The Study Of Failure Analysis Using Customer Information In Wareable Industry

This thesis is about failure analysis in wearable industry.It analyzes potential failure modes caused by customer factors considering the wearable industrial characteristics.On the basis of this the definition of customer data is proposed and the way of collecting customer data is researched.This thesis uses statistic methods and support vector machine to analyze the relationship between customer data and product failure.At last a failure forecasting platform is developed.The detailed research is presented below:Firstly,the customer information which can cause failures of wearable products is summarized.On the basis of analysis about customer requirements in wearable industry,this thesis summed up some key customer information: body characteristics,temperature,humidity of utility environment,special events and so on.All of these are directions of subsequent research.Secondly,customer data is defined and the ways of data collection is researched.Two ways of data collection is proposed in this thesis.One way is collecting useful information during the refund process,another way is analyzing user behaviors using the data collected by wearable products.Thirdly,methods of failure analysis using customer data is researched.Statistic is used to find the correlation between customer data and failures.Support vector machine is used to classify and forecast failures.At last the ways of data analysis is used in a wearable enterprise.How to improve quality level of product is very important in wearable industry.This thesis proposes failure analysis using customer data and researches main methods in data analysis.An application of these methods in the typical wearable enterprise has verified the efficiency of these methods.It is foreseeable that this will help wearable enterprise to improve the management level of product design and after-sale supply chain management.