Data Analytics in Manufacturing

  • M. Sami SivriEmail author
  • Basar Oztaysi
Part of the Springer Series in Advanced Manufacturing book series (SSAM)


Development of technology has emerged a new concept, Industry 4.0. It has come with two technological improvements, Cyber-Physical System (CPS) and Internet of Things (IoT) that drive manufacturing companies to Data Analytics by generating the huge amount data. In terms of Industry 4.0, data analytics focus on “what will happen” rather than “what has happened”. These problems are entitled as predictive analytics and aims at building models for forecasting future possibilities or unknown events. The aim of this paper is to give insight about these techniques, provide applications from the literature and show a real world case study from a manufacturing company.



This work is supported by Scientific and Technological Research Council of Turkey (TUBİTAK), TEYDEB 1507, Grant No: 7141451.


  1. Abbott D (2014) Chapter 8—Predictive modeling. Applied predictive analytics: principles and techniques for the professional data analyst. Wiley, Hoboken, pp 213–281Google Scholar
  2. Anicic O, Jović S, Skrijelj H, Nedić B (2017) Prediction of laser cutting heat affected zone by extreme learning machine. Opt Lasers Eng 88:1–4CrossRefGoogle Scholar
  3. Delen D, Demirkan H (2013) Data, information and analytics as services. Decis Support Syst 55(1):359–363CrossRefGoogle Scholar
  4. Esmaeilian B, Behdad S, Wang B (2016) The evolution and future of manufacturing: a review. J Manuf Syst 39:79–100CrossRefGoogle Scholar
  5. Hegde C, Gray KE (2017) Use of machine learning and data analytics to increase drilling efficiency for nearby wells. J Nat Gas Sci Eng 40:327–335CrossRefGoogle Scholar
  6. Hyndman R (2014) Chapter 8—Forecasting performance evaluation and reporting. Business forecasting: practical problems and solutions. SAS Institute Inc., pp 177–184Google Scholar
  7. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22:679–688CrossRefGoogle Scholar
  8. Jain VK (2017). Chapter 1—Overview of big data. Big data and Hadoop. Khanna Book Publishing Co LtdGoogle Scholar
  9. Kang S, Kang P (2017) An intelligent virtual metrology system with adaptive update for semiconductor manufacturing. J Process Control 52:66–74CrossRefGoogle Scholar
  10. Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268MathSciNetzbMATHGoogle Scholar
  11. König R (2009) Predictive techniques and methods for decision support in situations with poor data quality. University of Boras, School of Business and Informatics, University of Skovde, informatics Research Center, University of Orebro, School of Science and Technology. Örebro University, 112 pGoogle Scholar
  12. Lee J, Bagheri B, Kao H-A (2015a) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23CrossRefGoogle Scholar
  13. Lee J, Kao H, Ardakani HD, Siegel D (2015b) Chapter 19—Intelligent factory agents with predictive analytics for asset management. In: Industrial agents. Elsevier Inc., pp 341–360Google Scholar
  14. Lee J, Kao H-A, Yang S (2014) Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16:3–8CrossRefGoogle Scholar
  15. Li X, Lim BS, Zhou JH, Huang S, Phua SJ, Shaw KC, Er MJ (2009) Fuzzy neural network modelling for tool wear estimation in dry milling operation. In: Annual conference of the prognostics and heath management society, pp 1–11Google Scholar
  16. Lieber D, Stolpe M, Konrad B, Deuse J, Morik K (2013) Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. Procedia CIRP 7:193–198CrossRefGoogle Scholar
  17. Loyer J-L, Henriques E, Fontul M, Wiseall S (2016) Comparison of machine learning methods applied to the estimation of manufacturing cost of jet engine components. Int J Prod Econ 178:109–119CrossRefGoogle Scholar
  18. Mehta P, Butkewitsch-choze S, Seaman C (in press) Data analytics framework for semi-continuous manufacturing process—implementation vision with a use case. J Manuf SystGoogle Scholar
  19. Melhem M, Ananou B, Ouladsine M, Pinaton J (2016) Regression methods for predicting the product’s quality in the semiconductor manufacturing process. IFAC-Papers OnLine 49(12):83–88CrossRefGoogle Scholar
  20. Qin J, Liu Y, Grosvenor R (2016) A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP 52:173–178CrossRefGoogle Scholar
  21. Shalev-Shwartz S, Ben-David S (2014) Chapter 20—Neural networks. Understanding machine learning : from theory to algorithms. Cambridge University Press, Cambridge, pp 269–282Google Scholar
  22. Shin S-J, Woo J, Rachuri S (2014) Predictive analytics model for power consumption in manufacturing. Procedia CIRP 15:153–158CrossRefGoogle Scholar
  23. Soltanpoor R, Sellis T (2016) Prescriptive analytics for big data. In: Cheema MA, Zhang W, Chang L (eds) Databases theory and applications. Paper presented at the 27th Australasian Database Conference: ADC 2016. Springer International Publishing, Sydney, NSW, pp 245–256Google Scholar
  24. Wu D, Liu S, Zhang L, Terpenny J, Gao RX, Kurfess T, Guzzo JA (2017) A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. J Manuf Syst 43:25–34CrossRefGoogle Scholar
  25. Zhang H, Kang Y, Zhu Y, Zhao K, Liang J, Ding L (2017) Toxicology in Vitro Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity. Toxicol In Vitro 41:56–63CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2018

Authors and Affiliations

  1. 1.Istanbul Technical UniversityIstanbulTurkey

Personalised recommendations