Digitalization and automation for supply chain resilience using asset administration shell

Abstract

Coronavirus disease 2019 (COVID-19) revealed the vulnerability of supply chains worldwide. This disruption has forced manufacturers to establish systematic strategies for supply chain resilience (SCRES) to restore the original performance while minimizing recovery time. SCRES largely relies on human labor via on/off-line methods. Relevant studies have dominantly suggested concepts, methodologies, and mathematical models from a theoretical perspective; however, few empirical solutions have been derived to recover supply chain disconnection. This paper proposes an SCRES method to reconstruct a supply chain at the machine level in a semi-automated manner based on an asset administration shell (AAS), that is, a standardized model that identifies the digital representation of assets to ensure interoperability. In this method, web scraping is used to collect machine data provided by manufacturers on webpages. AASs are created to digitalize machine agents based on the proposed AAS structure with ECLASS-driven semantic classification and mapping. AASs are retrieved to search for alternative machines in a digitalized machine network when a machine breaks down. A supply chain is reconstructed using a path search algorithm by deriving the best alternative machine to substitute the broken machine. The proposed method is demonstrated in a case study of crankshaft production using a prototype implementation. The proposed method is a machine-level approach to implement independent and interconnected machine agents, whose actions and interactions affect the reaction of a supply chain based on complex adaptive system theory in SCRES.