based on this,this paper discusses the detection of customer requirement elements based on the Kano model integration method and conducts an empirical study:(1)Research on the determination of the importance of customer requirement elements based on the integration of Kano model and FMEA.Firstly,the classification results of customer requirement elements were determined through the improved Kano classification table,and the classification curve of requirement elements was drawn.Then,the RPN values of various customer requirement elements were calculated and their importance was determined based on FMEA to determine the advantages of customer requirement elements,so as to solve the problem that the traditional Kano model ignores the importance of customer requirement elements.(2)Research on the dynamic prediction of customer requirement element classification results based on Kano model and MCGM(1,1)integration.Firstly,the grey GM(1,1)model was used to dynamically predict the accuracy of the classification of customer requirement elements and the accuracy was checked.Then,using the Markov chain model constructs the state transition matrix of customer requirement elements,and the predicted value was revised to determine the final predicted value of the state of customer requirement elements to solve the problem of dynamic customer requirement elements.(3)Empirical research on customer requirement elements of fresh cold-chain logistics enterprises.Firstly,the data of customer requirement elements were collected through the questionnaire designed with the SERVQUAL scale,and the reliability of the questionnaire was verified through descriptive statistical analysis and reliability analysis.Secondly,the importance of customer requirement elements in fresh cold chain logistics was determined.The empirical study found that the distribution price in the customer requirement elements was higher based on the market price and the self-picking of the community refrigerator,while the importance of booking the next day service was lower.In addition,the classification results of customer requirement elements in fresh cold chain logistics are dynamically predicted.The empirical results show that the low loss of fresh distribution in customer requirement elements has experienced the life cycle of(),and the fresh quality and the whole cold-chain will change from one-dimensional requirement elements to indispensable requirement elements in the next stage.Finally,the paper puts forward the management enlightenment for fresh cold chain logistics enterprises.The above research has certain theoretical significance and practical value for improving the relevant theories and methods of the Kano model and for in-depth detection of customer requirement elements.