IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i5d10.1007_s10845-020-01711-w.html
   My bibliography  Save this article

Pitfalls and protocols of data science in manufacturing practice

Author

Listed:
  • Chia-Yen Lee

    (National Taiwan University
    National Cheng Kung University)

  • Chen-Fu Chien

    (National Tsing Hua University)

Abstract

Driven by ongoing migration for Industry 4.0, the increasing adoption of artificial intelligence, big data analytics, cloud computing, Internet of Things, and robotics have empowered smart manufacturing and digital transformation. However, increasing applications of machine learning and data science (DS) techniques present a range of procedural issues including those that involved in data, assumptions, methodologies, and applicable conditions. Each of these issues may increase difficulties for implementation in practice, especially associated with the manufacturing characteristics and domain knowledge. However, little research has been done to examine and resolve related issues systematically. Gaps of existing studies can be traced to the lack of a framework within which the pitfalls involved in implementation procedures can be identified and thus appropriate procedures for employing effective methodologies can be suggested. This study aims to develop a five-phase analytics framework that can facilitate the investigation of pitfalls for intelligent manufacturing and suggest protocols to empower practical applications of the DS methodologies from descriptive and predictive analytics to prescriptive and automating analytics in various contexts.

Suggested Citation

  • Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-020-01711-w
    DOI: 10.1007/s10845-020-01711-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01711-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01711-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yun Bai & Zhenzhong Sun & Bo Zeng & Jianyu Long & Lin Li & José Valente Oliveira & Chuan Li, 2019. "A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2245-2256, June.
    2. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    3. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    4. Milton Friedman, 1957. "Introduction to "A Theory of the Consumption Function"," NBER Chapters, in: A Theory of the Consumption Function, pages 1-6, National Bureau of Economic Research, Inc.
    5. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    6. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
    9. Chen-Fu Chien & Chung-Jen Kuo & Chih-Min Yu, 2020. "Tool allocation to smooth work-in-process for cycle time reduction and an empirical study," Annals of Operations Research, Springer, vol. 290(1), pages 1009-1033, July.
    10. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    11. Shen, Liji & Dauzère-Pérès, Stéphane & Neufeld, Janis S., 2018. "Solving the flexible job shop scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 265(2), pages 503-516.
    12. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    13. Golmohammadi, Davood, 2015. "A study of scheduling under the theory of constraints," International Journal of Production Economics, Elsevier, vol. 165(C), pages 38-50.
    14. Chia-Yen Lee & Ting-Syun Huang & Meng-Kun Liu & Chen-Yang Lan, 2019. "Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings," Energies, MDPI, vol. 12(5), pages 1-18, February.
    15. Milton Friedman, 1957. "A Theory of the Consumption Function," NBER Books, National Bureau of Economic Research, Inc, number frie57-1.
    16. Hammer, Michael & Champy, James, 1993. "Reengineering the corporation: A manifesto for business revolution," Business Horizons, Elsevier, vol. 36(5), pages 90-91.
    17. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    18. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    19. Bakker, Monique & Riezebos, Jan & Teunter, Ruud H., 2012. "Review of inventory systems with deterioration since 2001," European Journal of Operational Research, Elsevier, vol. 221(2), pages 275-284.
    20. Chien, Chen-Fu & Wang, Hung-Ju & Wang, Min, 2007. "A UNISON framework for analyzing alternative strategies of IC final testing for enhancing overall operational effectiveness," International Journal of Production Economics, Elsevier, vol. 107(1), pages 20-30, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    2. Duo Qin, 2010. "Econometric Studies of Business Cycles in the History of Econometrics," Working Papers 669, Queen Mary University of London, School of Economics and Finance.
    3. Chalamandaris, Georgios & Rompolis, Leonidas S., 2012. "Exploring the role of the realized return distribution in the formation of the implied volatility smile," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1028-1044.
    4. Gregory, Allan W. & Smith, Gregor W., 1996. "Measuring business cycles with business-cycle models," Journal of Economic Dynamics and Control, Elsevier, vol. 20(6-7), pages 1007-1025.
    5. David Greasley & Les Oxley, 2010. "Cliometrics And Time Series Econometrics: Some Theory And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 24(5), pages 970-1042, December.
    6. Aurélien Goutsmedt & Erich Pinzon-Fuchs & Matthieu Renault & Francesco Sergi, 2015. "Criticizing the Lucas Critique: Macroeconometricians' Response to Robert Lucas," Post-Print halshs-01179114, HAL.
    7. Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412.
    8. Sinelnikov-Murylev, Sergei (Синельников-Мурылев, Сергей) & Drobyshevskiy, Sergei (Дробышевский, Сергей) & Kazakova, Maria (Казакова, Мария), 2014. "Decomposition of the russian GDP growth rate in 1999-2014 [Декомпозиция Темпов Роста Ввп России В 1999—2014 Годах]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 5, pages 7-37, October.
    9. repec:aer:wpaper:70 is not listed on IDEAS
    10. Yener Coskun & Burak Sencer Atasoy & Giacomo Morri & Esra Alp, 2018. "Wealth Effects on Household Final Consumption: Stock and Housing Market Channels," IJFS, MDPI, vol. 6(2), pages 1-32, June.
    11. Stephen Pollock & Nikoletta Lekka, 2001. "Deconstructing the Consumption Function: New Tools and Old Problems," Working Papers 448, Queen Mary University of London, School of Economics and Finance.
    12. Hiroaki Hayakawa, 2020. "Consumer behavior in a monetary economy and smoothing of composite consumption," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 10(1), pages 89-122, March.
    13. Lamm, R. McFall Jr., 1980. "Effects of Government Policy on Agriculture: An Empirical Analysis," Economics Statistics and Cooperative Services (ESCS) Reports 329209, United States Department of Agriculture, Economic Research Service.
    14. Sargent, Thomas J., 1996. "Expectations and the nonneutrality of Lucas," Journal of Monetary Economics, Elsevier, vol. 37(3), pages 535-548, June.
    15. Kamal P. Upadhyaya & Dharmendra Dhakal & Franklin G. Mixon, 2017. "Housing prices, stock prices and the US economy," Applied Economics, Taylor & Francis Journals, vol. 49(59), pages 5916-5922, December.
    16. Ángel Guillén & Gabriel Rodríguez, 2014. "Trend-cycle decomposition for Peruvian GDP: application of an alternative method," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 23(1), pages 1-44, December.
    17. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    18. Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412, April.
    19. Habimana, Olivier, 2018. "Asymmetry and Multiscale Dynamics in Macroeconomic Time Series Analysis," MPRA Paper 87823, University Library of Munich, Germany.
    20. Sergey Sinelnikov-Murylev & Sergey Drobyshevsky & Maria Kazakova & Michael Alexeev, 2016. "Decomposition of Russia's GDP Growth Rates," Research Paper Series, Gaidar Institute for Economic Policy, issue 167P, pages 123-123.
    21. Mehmet Özcan, 2016. "Economical Expectation Theories with Quantitative Aspects: Case of Turkey and Kazakhstan," Eurasian Academy Of Sciences Social Sciences Journal, Eurasian Academy Of Sciences, vol. 7(7), pages 50-73, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-020-01711-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.