Data-Driven Process Analysis of Logistics Systems: Implementation Process of a Knowledge-Based Approach

Konstantin Muehlbauer, Stephan Schnabel, Sebastian Meissner

2024

Abstract

Due to the use of planning and control systems and the integration of sensors in the material flow, a large amount of transaction data is generated by logistics systems in daily operations. However, organizations rarely use this data for process analysis, problem identification, and process improvement. This article presents a knowledge-based, data-driven approach for transforming low-level transaction data obtained from logistics systems into valuable insights. The procedure consists of five steps aimed at deploying a decision support system designed to identify optimization opportunities within logistics systems. Based on key performance indicators and process information, a system of interdependent effects evaluates the logistics system’s performance in individual working periods. Afterward, a machine learning model classifies unfavorable working periods into predefined problem classes. As a result, specific problems can be quickly analyzed. By means of a case study, the functionality of the approach is validated. In this case study, a trained gradient-boosting classifier identifies predefined classes on previously unseen data.

Paper Citation

in Harvard Style

Muehlbauer K., Schnabel S. and Meissner S. (2024). Data-Driven Process Analysis of Logistics Systems: Implementation Process of a Knowledge-Based Approach. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 28-38. DOI: 10.5220/0012505200003690

in Bibtex Style

@conference{iceis24,
author={Konstantin Muehlbauer and Stephan Schnabel and Sebastian Meissner},
title={Data-Driven Process Analysis of Logistics Systems: Implementation Process of a Knowledge-Based Approach},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={28-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012505200003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Data-Driven Process Analysis of Logistics Systems: Implementation Process of a Knowledge-Based Approach
SN - 978-989-758-692-7
AU - Muehlbauer K.
AU - Schnabel S.
AU - Meissner S.
PY - 2024
SP - 28
EP - 38
DO - 10.5220/0012505200003690
PB - SciTePress