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Pitfalls and protocols of data science in manufacturing practice

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  • 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
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    1. 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.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. 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.
    4. 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.
    5. 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.
    6. Golmohammadi, Davood, 2015. "A study of scheduling under the theory of constraints," International Journal of Production Economics, Elsevier, vol. 165(C), pages 38-50.
    7. 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.
    8. Milton Friedman, 1957. "A Theory of the Consumption Function," NBER Books, National Bureau of Economic Research, Inc, number frie57-1, March.
    9. Hammer, Michael & Champy, James, 1993. "Reengineering the corporation: A manifesto for business revolution," Business Horizons, Elsevier, vol. 36(5), pages 90-91.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    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.
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    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.

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