Big Data in product lifecycle management

Recently, “Big Data” has attracted not only researchers’ but also manufacturers’ attention along with the development of information technology. In this paper, the concept, characteristics, and applications of “Big Data” are briefly introduced first. Then, the various data involved in the three main phases of product lifecycle management (PLM) (i.e., beginning of life, middle of life, and end of life) are concluded and analyzed. But what is the relationship between these PLM data and the term “Big Data”? Whether the “Big Data” concept and techniques can be employed in manufacturing to enhance the intelligence and efficiency of design, production, and service process, and what are the potential applications? Therefore, in order to answer these questions, the existing applications of “Big Data” in PLM are summarized, and the potential applications of “Big Data” techniques in PLM are investigated and pointed out.

[1]  John W. Payne,et al.  Task complexity and contingent processing in decision making: An information search and protocol analysis☆ , 1976 .

[2]  Huth Ej.  The information explosion. , 1989, Bulletin of the New York Academy of Medicine.

[3]  Madan G. Singh,et al.  TAPS: a knowledge support system for marketing budget sizing, allocation and targeting in retail banking and other industries , 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

[4]  Bruno Lotter,et al.  Manufacturing Assembly Handbook , 1990 .

[5]  Yoji Akao,et al.  Quality Function Deployment : Integrating Customer Requirements into Product Design , 1990 .

[6]  Ricardo Baeza-Yates,et al.  Information Retrieval: Data Structures and Algorithms , 1992 .

[7]  Hans Hagen,et al.  Proceedings of the 8th conference on Visualization '97 , 1997 .

[8]  M. Cox,et al.  Application-controlled demand paging for out-of-core visualization , 1997, Proceedings. Visualization '97 (Cat. No. 97CB36155).

[9]  Michael Cox,et al.  Application-controlled demand paging for out-of-core visualization , 1997 .

[10]  Wynne Hsu,et al.  Current research in the conceptual design of mechanical products , 1998, Comput. Aided Des..

[11]  R J. Kuo,et al.  Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network , 1999, Neural Networks.

[12]  Sang-Jae Song.  Intelligent decision support system for continuous improvement of resource-saving and recycling-conscious manufacturing , 1999, Proceedings First International Symposium on Environmentally Conscious Design and Inverse Manufacturing.

[13]  Hal R. Varian,et al.  Reprint: How Much Information? , 2000 .

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

[15]  Ram D. Sriram,et al.  Design Repositories: Engineering Design's New Knowledge Base , 2000, IEEE Intell. Syst..

[16]  Kenneth C. Laudon,et al.  Essentials of Management Information Systems , 2000 .

[17]  Chang Liu,et al.  Exploring the factors associated with Web site success in the context of electronic commerce , 2000, Inf. Manag..

[18]  Giovani J.C. da Silveira,et al.  Mass customization: Literature review and research directions , 2001 .

[19]  Michael J. Shaw,et al.  Knowledge management and data mining for marketing , 2001, Decis. Support Syst..

[20]  Weiming Shen,et al.  Collaborative conceptual design - state of the art and future trends , 2002, Comput. Aided Des..

[21]  C. Forza,et al.  Managing for variety in the order acquisition and fulfilment process: The contribution of product configuration systems , 2002 .

[22]  V. Strahonja.  Complexity metric of data enquiry functions for public registers and electronic commerce , 2002, ITI 2002. Proceedings of the 24th International Conference on Information Technology Interfaces (IEEE Cat. No.02EX534).

[23]  Thad Starner,et al.  Web Technologies - Thick Clients for Personal Wireless Devices , 2002, Computer.

[24]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[25]  Wilhelm Dangelmaier,et al.  Virtual and augmented reality support for discrete manufacturing system simulation , 2005, Comput. Ind..

[26]  Ashok N. Srivastava,et al.  Data Mining: Concepts, Models, Methods, and Algorithms , 2005, J. Comput. Inf. Sci. Eng..

[27]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[28]  Gary M. Weiss.  Data Mining in Telecommunications , 2005, The Data Mining and Knowledge Discovery Handbook.

[29]  John Stark,et al.  Product Lifecycle Management , 2005 .

[30]  Tomi Dahlberg,et al.  A New Instrument to Measure the Success of IT Outsourcing , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[31]  Bo-Suk Yang,et al.  Development of an e-maintenance system integrating advanced techniques , 2006, Comput. Ind..

[32]  Roger Jianxin Jiao,et al.  Product family design and platform-based product development: a state-of-the-art review , 2007, J. Intell. Manuf..

[33]  Miya Knights.  Web 2.0 , 2007 .

[34]  Amiya R Mohanty,et al.  Estimation of tool wear during CNC milling using neural network-based sensor fusion , 2007 .

[35]  Kees Jan Roodbergen,et al.  Design and control of warehouse order picking: A literature review , 2006, Eur. J. Oper. Res..

[36]  D. R. Salgado,et al.  An approach based on current and sound signals for in-process tool wear monitoring , 2007 .

[37]  Vishal S. Sharma,et al.  Cutting tool wear estimation for turning , 2008, J. Intell. Manuf..

[38]  R. Steinbrook.  Personally controlled online health data--the next big thing in medical care? , 2008, The New England journal of medicine.

[39]  Benoît Iung,et al.  On the concept of e-maintenance: Review and current research , 2008, Reliab. Eng. Syst. Saf..

[40]  Yu-Cheng Lee,et al.  Quality function deployment implementation based on Fuzzy Kano model: An application in PLM system , 2008, Comput. Ind. Eng..

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

[42]  Irina Kļevecka,et al.  Pre-Processing of Input Data of Neural Networks: The Case of Forecasting Telecommunication Network Traffic , 2008 .

[43]  Winston A Hide,et al.  Big data: The future of biocuration , 2008, Nature.

[44]  Hassan Abdalla,et al.  Creative Approaches in Product Design , 2009 .

[45]  Dimitris Kiritsis,et al.  A framework for RFID applications in product lifecycle management , 2009, Int. J. Comput. Integr. Manuf..

[46]  Ray Y. Zhong,et al.  An Extensible Event-Driven Manufacturing Management with Complex Event Processing Approach , 2009 .

[47]  Adavi Balakrishna,et al.  DEVELOPMENT OF A MANUFACTURING DATABASE SYSTEM FOR STEP-NC DATA FROM EXPRESS ENTITIES , 2010 .

[48]  William H. Dutton,et al.  Clouds, big data, and smart assets: Ten tech-enabled business trends to watch , 2010 .

[49]  Rajkumar Buyya,et al.  Time and cost trade-off management for scheduling parallel applications on Utility Grids , 2010, Future Gener. Comput. Syst..

[50]  Rajkumar Roy,et al.  Guest editorial: IJAMT special issue on: product-service systems , 2011 .

[51]  Martin Hilbert,et al.  The World’s Technological Capacity to Store, Communicate, and Compute Information , 2011, Science.

[52]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[53]  D. Boyd,et al.  Six Provocations for Big Data , 2011 .

[54]  Fei Tao,et al.  Cloud manufacturing: a computing and service-oriented manufacturing model , 2011 .

[55]  Panpan Yang,et al.  Knowledge Repository Supported SOA Application in Collaborative MRO Planning , 2012 .

[56]  Saurabh Pal,et al.  Mining Educational Data to Analyze Students' Performance , 2012, ArXiv.

[57]  Joseph M. Hellerstein,et al.  Distributed GraphLab: A Framework for Machine Learning in the Cloud , 2012, Proc. VLDB Endow..

[58]  Martin Hilbert,et al.  Info Capacity| How to Measure the World’s Technological Capacity to Communicate, Store and Compute Information? Part I: Results and Scope , 2012 .

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

[60]  E. Schadt.  The changing privacy landscape in the era of big data , 2012, Molecular systems biology.

[61]  Ian T. Foster,et al.  Software as a service for data scientists , 2012, Commun. ACM.

[62]  Charu C. Aggarwal,et al.  Mining Text Data , 2012 .

[63]  J. Tien.  The next industrial revolution: Integrated services and goods , 2012, Journal of Systems Science and Systems Engineering.

[64]  ChiChung Lin,et al.  Choosing the Best Supplier using the TOPSIS Method and Improving Deteriorated or Defective Inventory with Batch Processing , 2012 .

[65]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

[66]  Mukesh K. Mohania,et al.  Cloud Computing and Big Data Analytics: What Is New from Databases Perspective? , 2012, BDA.

[67]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[68]  James M. Tien,et al.  Big Data: Unleashing information , 2013, 2013 10th International Conference on Service Systems and Service Management.

[69]  V. Marx.  Biology: The big challenges of big data , 2013, Nature.

[70]  S. Fawcett,et al.  Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain , 2013 .

[71]  H. Stanley,et al.  Quantifying Trading Behavior in Financial Markets Using Google Trends , 2013, Scientific Reports.

[72]  Fei Tao,et al.  FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System , 2013, IEEE Transactions on Industrial Informatics.

[73]  Tom Armes,et al.  Using Big Data and predictive machine learning in aerospace test environments , 2013, 2013 IEEE AUTOTESTCON.

[74]  Angelica N. Nieto Lee,et al.  Enhancement of industrial monitoring systems by utilizing context awareness , 2013, 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).

[75]  H. Eugene Stanley,et al.  Quantifying Wikipedia Usage Patterns Before Stock Market Moves , 2013, Scientific Reports.

[76]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration , 2012, Briefings Bioinform..

[77]  T. Murdoch,et al.  The inevitable application of big data to health care. , 2013, JAMA.

[78]  Raymond Gardiner Goss,et al.  Heading towards big data building a better data warehouse for more data, more speed, and more users , 2013, ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference.

[79]  C. F. Jian,et al.  BATCH TASK SCHEDULING-ORIENTED OPTIMIZATION MODELLING AND SIMULATION IN CLOUD MANUFACTURING , 2014 .

[80]  Ying Feng,et al.  CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling , 2014, Appl. Soft Comput..

[81]  Ray Y. Zhong,et al.  A big data cleansing approach for n-dimensional RFID-Cuboids , 2014, Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[82]  Fei Tao,et al.  IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing , 2014, IEEE Transactions on Industrial Informatics.

[83]  Fabrício F. Costa.  Big data in biomedicine. , 2014, Drug discovery today.

[84]  Malte Brettel,et al.  How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective , 2014 .

[85]  Fei Tao,et al.  Internet of Things and BOM-Based Life Cycle Assessment of Energy-Saving and Emission-Reduction of Products , 2014, IEEE Transactions on Industrial Informatics.

[86]  Luis G. Vargas,et al.  Firm-level outsourcing decision making: A balanced scorecard-based analytic network process model , 2014 .

[87]  Fei Tao,et al.  CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System , 2014, IEEE Transactions on Industrial Informatics.

[88]  Qining Wang,et al.  Concept, Principle and Application of Dynamic Configuration for Intelligent Algorithms , 2014, IEEE Systems Journal.

[89]  M. Christopher,et al.  The Supply Chain Becomes the Demand Chain , 2014 .

[90]  Ck Cheng,et al.  The Age of Big Data , 2015 .

[91]  Yongkui Liu,et al.  Manufacturing Service Management in Cloud Manufacturing: Overview and Future Research Directions , 2015 .

[92]  Peter Groves,et al.  The 'big data' revolution in healthcare: Accelerating value and innovation , 2016 .

[93]  Andrew Y. C. Nee,et al.  Advanced manufacturing systems: socialization characteristics and trends , 2015, Journal of Intelligent Manufacturing.