Abstract
In this paper we present a method to predict service levels in utility companies, giving them advanced visibility of expected service outcomes and helping them to ensure adherence to service level agreements made to their clients. Service level adherence is one of the key targets during the service chain planning process in service industries, such as telecoms or utility companies. These specify a time limit for successful completion of a certain percentage of tasks on that service level agreement. With the increasing use of automation within the planning process, the requirement for a method to evaluate the current plan decisions effects on service level outcomes has surfaced.
We build neural network models to predict using the current state of the capacity plan, investigating the accuracy when predicting both daily and weekly service level outcomes. It is shown that the models produce a high accuracy, particularly in the weekly view. This provides a solution that can be used to both improve the current planning process and also as an evaluator in an automated planning process.
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References
Voudouris, C.: Defining and understanding service chain management. In: Voudouris, C., Lesaint, D., Owusu, G. (eds.) Service Chain Management, pp. 1–17. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-75504-3_1
Verma, D.: Service level agreements on IP networks. Proc. IEEE 92(9), 1382–1388 (2004)
Owusu, G., O’Brien, P., McCall, J., Doherty, N.F.: Transforming Field and Service Operations. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-44970-3_2
Anderson, J.: An Introduction to Neural Networks. MIT Press, Cambridge (1995)
Haga, M., Demuth, H., Beale, M., De Jesús, O.: Neural Network Design, vol. 20. PWS Publishing Company, Boston (1996)
Kourentzes, N., Crone, S.: Advances in forecasting with artificial neural networks. Lancaster University (2010)
Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32(14), 2627–2636 (1998)
Zhang, G., Eddy Patuwo, B., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)
Sibi, P., Jones, S., Siddarth, P.: Analysis of different activation functions using back propagation neural networks. J. Theor. Appl. Inf. Technol. 47(3), 1264–1268 (2013)
Heaton, J.: Encog: library of interchangeable machine learning models for Java and C#. J. Mach. Learn. Res. 16, 1243–1247 (2015)
Ainslie, R., McCall, J., Shakya, S., Owusu, G.: Predictive planning with neural networks. In: Proceedings of the International Joint Conference on Neural Networks (2016). https://doi.org/10.1109/IJCNN.2016.7727460
Kisi, Ö., Uncuoglu, E.: Comparison of three back-propagation training algorithms for two case studies. Indian J. Eng. Mater. Sci. 12(5), 434–442 (2005)
Prechelt, L.: PROBEN1 – a set of neural network benchmark problems and benchmarking rules, Karlsruhe (1994)
Tian, Y., Shi, Y., Liu, X.: Recent advances on support vector machines research. Technol. Econ. Devel. Econ. 18, 5–33 (2012)
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Ainslie, R., McCall, J., Shakya, S., Owusu, G. (2017). Predicting Service Levels Using Neural Networks. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXIV. SGAI 2017. Lecture Notes in Computer Science(), vol 10630. Springer, Cham. https://doi.org/10.1007/978-3-319-71078-5_35
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DOI: https://doi.org/10.1007/978-3-319-71078-5_35
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