Predicting fluid work demand in service organizations using AI techniques

Sara AlShizawi, Siddhartha Shakya, Andrzej Sluzek, Russell Ainslie, Gilbert Owusu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Prediction is about making claims on future events based on past information and the current state. Predicting workforce demand for the future can help service organizations adjust their resources and reach their goals of cost saving and enhanced efficiency. In this paper, a use case for a telecom service organization is presented and a framework for predicting workforce demand using neural networks is provided. The experiments were performed with real-world data, and the results were compared against other popular techniques such as linear regression and also moving average which served as a simulation of the technique historically applied manually in the organization. The results show that the accuracy of prediction is improved with the use of neural networks. The technique is being built into a tool that is being tested by the partner telecom organization.

Original languageBritish English
Title of host publicationArtificial Intelligence XXXV - 38th SGAI International Conference on Artificial Intelligence, AI 2018, Proceedings
EditorsMax Bramer, Miltos Petridis
PublisherSpringer Verlag
Pages266-276
Number of pages11
ISBN (Print)9783030041908
DOIs
StatePublished - 2018
Event38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018 - Cambridge, United Kingdom
Duration: 11 Dec 201813 Dec 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11311 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference38th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2018
Country/TerritoryUnited Kingdom
CityCambridge
Period11/12/1813/12/18

Keywords

  • Linear regression
  • Moving average
  • Neural network
  • Prediction

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