A customer requirements analysis method of considering product scenarios for improving product design

With increasing concerns on customer requirements (CRs) in product improvement, the research of integrating product scenarios into CRs analysis has attracted the attention of designers. Importance-performance-analysis (IPA), which can be used to represent the relationship between CR importance and product performance, is a well-known model for analyzing CRs. However, a major challenge of the IPA model is the quantification of the importance of CRs accurately. Network analysis (NA) is an appropriate method to understand and calculate the importance of CRs in different usage scenarios. This research presents a Network Analysis-Importance Performance Analysis (NA-IPA) model that integrates NA with the IPA model to improve product design through mapping CRs to configuration plans. Accordingly, two mechanisms are integrated to support product design improvement, namely, (1) the classifier of the NA-IPA model is used to categorise CRs, and (2) the performance indicator is introduced as an evaluation metric of product configuration plans. A case study on product improvement was carried out for the interior space of automotive, to validate the feasibility and advantages of the proposed method. This proposed NA-IPA model provides designers with an efficient method to improve product design and customer satisfaction in response to market competition.

[1]  F. Forcellini,et al.  Innovation level in set-based design: an integrated approach with chosen-to-fit and custom-to-fit solutions , 2022, Journal of Engineering Design.

[2]  Phyo Htet Hein,et al.  Reasoning support for predicting requirement change volatility using complex network metrics , 2022, Journal of Engineering Design.

[3]  Lei Wang,et al.  An analysis method of dynamic requirement change in product design , 2022, Comput. Ind. Eng..

[4]  Zhenni Wu,et al.  A complex network-based response method for changes in customer requirements for design processes of complex mechanical products , 2022, Expert Syst. Appl..

[5]  Ismail M. Ali,et al.  Dynamic Modeling for Product Family Evolution Combined with Artificial Neural Network Based Forecasting Model: A Study of iPhone Evolution , 2021, Technological Forecasting and Social Change.

[6]  Harrison M. Kim,et al.  Approach for Importance–Performance Analysis of Product Attributes From Online Reviews , 2021 .

[7]  Daming Shi,et al.  Multi-objective evolutionary clustering with complex networks , 2021, Expert Syst. Appl..

[8]  Ankit Thakkar,et al.  Predicting stock trend using an integrated term frequency-inverse document frequency-based feature weight matrix with neural networks , 2020, Appl. Soft Comput..

[9]  Bo Rong,et al.  Dynamical mining of ever-changing user requirements: A product design and improvement perspective , 2020, Adv. Eng. Informatics.

[10]  A. V. Barenji,et al.  A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics , 2020, Int. J. Prod. Res..

[11]  Masahiro Ikeda,et al.  Hypergraph Clustering Based on PageRank , 2020, KDD.

[12]  Qiang Zhang,et al.  Integrating customer requirements into customized product configuration design based on Kano’s model , 2020, J. Intell. Manuf..

[13]  A. Janssens,et al.  Reflection on modern methods: Revisiting the area under the ROC Curve. , 2020, International journal of epidemiology.

[14]  Michel Aldanondo,et al.  Optimisation of the concurrent product and process configuration: an approach to reduce computation time with an experimental evaluation , 2019, Int. J. Prod. Res..

[15]  Laura Serviere Munoz,et al.  Siri, Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications , 2019, Journal of Marketing Management.

[16]  Emilie Poirson,et al.  Mining Changes in User Expectation Over Time From Online Reviews , 2019, Journal of Mechanical Design.

[17]  Zhanglin Peng,et al.  Integrating customer requirements into customized product configuration design based on Kano’s model , 2019, Journal of Intelligent Manufacturing.

[18]  Patrícia Augustin Jaques,et al.  An Analysis of Hierarchical Text Classification Using Word Embeddings , 2018, Inf. Sci..

[19]  Danni Chang,et al.  A product affective properties identification approach based on web mining in a crowdsourcing environment , 2018 .

[20]  Wei Chen,et al.  Predicting product co-consideration and market competitions for technology-driven product design: a network-based approach , 2018, Design Science.

[21]  Norsaremah Salleh,et al.  Development of scenario management and requirements tool (SMaRT):Towards supporting scenario-based requirements engineering methodology , 2018 .

[22]  Jianer Chen,et al.  Meta-metric for saliency detection evaluation metrics based on application preference , 2018, Multimedia Tools and Applications.

[23]  H. Chong,et al.  Critical Review of Social Network Analysis Applications in Complex Project Management , 2018 .

[24]  Naomi S. Altman,et al.  Points of Significance: Principal component analysis , 2017, Nature Methods.

[25]  Enrico Vezzetti,et al.  Kano qualitative vs quantitative approaches: An assessment framework for products attributes analysis , 2017, Comput. Ind..

[26]  Jia Hao,et al.  A quantitative approach to design alternative evaluation based on data-driven performance prediction , 2017, Adv. Eng. Informatics.

[27]  Francesco Leali,et al.  A review on decision-making methods in engineering design for the automotive industry , 2017 .

[28]  Wei Chen,et al.  Modeling customer preferences using multidimensional network analysis in engineering design , 2016, Design Science.

[29]  Runliang Dou,et al.  Customer-oriented product collaborative customization based on design iteration for tablet personal computer configuration , 2016, Comput. Ind. Eng..

[30]  Gary B. Wills,et al.  Importance-Performance Analysis based SWOT analysis , 2016, Int. J. Inf. Manag..

[31]  Azucena Gracia,et al.  Consumers' willingness-to-pay for sustainable food products: the case of organically and locally grown almonds in Spain , 2016 .

[32]  Hu-Chen Liu,et al.  A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis , 2015, Qual. Reliab. Eng. Int..

[33]  Ivan Sever,et al.  Importance-performance analysis: A valid management tool? , 2015 .

[34]  Ahmad A. Kardan,et al.  A novel method for expert finding in online communities based on concept map and PageRank , 2015, Human-centric Computing and Information Sciences.

[35]  Takaya Saito,et al.  The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.

[36]  Jian Jin,et al.  Prioritising engineering characteristics based on customer online reviews for quality function deployment , 2014 .

[37]  Shahidan M. Abdullah,et al.  An overview of principal component analysis , 2013 .

[38]  Bernard Yannou,et al.  Set-based design by simulation of usage scenario coverage , 2013 .

[39]  Nitesh V. Chawla,et al.  Market basket analysis with networks , 2011, Social Network Analysis and Mining.

[40]  R. Belk.  An Exploratory Assessment of Situational Effects in Buyer Behavior , 1974 .

[41]  Isnaini Nurul Khasanah.  Sentiment Classification Using fastText Embedding and Deep Learning Model , 2021, ACLING.

[42]  G. Parnell,et al.  Set-Based Design , 2020 .

[43]  Guangdong Tian,et al.  Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method , 2018 .

[44]  Ronald M. Summers,et al.  Optimizing area under the ROC curve using semi-supervised learning , 2015, Pattern Recognit..

[45]  Xi Yang,et al.  An analytical Kano model for customer need analysis , 2009 .