基于Kano-AHP-QFD的定制衣柜产品用户模糊需求转化OA

To address the issues of unclear user requirements and sequencing,fuzzy functional design logic,and the inability to fully meet users·individualized demands in function customization during the design and development of customized-wardrobe products,a standardized and quantitative design method is needed.To achieve this and establish a more efficient intelligent design process,a user-requirement acquisition and quantitative analysis method based on Kano-AHP-QFD was utilized.By integrating three theoretical models,a structured approach was developed to quantitatively interpret and translate user demands into functional design parameters.Firstly,user demands were collected by in-home research and user interviews,and were categorized and extracted by the affinity diagram method.Sixteen user demands were derived in three categories including environmental impact,usage experience,and storage of items.Secondly,Kano model was utilized to classify user demands into three categories:charismatic demands,desired demands,and essential demands.The better-worse coefficients were used to determine the out-of-layer weights of the three categories.Thirdly,analytic hierarchy process(AHP)was applied to disassemble the design problem into goal,criterion,and sub-criterion layers,to find out the comprehensive weights of the user demand indicators and obtain the final weights with uniform scale after normalization.Fourthly,quality function deployment(QFD)theory was applied to convert quantified user requirements into product engineering features,which specifically categorized into design features,auxiliary functional features,and storage features.Based on the existing user demand indicators,focus groups were conducted to collect possible product engineering features.The duplicates were merged.The final 32 product engineering features were evaluated by QFD,being confirmed after the group discussion and voting.Six industry experts were invited to participate in the focus group for correlation evaluation.In previous studies,the evaluation granularity of the correlation matrix in QFD was dominated by 3,5,and 7,and some of the studies only considered the positive correlation between different product engineering features and user demand indicators.This study expanded the evaluation of granularity to positive and negative correlation,and introduced the negative correlation results into the correlation analysis to draw a more accurate conclusion.Hesitant fuzzy linguistic information system was integrated to improve the accuracy of expert evaluations.The product engineering features and weight were obtained.Finally,a weighting algorithm was designed to adjust product function weight according to the user-individualized-requirements.Specifically,apart from the"Delete Function"and"Required Functions",which were assigned values of 0 and 1,respectively,all other functions were weighted using a ratio of 6∶4 between the basic weight and user weight.These functions were then comprehensively ranked to determine their respective weights and priority order in the design process.By fully combining user-individualized requirements through scientific and quantitative means,the customized-wardrobes function-design strategy can be made to improve the accuracy of user-demand converting and realize standardized and process-oriented design of customized-wardrobe products.This establishes a foundational framework for the intelligent generation and design of customized wardrobes.