Predicting service levels using neural networks.

Ainslie, Russell; McCall, John; Shakya, Sid; Owusu, Gilbert

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.

Citation

AINSLIE, R., MCCALL, J., SHAKYA, S. and OWUSU, G. 2017. Predicting service levels using neural networks. In Bramer, M. and Petridis, M. (eds.) Artificial intelliegence XXXIV: proceedings of the 37th SGAI International innovative techniques and applications of artifical intelligence conference 2017 (AI 2017), 12-14 December 2017, Cambridge, UK. Lecture notes in computer science, 10630. Cham: Springer [online], pages 411-416. Available from: https://doi.org/10.1007/978-3-319-71078-5_35

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 37th SGAI International innovative techniques and applications of artifical intelligence conference 2017 (AI 2017)
• Start Date: Dec 12, 2017
• End Date: Dec 14, 2017
• Acceptance Date: Sep 4, 2017
• Online Publication Date: Nov 21, 2017
• Publication Date: Nov 21, 2017
• Deposit Date: Feb 14, 2019
• Publicly Available Date: Feb 14, 2019
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 411-416
• Series Title: Lecture notes in computer science
• Series Number: 10630
• Series ISSN: 0302-9743
• ISBN: 9783319710778
• DOI: https://doi.org/10.1007/978-3-319-71078-5_35
• Keywords: Neural network; NN; Prediction; Service levels; Early stopping strategy; Planning
• Public URL: http://hdl.handle.net/10059/3295
• Contract Date: Feb 14, 2019