Advertisement

Big Data in product lifecycle management

  • Jingran Li
  • Fei Tao
  • Ying Cheng
  • Liangjin Zhao
ORIGINAL ARTICLE

Abstract

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.

Keywords

Big Data Manufacturing Product lifecycle management Potential application 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Crawford K (2011) Six provocations for big data. Oxford Internet Institute’s. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431
  2. 2.
    Howe D, Costanzo M, Fey P, Gojobori T, Hannick L, Hide W, Rhee SY (2008) Big Data: the future of biocuration. Nature 455(7209):47–50CrossRefGoogle Scholar
  3. 3.
    Larose DT (2014) Discovering knowledge in data: an introduction to data mining[M]. John Wiley & Sons, New York, pp 240Google Scholar
  4. 4.
    Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms. John Wiley & Sons, New York, pp 552CrossRefGoogle Scholar
  5. 5.
    Low Y, Bickson D, Gonzalez J, Guestrin C, Kyrola A, Hellerstein JM (2012) Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proc VLDB Endowment 5(8):716–727CrossRefGoogle Scholar
  6. 6.
    Aggarwal C C, Zhai C (2012). Mining text data. Springer Science & Business Media, USA, pp 524Google Scholar
  7. 7.
    Baradwaj B K., Pal S (2012). Mining educational data to analyze students’ performance. arXiv preprint arXiv:1201.3417.Google Scholar
  8. 8.
    Manyika J., Chui M., Brown B., Bughin J., Dobbs R., Roxburgh C., Byers A. H. (2011). Big data: the next frontier for innovation, competition and productivity. Technical report, McKinsey Global Institute 5(33):222, http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation
  9. 9.
    Goss R G, Veeramuthu K. (2013) Heading towards “Big Data” building a better data warehouse for more data, more speed, and more users. Advanced Semiconductor Manufacturing Conference (ASMC) 24th Annual SEMI. 220–225.Google Scholar
  10. 10.
    Jian CF, Wang Y (2014) Batch task scheduling-oriented optimization modelling and simulation in cloud manufacturing. Int J Simul Model 13(1):93–101CrossRefGoogle Scholar
  11. 11.
    Garg SK, Buyya R, Siegel HJ (2010) Time and cost trade-off management for scheduling parallel applications on utility grids. Futur Gener Comput Syst 26(8):1344–1355CrossRefGoogle Scholar
  12. 12.
    Laudon KC, Laudon JP (2011) Essentials of management information systems. Pearson, Upper Saddle RiverGoogle Scholar
  13. 13.
    Waller MA, Fawcett SE (2013) Click here for a data scientist: “Big Data”, predictive analytics, and theory development in the Era of a maker movement supply chain. J Bus Logist 34(4):249–252CrossRefGoogle Scholar
  14. 14.
    Christopher M, Ryals LJ (2014) The supply chain becomes the demand chain. J Bus Logist 35(1):29–35CrossRefGoogle Scholar
  15. 15.
    Da Silveira G, Borenstein D, Fogliatto FS (2001) Mass customization: literature review and research directions. Int J Prod Econ 72(1):1–13CrossRefGoogle Scholar
  16. 16.
    Tien JM (2012) The next industrial revolution: integrated services and goods. J Syst Sci Syst Eng 21(3):257–296CrossRefGoogle Scholar
  17. 17.
    Tien JM (2013) “Big Data”: unleashing information. J Syst Sci Syst Eng 22(2):127–151MathSciNetCrossRefGoogle Scholar
  18. 18.
    Tao F, Cheng Y, Zhang L, Nee A Y C (2015) Advanced manufacturing systems: socialization characteristics and trends, Journal of Intelligent Manufacturing, DOI:  10.1007/s10845-015-1042-8, (in Press)
  19. 19.
    Tao F, Zhang L, Venkatesh VC, Luo Y, Cheng Y (2012) Cloud manufacturing: a computing and service-oriented manufacturing model. Proc Inst Mech Eng B J Eng Manuf 225(10):1969–1976CrossRefGoogle Scholar
  20. 20.
    Babu KS, Rao DDN, Balakrishna A, Rao CS (2010) Development of a manufacturing database system for STEP-NC data from express entities. Int J Eng Sci Technol 2(11):6819–6828Google Scholar
  21. 21.
  22. 22.
    Russom P (2011) Big data analytics. TDWI Best Practices Report, Fourth Quarter http://public.dhe.ibm.com/common/ssi/ecm/en/iml14293usen/IML14293USEN.PDF
  23. 23.
    Huth EJ (1989) The information explosion. Bull N Y Acad Med 65(6):647Google Scholar
  24. 24.
    Manabe T, Matsuura J, Murakami O, Matsuura J (1994) Information collecting and/or service furnishing systems by which a user can request information from a central data base using a portable personal terminal and an access terminal. U.S. Patent 5,339,239Google Scholar
  25. 25.
    Frakes WB, Baeza Yates R (1992) Information retrieval: data structures and algorithms. Prentice Hall, Englewood Cliffs, pp 464Google Scholar
  26. 26.
    Payne JW (1976) Task complexity and contingent processing in decision making: an information search and protocol analysis. Organ Behav Hum Perform 16(2):366–387CrossRefGoogle Scholar
  27. 27.
    Cox M, Ellsworth D (1997) Application-controlled demand paging for out-of-core visualization. Proceedings of the 8th conference on Visualization 97. IEEE Computer Society Press, USA, 235-ffGoogle Scholar
  28. 28.
    Power DJ (2007) A brief history of decision support systems. World Wide Web. http://DSSResources.COM/history/dsshistory
  29. 29.
    Chen H, Chiang RHL, Storey VC (2012) Business intelligence and analytics: from Big Data to Big Impact. MIS Q 36(4):1165–1188Google Scholar
  30. 30.
    Lyman P, Varian HR (2000) Reprint: how much information? J Electron Publ 6(2) DOI:  10.3998/3336451.0006.204
  31. 31.
    Laney D (2001) 3D data management: controlling data volume, velocity and variety. META Group Research Note. Retrieved from http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
  32. 32.
    Hilbert M, López P (2011) The world’s technological capacity to store, communicate, and compute information. Science 332(6025):60–65CrossRefGoogle Scholar
  33. 33.
    Knights, M. I. Y. A. (2007). Web 2.0 [web technologies]. Communications Engineer, 5(1):30–35Google Scholar
  34. 34.
    Gantz JF (2007) The expanding digital universe: a forecast of worldwide information growth through 2010. IDCGoogle Scholar
  35. 35.
    Bryant R, Katz RH, Lazowska ED (2008) Big-Data computing: creating revolutionary breakthroughs cn Commerce, science and society. http://www.datascienceassn.org/sites/default/files/Big%20Data%20Computing%202008%20Paper.pdf
  36. 36.
    Gupta R, Gupta H, Mohania M (2012) Cloud computing and “Big Data” analytics: what is new from databases perspective? “Big Data” analytics. Springer, Berlin, pp 42–61Google Scholar
  37. 37.
    M. Graen (1999) Technology in manufacturer/retailer integration: Wal-Mart and Procter & Gamble. Private communicationGoogle Scholar
  38. 38.
    Shaw MJ, Subramaniam C, Tan GW, Welge ME (2001) Knowledge management and data mining for marketing. Decis Support Syst 31(1):127–137CrossRefGoogle Scholar
  39. 39.
    Liu C, Arnett KP (2000) Exploring the factors associated with Web site success in the context of electronic commerce. Inform Manag 38(1):23–33CrossRefGoogle Scholar
  40. 40.
    Strahonja V (2002) Complexity metric of data enquiry functions for public registers and electronic commerce. Inf Technol Interfaces :63–68Google Scholar
  41. 41.
    Wei FF (2013) ECL Hadoop: “Big Data” processing based on Hadoop strategy in effective e-commerce logistics. Comput Eng Sci 35(10):65–71Google Scholar
  42. 42.
    Preis T, Moat HS, Stanley HE (2013) Quantifying trading behavior in financial markets using Google Trends. Sci Rep 3:1684Google Scholar
  43. 43.
    Moat HS, Curme C, Avakian A, Kenett DY, Stanley HE, Preis T (2013) Quantifying Wikipedia usage patterns before stock market moves. Sci Rep 111(32):11600–11605Google Scholar
  44. 44.
    Fuhrer E (2000) System for enhanced financial trading support: U.S. Patent 6,105,005[P]Google Scholar
  45. 45.
    Bughin J, Chui M, Manyika J (2010) Clouds, “Big Data”, and smart assets: ten tech-enabled business trends to watch. McKinsey Q 56(1):75–86Google Scholar
  46. 46.
    Murdoch TB, Detsky AS (2013) The inevitable application of “Big Data” to health care [J]. JAMA 309(13):1351–1352CrossRefGoogle Scholar
  47. 47.
    Steinbrook R (2008) Personally controlled online health data-the next big thing in medical care. N Engl J Med 358(16):1653CrossRefGoogle Scholar
  48. 48.
    Groves P, Kayyali B, Knott D, Van Kuiken S (2013) The “Big Data” revolution in healthcare. McKinsey Q http://www.pharmatalents.es/assets/files/Big_Data_Revolution.pdf
  49. 49.
    Weiss GM (2005) Data mining in telecommunications. Data mining and knowledge discovery handbook. Springer, US, pp 1189–1201CrossRefGoogle Scholar
  50. 50.
    Kļevecka I, Lelis J (2008) Pre-processing of input data of neural networks: the case of forecasting telecommunication network traffic. Riga Tech Univ 104:168–178Google Scholar
  51. 51.
    Stark J (2011) Product lifecycle management. Springer, LondonCrossRefGoogle Scholar
  52. 52.
    Jun HB, Shin JH, Kim YS, Kiritsis D, Xirouchakis P (2009) A framework for RFID applications in product lifecycle management. Int J Comput Integr Manuf 22(7):595–615CrossRefGoogle Scholar
  53. 53.
    Shehab E, Roy R (2011) Guest editorial: IJAMT special issue on: product-service systems. Int J Adv Manuf Technol 52(9):1115–1116CrossRefGoogle Scholar
  54. 54.
    Tao F, Cheng Y, Xu L, Zhang L, Li B (2014) CCIoT-CMfg: cloud computing and internet of things based cloud manufacturing service system. IEEE Trans Ind Inf 10(2):1435–1442CrossRefGoogle Scholar
  55. 55.
    Tao F, Laili YJ, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans Ind Inf 9(4):2023–2033CrossRefGoogle Scholar
  56. 56.
    Tao F, Zhang L, Liu Y, Cheng Y, Wang LH, Xun X (2015) Manufacturing service management in cloud manufacturing: overview and future research directions. J Manuf Sci Eng Trans ASME (In Press)Google Scholar
  57. 57.
    Singh Madan G, Bennavail JC (1989) TAPS: a knowledge support system for marketing budget sizing, allocation and targeting in retail banking and other industries Systems. Man Cybern 1:119–124Google Scholar
  58. 58.
    Hsu W, Woon IMY (1998) Current research in the conceptual design of mechanical products. Comput Aided Des 30(5):377–389CrossRefGoogle Scholar
  59. 59.
    Loiter B (1986) Manufacturing assembly handbook. Butterworths, BostonGoogle Scholar
  60. 60.
    Akao Y, King B (1990). Quality function deployment: integrating customer requirements into product design vol 21. Cambridge, MA: Productivity PressGoogle Scholar
  61. 61.
    Lee YC, Sheu LC, Tsou YG (2008) Quality function deployment implementation based on Fuzzy Kano model: an application in PLM system. Comput Ind Eng 55(1):48–63CrossRefGoogle Scholar
  62. 62.
    Wang L, Shen W, Xie H, Neelamkavil J, Pardasani A (2002) Collaborative conceptual design—state of the art and future trends. Comput Aided Des 34(13):981–996CrossRefGoogle Scholar
  63. 63.
    Jiao JR, Simpson TW, Siddique Z (2007) Product family design and platform-based product development: a state-of-the-art review. J Intell Manuf 18(1):5–29CrossRefGoogle Scholar
  64. 64.
    Caldwell NHM, Clarkson PJ, Rodgers PA, Huxor AP (2000) Web-based knowledge management for distributed design. Intell Syst Appl 15(3):40–47CrossRefGoogle Scholar
  65. 65.
    Abdalla HS, Salah F (2009) Creative approaches in product design. Proceedings of the 19th CIRP Design Conference–Competitive Design http://hdl.handle.net/1826/3720
  66. 66.
    Szykman S, Sriram RD, Bochenek C, Racz JW, Senfaute J (2000) Design repositories: engineering design’s new knowledge base. IEEE Intell Syst 15(3):48–55 CrossRefGoogle Scholar
  67. 67.
    Lin CC, Su CT (2012) Choosing the best supplier using the TOPSIS Method and improving deteriorated or defective inventory with batch processing. IJACT: Int J Adv Comput Technol 4(23):600–608CrossRefGoogle Scholar
  68. 68.
    Dahlberg T, Nyrhinen M (2006) A new instrument to measure the success of IT outsourcing. System Sciences. Proceedings of the 39th Annual Hawaii International Conference. IEEE 8:200aGoogle Scholar
  69. 69.
    Tjader Y, May JH, Shang J, Vargas LG, Gao N (2014) Firm-level outsourcing decision making: a balanced scorecard-based analytic network process model. Int J Prod Econ 147:614–623CrossRefGoogle Scholar
  70. 70.
    Lee AN, Martinez Lastra JL (2013) Enhancement of industrial monitoring systems by utilizing context awareness. Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2013 IEEE International Multi-Disciplinary Conference On. IEEE, 2013 :277–284Google Scholar
  71. 71.
    Zhang YH, Dai QY, Zhong RY (2009) An extensible event-driven manufacturing management with complex event processing approach. Int J Control Autom 2(3):1–12Google Scholar
  72. 72.
    Zhong RY, Huang GQ, Dai Q (2014) A “Big Data” cleansing approach for n-dimensional RFID-Cuboids. Computer Supported Cooperative Work in Design (CSCWD), Proceedings of the 2014 I.E. 18th International Conference On. IEEE, 2014 :289–294Google Scholar
  73. 73.
    Armes T, Refern M (2013) Using “Big Data” and predictive machine learning in aerospace test environments. AUTOTESTCON IEEE :1–5Google Scholar
  74. 74.
    Kuo RJ, Cohen PH (1999) Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network. Neural Netw 12(2):355–370CrossRefGoogle Scholar
  75. 75.
    Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tools Manuf 47(14):2140–2152CrossRefGoogle Scholar
  76. 76.
    Sharma VS, Sharma SK, Sharma AK (2008) Cutting tool wear estimation for turning. J Intell Manuf 19(1):99–108CrossRefGoogle Scholar
  77. 77.
    Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21(1):466–479CrossRefGoogle Scholar
  78. 78.
    Forza C, Salvador F (2002) Managing for variety in the order acquisition and fulfilment process: The contribution of product configuration systems. Int J Prod Econ 76(1):87–98CrossRefGoogle Scholar
  79. 79.
    Li SG, Kuo X (2008) The inventory management system for automobile spare parts in a central warehouse. Expert Syst Appl 34(2):1144–1153MathSciNetCrossRefGoogle Scholar
  80. 80.
    De Koster R, Le-Duc T, Roodbergen KJ (2007) Design and control of warehouse order picking: a literature review. Eur J Oper Res 182(2):481–501CrossRefzbMATHGoogle Scholar
  81. 81.
    Muller A, Crespo Marquez A, Iung B (2008) On the concept of e-maintenance: review and current research. Reliab Eng Syst Saf 93(8):1165–1187CrossRefGoogle Scholar
  82. 82.
    Ren M, Yang P (2012) Knowledge repository supported SOA application in collaborative MRO planning. Int J Digit Content Technol Appl 5(16)Google Scholar
  83. 83.
    Han T, Yang BS (2006) Development of an e-maintenance system integrating advanced techniques. Comput Ind 57(6):569–580MathSciNetCrossRefGoogle Scholar
  84. 84.
    Dat LQ, Truc Linh DT, Chou SY, Vincent FY (2012) Optimizing reverse logistic costs for recycling end-of-life electrical and electronic products. Expert Syst Appl 39(7):6380–6387CrossRefGoogle Scholar
  85. 85.
    Song SJ (1999) Intelligent decision support system for continuous improvement of resource-saving and recycling-conscious manufacturing. Environmentally Conscious Design and Inverse Manufacturing, 1999. Proceedings. EcoDesign '99: First International Symposium On. IEEE, 1999:723–727Google Scholar
  86. 86.
    Jaspernite J (2014) Was hinter Begriffen wie Industrie 4.0 steckt. Comput Autom No.12, 12:24–28Google Scholar
  87. 87.
    Kagermann H, Wahlster W, Helbig J (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0—final report of the Industrie 4.0 Working Group. Acatech, München, pp 19–26Google Scholar
  88. 88.
    Dangelmaier W, Fischer M, Gausemeier J, Grafe M, Matysczok C, Mueck B (2005) Virtual and augmented reality support for discrete manufacturing system simulation. Comput Ind 56(4):371–383CrossRefGoogle Scholar
  89. 89.
    Brettel M, Friederichsen N, Keller M, Rosenberg M (2014) How virtualization, decentralization and network building change the manufacturing landscape: an Industry 4.0 Perspective. Int J Mech Ind Sci Eng 8(1):37–44Google Scholar
  90. 90.
    Costa FF (2014) Big Data in biomedicine. Drug Discov Today 19(4):433–440CrossRefGoogle Scholar
  91. 91.
    Allen B, Bresnahan J, Childers L, Foster I, Kandaswamy G, Kettimuthu R, Tuecke S (2012) Software as a service for data scientists. Commun ACM 55:81–88CrossRefGoogle Scholar
  92. 92.
    Marx V (2013) The big challenges of big data. Nature 498:255–260CrossRefGoogle Scholar
  93. 93.
    Schadt EE (2012) The changing privacy landscape in the era of Big Data. Mol Syst Biol 8:612CrossRefGoogle Scholar
  94. 94.
    Thorvaldsdóttir H, Robinson JT, Mesirov JP (2012) Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14(2):178–192, bbs017 CrossRefGoogle Scholar
  95. 95.
    Tao F, Zuo Y, Xu L, Lv L, Zhang L (2014) Internet of things and BOM based life cycle assessment of energy-saving and emission-reduction of product. IEEE Trans Ind Inf 10(2):1252–1264CrossRefGoogle Scholar
  96. 96.
    Tao F, Feng Y, Zhang L, Liao TW (2014) CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl Soft Comput 19:264–279CrossRefGoogle Scholar
  97. 97.
    Tao F, Laili YJ, Liu YL, Feng Y, Wang Q, Zhang L, Xu L (2014) Concept, principle and application of configurable intelligent optimization algorithm. IEEE Syst J 8(1):28–42CrossRefGoogle Scholar
  98. 98.
    Tao F, Zuo Y, Xu L, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inf 10(2):1547–1557CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Jingran Li
    • 1
  • Fei Tao
    • 2
  • Ying Cheng
    • 2
  • Liangjin Zhao
    • 3
  1. 1.School of AstronauticsBeihang UniversityBeijingChina
  2. 2.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  3. 3.School of Advanced EngineeringBeihang UniversityBeijingChina

Personalised recommendations