Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing

Published Online:https://doi.org/10.1287/mnsc.2021.4190

References

  • Bastani H, Bastani O, Kim C (2018) Interpreting predictive models for human-in-the-loop analytics. Accessed August 18, 2021, https://hamsabastani.github.io/interp.pdf.Google Scholar
  • Bastani H, Zhang D, Zhang H (2021) Applied machine learning in operations management. Babich V, Birge J, Hilary G, eds., Innovative Technology at the Interface of Finance and Operations, Springer Series in Supply Chain Management (Springer Nature, New York).Google Scholar
  • Bertsimas D, Dunn J (2017) Optimal classification trees. Machine Learn. 106(7):1039–1082.CrossrefGoogle Scholar
  • Breiman L (2001) Random forests. Machine Learn. 45(1):5–32.CrossrefGoogle Scholar
  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and Regression Trees (Wadsworth, Belmont, CA).Google Scholar
  • Chen WC, Tseng SS, Wang CY (2005) A novel manufacturing defect detection method using association rule mining techniques. Expert Systems Appl. 29(4):807–815.CrossrefGoogle Scholar
  • Chien CF, Wang WC, Cheng JC (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Systems Appl. 33(1):192–198.CrossrefGoogle Scholar
  • Corbett CJ (2018) How sustainable is big data? Production Oper. Management 27(9):1685–1695.CrossrefGoogle Scholar
  • Cui R, Gallino S, Moreno A, Zhang DJ (2018) The operational value of social media information. Production Oper. Management 27(10):1749–1769.CrossrefGoogle Scholar
  • Field JM, Sinha KK (2005) Applying process knowledge for yield variation reduction: A longitudinal field study. Decision Sci. 36(1):159–186.CrossrefGoogle Scholar
  • Fisher RA (1935) The Design of Experiments (Oliver and Boyd, Edinburgh, United Kingdom).Google Scholar
  • Friedman JH (2001) Greedy function approximation: A gradient boosting machine. Ann. Statist. 29(5):1189–1232.CrossrefGoogle Scholar
  • Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D (2018) A survey of methods for explaining black box models. ACM Comput. Surveys 51(5):1–42.CrossrefGoogle Scholar
  • Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Machine Learn. 46(1/3):389–422.CrossrefGoogle Scholar
  • Hastie T, Tibshirani R, Friedman JH (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. (Springer, New York).CrossrefGoogle Scholar
  • Hopp WJ, Spearman ML (2011) Factory Physics, 3rd ed. (Waveland Press, Long Grove, IL).Google Scholar
  • Ittner CD (1994) An examination of the indirect productivity gains from quality improvement. Production Oper. Management 3(3):153–170.CrossrefGoogle Scholar
  • Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) LightGBM: A highly efficient gradient boosting decision tree. Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Proc. 31st Conf. Neural Inform. Processing Systems (NIPS 2017, Long Beach, CA) (Curran Associates Inc., Red Hook, NY), 3149–3157.Google Scholar
  • Kusiak A (2017) Smart manufacturing must embrace big data. Nature 544(7648):23–25.CrossrefGoogle Scholar
  • Lundberg S, Lee SI (2017) A unified approach to interpreting model predictions. Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Proc. 31st Conf. Neural Inform. Processing Systems (NIPS 2017, Long Beach, CA) (Curran Associates Inc., Red Hook, NY), 4768–4777.Google Scholar
  • Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee SI (2020) From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence 2(1):56–67.CrossrefGoogle Scholar
  • Mišić VV, Perakis G (2020) Data analytics in operations management: A review. Manufacturing Service Oper. Management 22(1):158–169.LinkGoogle Scholar
  • Olsen TL, Tomlin B (2020) Industry 4.0: Opportunities and challenges for operations management. Manufacturing Service Oper. Management 22(1):113–122.LinkGoogle Scholar
  • Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you?: Explaining the predictions of any classifier. Krishnapuram B, Shah M, Smola A, Aggarwal C, Shen D, Rastogi R, eds. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (KDD 2016, San Francisco, CA) (Association for Computing Machinery, New York), 1135–1144.Google Scholar
  • Schmenner RW, Swink ML (1998) On theory in operations management. J. Oper. Management 17(1):97–113.CrossrefGoogle Scholar
  • Shapley LS (1953) A value for n-person games. Kuhn HW, Tucker AW, eds. Contributions to the Theory of Games, Annals of Mathematics Studies (Princeton University Press, Princeton, NJ), 307–318.CrossrefGoogle Scholar
  • Shewhart WA (1926) Quality control charts. Bell System Tech. J. 5(1):593–603.CrossrefGoogle Scholar
  • Slack D, Hilgard S, Jia E, Singh S, Lakkaraju H (2020) Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. Proc. AAAI/ACM Conf. AI Ethics Soc. (Association for Computing Machinery, New York), 180–186. Google Scholar
  • Sun J, Zhang D, Hu H, Van Mieghem JA (2021) Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations. Management Sci., ePub ahead of print September 10, https://doi.org/10.1287/mnsc.2021.3990.LinkGoogle Scholar
  • Taguchi G (1986) Introduction to Quality Engineering: Designing Quality into Products and Processes (Asian Productivity Organization, Tokyo).Google Scholar
  • Taguchi G, Clausing D (1990) Robust quality. Harvard Bus. Rev. 68(1):65–75.Google Scholar
  • Terwiesch C, Olivares M, Staats BR, Gaur V (2019) A review of empirical operations management over the last two decades. Manufacturing Service Oper. Management 22(4):656–668.LinkGoogle Scholar
  • Tsai TN (2012) Development of a soldering quality classifier system using a hybrid data mining approach. Expert Systems Appl. 39(5):5727–5738.CrossrefGoogle Scholar
  • Wu L, Zhang J (2010) Fuzzy neural network based yield prediction model for semiconductor manufacturing system. Internat. J. Production Res. 48(11):3225–3243.CrossrefGoogle Scholar
  • Yu B, Popplewell K (1994) Metamodels in manufacturing: A review. Internat. J. Production Res. 32(4):787–796.CrossrefGoogle Scholar
  • Zantek PF, Wright GP, Plante RD (2002) Process and product improvement in manufacturing systems with correlated stages. Management Sci. 48(5):591–606.LinkGoogle Scholar
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