###### Abstract
[15] highlighted that the \"cloud supply chain integrates all operational processes (e.g., logistics, warehousing, manufacturing, sales, and returns), all supply chain flows (i.e., material, informational and financial), and all the supply chain actors (i.e., suppliers, manufacturers, distributors, and customers) in a digital ecosystem.\" However, SCM is a core business operation that is defiantly resisting digital transformation, as is still dependent on spreadsheets, enterprise resource planning (ERP) systems, and manufacturing execution systems (MES), making strides in digital transformations futile [16]. SCs and planning ISs have been supported by ERP and MES software systems, which are built on traditional monolithic architecture and no longer suffice the rapid development and expansion for improving scalability in operations. In addition, in a manufacturing supply chain, BD generated by each organization's information system is fragmented and isolated. As such, companies need to adopt new technologies and business strategies to enable agile and scalable applications [17]. CC infrastructure was previously delineated into three offerings, namely infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [18]. Recently, serverless computing has become a prominent and preferred CC style for developers. Serverless computing was first introduced in 2014 by Amazon Web Services and later adopted in 2016 by Google Cloud and Microsoft Azure [19]. Hence, serverless computing is a newly established cloud-native paradigm, which represents an evolution in the cloud application deployment of applications, services, and the development of abstractions, programming models, and platforms [18]. In serverless computing, resources are dynamically provisioned at run-time, along with their configuration and management through event-driven allocation and control [20]. Consequently, this adds an abstraction layer to PaaS, whilst taking away backend server management [19]. Serverless computing allows developers to run event-driven and granular applications without addressing operational logic [21]. As such, SC operations are deployed as microservices, i.e., suites of small functional services which operate autonomously and are favourable to cloud-native applications [22]. [PERSON] et al. [23] highlighted that the relationship between cloud manufacturing and new industry 4.0 technologies is barely discussed, although the integration of these technologies has a bearing on an organization's internal operations and management. SCM systems are proprietary information value chain systems, exposing the IS to cyber risk [24]. Blockchainis a disruptive technology for the 21 st century [25], which enhances the flexibility and security of collaboration amongst stakeholders. As a notable priority technology, blockchain is being implemented in every facet of business and it is expected that 10% of the world's GDP shall be stored in a blockchain system by 2027 [26]. [PERSON] et al. [27] gave an in-depth review of the application and use case of AI and blockchain technologies. [PERSON] and [PERSON] [28] identified that in the field of sustainable studies, the application of system dynamics is relatively new, which justifies the multiple opportunities relative to sustainable supply chain management. Hence, multiple organizations are looking into automating the tracing of their enterprise data using blockchain technologies [29]. Subsequently, by integrating emerging technologies, a blockchain-based cloud manufacturing SCM system was developed for a railcar manufacturing enterprise through the integration of a private blockchain with a cloud manufacturing system for boxed sheet metal parts. This paper presents a case study on a railcar manufacturing supply chain for production of boxed flat sheet metal parts. A two-tier manufacturing supply chain, i.e., railcar operator-railcar manufacturer and railcar manufacturer-manufacturing supplier, exists within the SC.