Abstract
Artificial intelligence in general and the techniques of machine learning in particular provide many possibilities for data analysis. When applied to services, they allow them to become smart by intelligently analyzing data of typical service transactions, e.g., encounters between customers and providers. We call this service analytics. In this chapter, we define the terminology associated with service analytics, artificial intelligence, and machine learning. We describe the concept of service analytics and illustrate it with typical examples from industry and research.
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References
Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3–31.
Böhmann, T., Leimeister, J. M. & Möslein, K. (2014). Service-systems-engineering. Wirtschaftsinf, 56, 83–90 (2014).
Bousquet, O., von Luxburg, U., & Rätsch, G. (2011). Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, February 2–14, 2003, Tübingen, Germany, August 4–16, 2003, Revised Lectures (Vol. 3176). Berlin: Springer.
Chollet, F. (2017). Deep learning with python (1st ed.). Greenwich, CT: Manning Publications.
Davenport, T., & Harris, J. (2017). Competing on analytics: Updated, with a new introduction: The new science of winning. Brighton, MA: Harvard Business Press.
Delen, D., & Demirkan, H. (2013). Data, information and analytics as services.
Eichengreen, B., & Gupta, P. (2011). The two waves of service-sector growth. Oxford Economic Papers, 65(1), 96–123.
Emmert-Streib, F., & Dehmer, M. (2009). Information theory and statistical learning. Berlin: Springer.
Fromm, H., Habryn, F., & Satzger, G. (2012). Service analytics: leveraging data across enterprise boundaries for competitive advantage. In: U. Bäumer, P. Kreutter, W. Messner (Eds.), Globalization of professional services: Innovative strategies, successful processes, inspired talent management, and first-hand experiences (pp. 139–149). Berlin: Springer.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press.
Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: Data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83–85.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Heid, B., Huth, C., Kempf, S., & Wu, G. (2018). Ready for inspection: The automotive aftermarket in 2030. Technical report, McKinsey & Company.
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-R., Jaitly, N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29(6), 82–97.
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2004). Extreme learning machine: a new learning scheme of feedforward neural networks. In Proceedings. 2004 IEEE International Joint Conference on Neural Networks, 2004 (Vol. 2, pp. 985–990). Piscataway, NJ: IEEE.
Kisore, N. R., & Reddy, P. G. (2015). Empirical determination and evaluation of factors that impact ATM placement. In 2015 48th Hawaii International Conference on System Sciences (pp. 1341–1348). Piscataway, NJ: IEEE.
Kohavi, R., Rothleder, N. J., & Simoudis, E. (2002). Emerging trends in business analytics. Communications of the ACM, 45(8), 45–48.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
Kühl, N., Goutier, M., Hirt, R., & Satzger, G. (2019). Machine learning in artificial intelligence: Towards a common understanding. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
Kühl, N., Hirt, R., Baier, L., Schmitz, B., Satzger, G. (2020). How to conduct rigorous supervised machine learning in information systems research: The supervised machine learning reportcard, communications of the association for information systems.
Laubis, K., Simko, V., Schuller, A., & Weinhardt, C. (2017). Road condition estimation based on heterogeneous extended floating car data. In Proceedings of the 50th Hawaii International Conference on System Sciences.
LeCun, Y. A., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436–444.
Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Trans. Information Theory, 28, 129–136.
Maglio, P. P., Kieliszewski, C. A., Spohrer, J. C., Lyons, K., Patrício, L., & Sawatani, Y. (2018). Handbook of service science (Vol. II). Berlin: Springer.
Martin, D., Kühl, N., von Bischhoffshausen, J. K., & Satzger, G. (in press). System-wide learning in cyber-physical service systems: A research agenda. In Proceedings of the 15th International Conference on Design Science Research in Information Systems and Technology (DESRIST 2020), Kristiansand, Norwegen, November 2020.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. Cambridge, MA: MIT Press.
Neely, A. (2008). Exploring the financial consequences of the servitization of manufacturing. Operations Management Research, 1(2), 103–118.
Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society. Series A (General), 135(3), 370–384.
Nilsson, N. J. (2014). Principles of artificial intelligence. Burlington, MA: Morgan Kaufmann.
Schoch, J., Staudt, P., and Setzer, T. (2017). Smart data selection and reduction for electric vehicle service analytics.
Schommer, C. (2008). An unified definition of data mining. Preprint arXiv:0809.2696
Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1–47.
Shwartz-Ziv, R., & Tishby, N. (2017). Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810.
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of go without human knowledge. Nature, 550, 354–359.
Steins, K., Matinrad, N., & Granberg, T. (2019). Forecasting the demand for emergency medical services. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
Steuer, D., Hutterer, V., Korevaar, P., & Fromm, H. (2018). A similarity-based approach for the all-time demand prediction of new automotive spare parts. In: Proceedings of the 51st Hawaii International Conference on System Sciences
Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: Continuing the evolution. Journal of the Academy of Marketing Science, 36(1), 1–10.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed., Vol. 54). Burlington, MA: Morgan Kaufmann
Wolff, C., Vössing, M., Schmitz, B., & Fromm, H. (2018). Towards a technician marketplace using capacity-based pricing. In Proceedings of the 51st Hawaii International Conference on System Sciences.
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Kühl, N., Fromm, H., Schöffer, J., Satzger, G. (2020). Service Analytics: Putting the “Smart” in Smart Services. In: Maleshkova, M., Kühl, N., Jussen, P. (eds) Smart Service Management. Springer, Cham. https://doi.org/10.1007/978-3-030-58182-4_13
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DOI: https://doi.org/10.1007/978-3-030-58182-4_13
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