Tools
ToolsXu, L., de Vrieze, P. T., Arshad, R. and Oyekola, O., 2022. Enhance Supply Chain Resilience through Industry 4.0 – A view of designing simulation scenarios. In: IEEE International Conference on E-Business Engineering (ICEBE) 2022, 14-16 October 2022, Bournemouth, UK.
Full text available as:
|
PDF
Submit version-Enhance SCR through I4.0_camera-ready version.pdf - Published Version Available under License Creative Commons Attribution Non-commercial. 497kB | |
|
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
Official URL: https://conferences.computer.org/icebe/2022/index....
Abstract
COVID-19 pandemic and the Ukraine war demonstrate the inevitability of supply chain disruptions. A lack of effective supply chain resilience (SCR) causes mismatches between demand and supply, and the destabilization of normal operational policies in production, distribution, and inventory control. Existing research mainly provides different definitions and measurements of supply chain resilience for different product supply chains. In this paper, we look at how Industry 4.0 (I4.0) technologies enhance supply chain resilience. An I4.0 enabled architecture is designed for a factory with multiple suppliers and extended inventory for improving SCR. Different simulation scenarios for a LED factory are designed for demonstrating I4.0 technologies supporting SCR in different phases.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Funded by FIRST |
| Uncontrolled Keywords: | supply chain resilience; industry 4.0; supply chain disruption; supply chain recovery; simulation |
| Group: | Faculty of Science & Technology |
| ID Code: | 37650 |
| Deposited By: | Symplectic RT2 |
| Deposited On: | 18 Oct 2022 09:39 |
| Last Modified: | 20 Dec 2022 09:15 |
Downloads
Downloads per month over past year
| Repository Staff Only - |