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International Conference on Information and Software Technologies

ICIST 2019: Information and Software Technologies pp 44–55Cite as

Decision-Making Algorithms for ERP Systems in Road Maintenance Work

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1078))

Abstract

The article reviews the studies carried out so far for road maintenance work. Exploring approaches in which raw data can be processed from road weather-stations to actual forecasting and forecasting model creation. The goal is to be able to make forecasting and build the best forecasting models that will be implemented into the BaSeCaaS platform in the future. Forecasting is designed to improve the current situation during the winter months for road maintenders for better decision-making. Initially, the missing data is filled to be able to make forecasting possible. Several methods are applied and identified, which is the best from an accuracy perspective. An experiment is conducted with ARIMA the best forecasting model for the particular dataset. As well as looking for the best approach to updating the forecasting model parameter to improve accuracy and better results. The concept is created under this article, and the BPMN of Road Maintainers Case process is reflected. Uptake of the current research is depicted in forecasting UML class diagram that is created and represented within the UML sequence diagram of the forecasting process.

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Authors and Affiliations

  1. Institute of Information Technology, Riga Technical University, Kalku Street 1, Riga, Latvia

    Jānis Pekša

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  1. Jānis Pekša
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Correspondence to Jānis Pekša .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Giedrė Vasiljevienė

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Pekša, J. (2019). Decision-Making Algorithms for ERP Systems in Road Maintenance Work. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2019. Communications in Computer and Information Science, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-030-30275-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-30275-7_5

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  • Online ISBN: 978-3-030-30275-7

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