Integrated Platform and Digital Twin Application for Global Automotive Part Suppliers

Abstract

For global automakers that manufacture products through a globally distributed supply chain, it is essential for this supply chain to be managed efficiently to enhance the efficiency and responsiveness to uncertain changes in the market. These manufacturers face limitations associated with independent applications, such as difficulties in collecting information from distributed sites and the inability to make quick decisions. To solve this problem, researchers have been actively investigating the application of smart manufacturing technology to improve productivity as well as the optimal use of manufacturing resources in dynamic environments. Furthermore, different countries have different cultures, regulations, and policies. Hence, a universal integrated platform is required to address these difficulties. Accordingly, this study proposes an integrated cyberphysical system-based platform that reflects international standards and has versatile applications. This platform can be used to utilize information from various distributed manufacturing sites in real time. In addition, the proposed system was verified through a field application case study.

Digital twin-driven smart supply chain

“…In addition to these general conceptual development, the DTSC concept has been discussed in several specified application fields, including circular supply chain (Preut et al, 2021), food supply chain (Smetana et al, 2021;Shoji et al, 2022), global port management (Wang et al, 2021), and logistics (Lee and Lee, 2021;Moshood et al, 2021;Park et al, 2021). In summary, the core functionality of a DTSC is to provide an integrated and holistic view, which enables all stakeholders to contribute jointly to the creation across different supply chain stages (Ducree et al, 2020;Yang et al, 2020).…”

Development of a Structural Model for the Adoption of Industry 4.0 Enabled Sustainable Operations for Operational Excellence

“…The role of 3D printing is significant in Industry 4.0 as it helps to support sustainability by generating less scrap and carbon emissions during the production phase [20]. Using big data, helpful information from large datasets can be extracted and used for intelligent decision-making [21,22]. Studies have provided a roadmap for the implementation of Industry 4.0 in the last few years, but these studies are very generalized [23].…”

A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques

“…Piston-type high pressure accumulators are preferred in aircraft hydraulic circuits to store hydraulic power under pressure. Accumulator size is obtained based on the universal gas law and is calculated using equation (9).…”

“…With the advancement of simulation technologies, the digital twin technology is currently being used in several fields, from aviation to the automotive industries. [8][9][10][11] Since the early 2000s, health management studies have gained importance in terms of the diagnostics and prognostics of aircraft systems. [12][13][14] Over the past decade, artificial intelligence (AI)-based approaches to detect and isolate individual failures in aircraft subsystems have gained tremendous prominence both in academics and the industry.…”

“…With the advancement of simulation technologies, the digital twin technology is currently being used in several fields, from aviation to the automotive industries. 8–11…”