C. M. Bishop, Pattern recognition and machine learning, 2006.

R. Carbonneau, K. Laframboise, and R. Vahidov, Application of machine learning techniques for supply chain demand forecasting, European Journal of Operational Research, vol.184, issue.3, pp.1140-1154, 2008.

Y. Cheng, K. Chen, H. Sun, Y. Zhang, and F. Tao, Data and knowledge mining with big data towards smart production, Journal of Industrial Information Integration, vol.9, pp.1-13, 2018.

A. K. Choudhary, J. A. Harding, and M. K. Tiwari, Data mining in manufacturing: a review based on the kind of knowledge, Journal of Intelligent Manufacturing, vol.20, issue.5, pp.501-521, 2009.

M. Chui, J. Manyika, M. Miremadi, N. Henke, R. Chung et al., Notes from the AI frontier: Insights from hundreds of use cases, 2018.

T. H. Davenport and J. G. Harris, Competing on analytics: The new science of winning, 2007.

P. A. Dreyfus and D. Kyritsis, A framework based on predictive maintenance, zerodefect manufacturing and scheduling under uncertainty tools, to optimize production capacities of high-end quality products, Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, IFIP Advances in Information and Communication Technology, vol.536, pp.296-303, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02177852

C. M. Flath and N. Stein, Towards a data science toolbox for industrial analytics applications, Computers in Industry, vol.94, pp.16-25, 2018.

J. A. Harding, M. Shahbaz, . Srinivas, and A. Kusiak, Data mining in manufacturing: A review, Journal of Manufacturing Science and Engineering, vol.128, issue.4, 2006.

N. Henke, J. Bughin, M. Chui, J. Manyika, T. Saleh et al., The age of analytics: Competing in a data-driven world, 2016.

T. Jebara, Machine Learning, 2004.


G. Köksal, ?. Batmaz, and M. C. Testik, A review of data mining applications for quality improvement in manufacturing industry, Expert Systems with Applications, vol.38, issue.10, pp.13448-13467, 2011.

M. Kraus, S. Feuerriegel, and A. Oztekin, Deep learning in business analytics and operations research: Models, applications and managerial implications

D. Lechevalier, A. Narayanan, S. Rachuri, and S. Foufou, A methodology for the semi-automatic generation of analytical models in manufacturing, Computers in Industry, vol.95, pp.54-67, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01862601

H. Leurent and E. De-boer, The next economic growth engine: Scaling fourth industrial revolution technologies in production, World Economic Forum, 2018.

J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs et al., Big data: The next frontier for innovation, competition, and productivity, 2011.

K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, Systematic mapping studies in software engineering. 12th International Conference on Evaluation and Assessment in Software Engineering, vol.8, pp.68-77, 2008.

D. T. Pham and A. A. Afify, Machine-learning techniques and their applications in manufacturing, Proceedings of the Institution of Mechanical Engineers, vol.219, issue.5, pp.395-412, 2005.

M. Sharp, R. Ak, and T. Hedberg, A survey of the advancing use and development of machine learning in smart manufacturing, Journal of Manufacturing Systems, vol.48, pp.170-179, 2018.

J. Z. Sikorska, M. Hodkiewicz, and L. Ma, Prognostic modelling options for remaining useful life estimation by industry, Mechanical Systems and Signal Processing, vol.25, issue.5, pp.1803-1836, 2011.

F. Tao, Q. Qi, A. Liu, and A. Kusiak, Data-driven smart manufacturing, Journal of Manufacturing Systems, vol.48, pp.157-169, 2018.


K. D. Thoben, S. Wiesner, and T. Wuest, Industrie 4.0" and smart manufacturing: A review of research issues and application examples, International Journal of Automation Technology, vol.11, issue.1, pp.4-16, 2017.

A. G. Villanueva-zacarias, P. Reimann, and B. Mitschang, A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing, Procedia CIRP, vol.72, pp.153-158, 2018.

J. Wang, Y. Ma, L. Zhang, R. X. Gao, and D. Wu, Deep learning for smart manufacturing: Methods and applications, Journal of Manufacturing Systems, vol.48, pp.144-156, 2018.

T. Wuest, D. Weimer, C. Irgens, and K. D. Thoben, Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, vol.4, pp.23-45, 2016.