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Purpose Smart manufacturing is the prime gripper for the transformation and upgrading of the manufacturing industry. Smart manufacturing systems (SMSs) largely determine how smart manufacturing evolves in technical and organizational dimensions and how it realizes values in products, production or services. SMSs are growing rapidly and receiving tons of attention from academic research and industrial practice. However, the development of SMSs is still in its fancy, and many issues wait to be identified and solved, such as single point failures, low transparency and ineffective resource sharing. Blockchain, an emerging technology deriving from Bitcoin, is competent to aid SMSs to conquer troubles due to its decentralization, traceability, trackability, disintermediation, auditability and etc. The purpose of this paper is to investigate the blockchain applications in SMSs, seek out the challenges faced by blockchain-enabled SMSs (BSMSs) and provide referable research directions and ideas. Design/methodology/approach A comprehensive literature review as a survey is conducted in this paper. The survey starts by introducing blockchain concepts, followed by the descriptions of a literature review method and the blockchain applications throughout the product life cycle in SMSs. Then, the key issues and challenges confronting BSMSs are discussed and some possible research directions are also proposed. It finally presents qualitative and quantitative descriptions of BSMSs, along with some conclusions and implications. Findings The findings of this paper present a deep understanding about the current status and challenges of blockchain adoption in SMSs. Furthermore, this paper provides a brand new thinking for future research. Originality/value This paper minutely analyzes the impacts that blockchain exerts on SMSs in view of the product life cycle, and proposes using the complexity science thinking to deal with BSMSs qualitatively and quantitatively, including tackling the current major problems BSMSs face. This research can serve as a foundation for future theoretical studies and enterprise practice.

With the ongoing progress in cloud computing, big data analytics (BDA) and other burgeoning technologies, the integration of intelligence and e-commerce systems now makes it possible to build e-commerce systems with enhanced efficiency, reduced transaction costs and smart information-processing patterns. However, despite the fact that smart e-commerce systems (SESs) offer great opportunities to the business field, the development of SESs is still in its infancy. Numerous issues still need to be resolved. To offer a better comprehension of SESs and facilitate future research, this paper first describes the holistic architecture of these systems and analyzes the main enablers underlying the development of SESs in terms of internet of things (IoT), social media, mobile internet, big data analytics and cloud computing. Then, the key challenges and issues pertaining to current SESs are presented, and some possible research directions are also proposed. Finally, the paper presents qualitative and quantitative depictions of SESs from a complex systems perspective, which provides a brand new idea of how to address the current SES issues.

Process-oriented ontology for a SMCPS is established based on WF-nets.A two-stage optimization model for data quality management of a SMCPS is proposed.Two algorithms to solve the two-stage optimization model are designed.The advantages of the proposed model are significant in balancing local and global management objectives. Service-oriented manufacturing (SOM) is a new worldwide manufacturing paradigm, and a cyber-physical system (CPS) is accepted as a strategic choice of SOM enterprises looking to provide bundles of satisfying products and services to customers. The issue of data quality is common in any CPS and poses great challenges to its efficient operation. This paper focuses on defective data generated by the improper operation of physical and cyber components of a service-oriented manufacturing CPS (SMCPS), and develops effective managerial policies to deal with such data. First, formal semantics of workflow nets (WF-nets) are employed to construct process-oriented ontology for the SMCPS. Second, a two-stage optimization model together with algorithms is designed to find optimal policies that balance local and global management objectives. Finally, our model is illustrated through a case. Results show that the proposed control strategy outperforms one-stage control and random control in guaranteeing data quality and saving control costs. Display Omitted

Fault diagnosis is an important topic both in practice and research. There is intense pressure on industrial systems to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering from potential faults as early as possible. From the historical perspective, this paper divides fault diagnosis into previous research and industrial big data era. According to primary drivers, this paper classifies fault diagnosis into knowledge-driven, data-driven, and value-driven methods. Among them, the former two approaches belong to the previous research on fault diagnosis. They mainly depend on expert experience and shallow models to detect and extract failures from relatively small size data. With the continuous exponential growth of data, it is insufficient to mine valuable fault information from massive multi-source heterogeneous data. The huge diagnostic value embodied in industrial big data has driven the emergence of the third category, which belongs to fault diagnosis based on big data. It consists of big data processing and analysis corresponding to high efficiency, cost effectiveness, and generality, which can deal well with problems that previous methods faced. We introduce the concept of a device electrocardiogram from the perspective of applicability to outline the present status of fault diagnosis for big data, and compare it with traditional diagnostic system. We also discuss issues and challenges that need to be further considered. It would be great valuable to integrate or explore more advanced diagnostic methods to handle collected industrial big data and put them into practice to mine the huge hidden diagnostic value.

Determining the importance of information subsystems is critical to the construction and maintenance of the information systems in manufacturing companies. From the perspective of the interactions among subsystems, several centrality models are developed to assess the importance of subsystems by using software called Code Blocks. A case study is done to test the methods. The results can provide an objective basis for decision making in management activities of an information system, such as information system planning and maintenance, resource allocation and utilization in manufacturing enterprises. The proposed methods will provide new approaches to the quantitative evaluation of the importance of information subsystems.