Data-driven customer requirements discernment in the product lifecycle management via intuitionistic fuzzy sets and electroencephalogram

Large amount of data are collected through the product lifecycle management, and the benefits of big data analytics permeate the entire manufacturing value chain. However, the existing methods pay little attention to the analysis of customer requirements data in the beginning of life period. Thus, a data-driven approach for customer requirements discernment is proposed in this paper. It not only manages the vagueness in the semantic expression level using the intuitionistic fuzzy sets, but also adopts the electroencephalogram data as endogenous neural indicators to handle the vagueness in the neurocognitive level. An experimental research integrated with the Kano model is developed to record the EEG data which inherently interpret customers’ psychological states. Benefit from the data mining method, the effect of customer requirements on psychological response can be investigated using the EEG data. Taking the data of initial requirement importance, performance realization levels and customers’ psychological states into consideration, three novel adjusting models are established to acquire the comprehensive importance of each requirement. A case study is conducted to illustrate the feasibility of the approach proposed in this paper.

[1]  Abdelmajid Benamara,et al.  “disassembly plan approach based on subassembly concept” , 2017 .

[2]  Benoit Montreuil,et al.  Interconnected logistic networks and protocols: simulation-based efficiency assessment , 2014 .

[3]  Xuening Chu,et al.  A new importance-performance analysis approach for customer satisfaction evaluation supporting PSS design , 2012, Expert Syst. Appl..

[4]  Yu-Cheng Lee,et al.  A new fuzzy concept approach for Kano's model , 2009, Expert Syst. Appl..

[5]  Krassimir T. Atanassov,et al.  On Intuitionistic Fuzzy Sets Theory , 2012, Studies in Fuzziness and Soft Computing.

[6]  L. Trainor,et al.  Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions , 2001 .

[7]  N. Kano,et al.  Attractive Quality and Must-Be Quality , 1984 .

[8]  Hui Cheng,et al.  Aided analysis for quality function deployment with an Apriori-based data mining approach , 2010, Int. J. Comput. Integr. Manuf..

[9]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[10]  Zeshui Xu,et al.  Intuitionistic Fuzzy Aggregation Operators , 2007, IEEE Transactions on Fuzzy Systems.

[11]  P. John Clarkson,et al.  Web-Based Knowledge Management for Distributed Design , 2000, IEEE Intell. Syst..

[12]  Arshdeep Bahga,et al.  Analyzing Massive Machine Maintenance Data in a Computing Cloud , 2012, IEEE Transactions on Parallel and Distributed Systems.

[13]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[14]  Shuo-Yan Chou,et al.  Optimizing reverse logistic costs for recycling end-of-life electrical and electronic products , 2012, Expert Syst. Appl..

[15]  Kai Chen,et al.  A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization , 2018, Neurocomputing.

[16]  Fei Tao,et al.  Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system , 2010, Knowledge and Information Systems.

[17]  Hao Luo,et al.  Product whole life-cycle and omni-channels data convergence oriented enterprise networks integration in a sensing environment , 2015, Comput. Ind..

[18]  Ting-Yu Chen,et al.  Determining objective weights with intuitionistic fuzzy entropy measures: A comparative analysis , 2010, Inf. Sci..

[19]  L. Cao.  Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[20]  Rachida Dssouli,et al.  Big Data Pre-processing: A Quality Framework , 2015, 2015 IEEE International Congress on Big Data.

[21]  Steven Saar,et al.  Toward Trash That Thinks: Product Tags for Environmental Management , 2002 .

[22]  Yingfeng Zhang,et al.  A framework for Big Data driven product lifecycle management , 2017 .

[23]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[24]  Yang Xu,et al.  Knowledge Management in Big Data Times , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.

[25]  Chih-Hung Wu,et al.  A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy , 2007, Expert Syst. Appl..

[26]  E. Anderson,et al.  Strengthening the Satisfaction-Profit Chain , 2000 .

[27]  Mohammad Esmalifalak,et al.  A data mining approach for fault diagnosis: An application of anomaly detection algorithm , 2014 .

[28]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[29]  San-yang Liu,et al.  A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection , 2011, Expert Syst. Appl..

[30]  Haluk Demirkan,et al.  The Next Wave of Sustainable IT , 2011, IT Professional.

[31]  Aggelos K. Katsaggelos,et al.  Analysis of High-Dimensional Phase Space via Poincaré Section for Patient-Specific Seizure Detection , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  C. M. Lim,et al.  Characterization of EEG - A comparative study , 2005, Comput. Methods Programs Biomed..

[33]  Leslie Monplaisir,et al.  Modeling of fuzzy-based voice of customer for business decision analytics , 2017, Knowl. Based Syst..

[34]  Bin Song,et al.  Mass production of tooling product families via modular feature-based design to manufacturing collaboration in PLM , 2007, J. Intell. Manuf..

[35]  Zeshui Xu,et al.  Intuitionistic preference relations and their application in group decision making , 2007, Inf. Sci..

[36]  Amin Vafadarnikjoo,et al.  Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain , 2015, Expert Syst. Appl..

[37]  Andrew Kusiak,et al.  Smart manufacturing must embrace big data , 2017, Nature.

[38]  Min Xie,et al.  A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure , 2016, Eng. Appl. Artif. Intell..

[39]  You-Shyang Chen,et al.  Extracting performance rules of suppliers in the manufacturing industry: an empirical study , 2011, Journal of Intelligent Manufacturing.

[40]  Kurt Matzler,et al.  How to make product development projects more successful by integrating Kano's model of customer satisfaction into quality function deployment , 1998 .

[41]  Mahour Mellat-Parast,et al.  Developing a grey-based decision-making model for supplier selection , 2012 .

[42]  H. Semlitsch,et al.  A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. , 1986, Psychophysiology.

[43]  Satoshi Yasuda,et al.  Advanced Semiconductor Manufacturing Using Big Data , 2015, IEEE Transactions on Semiconductor Manufacturing.

[44]  Yongsheng Ma,et al.  Product lifecycle management in aviation maintenance, repair and overhaul , 2008, Comput. Ind..

[45]  Fei Tao,et al.  Big Data in product lifecycle management , 2015, The International Journal of Advanced Manufacturing Technology.

[46]  S. G. Li,et al.  The inventory management system for automobile spare parts in a central warehouse , 2008, Expert Syst. Appl..

[47]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[48]  Athanasios V. Vasilakos,et al.  A Manufacturing Big Data Solution for Active Preventive Maintenance , 2017, IEEE Transactions on Industrial Informatics.

[49]  Humberto Bustince,et al.  Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets , 1996, Fuzzy Sets Syst..