MARE: Semantic Supply Chain Disruption Management and Resilience Evaluation Framework

Nour Ramzy, Sören Auer, Hans Ehm, Javad Chamanara

2022

Abstract

Supply Chains (SCs) are subject to disruptive events that potentially hinder the operational performance. Disruption Management Process (DMP) relies on the analysis of integrated heterogeneous data sources such as production scheduling, order management and logistics to evaluate the impact of disruptions on the SC. Existing approaches are limited as they address DMP process steps and corresponding data sources in a rather isolated manner which hurdles the systematic handling of a disruption originating anywhere in the SC. Thus, we propose MARE a semantic disruption management and resilience evaluation framework for integration of data sources included in all DMP steps, i.e. Monitor/Model, Assess, Recover and Evaluate. MARE, leverages semantic technologies i.e. ontologies, knowledge graphs and SPARQL queries to model and reproduce SC behavior under disruptive scenarios. Also, MARE includes an evaluation framework to examine the restoration performance of a SC applying various recovery strategies. Semantic SC DMP, put forward by MARE, allows stakeholders to potentially identify the measures to enhance SC integration, increase the resilience of supply networks and ultimately facilitate digitalization.

Paper Citation

in Harvard Style

Ramzy N., Auer S., Ehm H. and Chamanara J. (2022). MARE: Semantic Supply Chain Disruption Management and Resilience Evaluation Framework. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-569-2, pages 484-493. DOI: 10.5220/0010983500003179

in Bibtex Style

@conference{iceis22,
author={Nour Ramzy and Sören Auer and Hans Ehm and Javad Chamanara},
title={MARE: Semantic Supply Chain Disruption Management and Resilience Evaluation Framework},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2022},
pages={484-493},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010983500003179},
isbn={978-989-758-569-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - MARE: Semantic Supply Chain Disruption Management and Resilience Evaluation Framework
SN - 978-989-758-569-2
AU - Ramzy N.
AU - Auer S.
AU - Ehm H.
AU - Chamanara J.
PY - 2022
SP - 484
EP - 493
DO - 10.5220/0010983500003179