With the growing awareness of environmental and social issues, sustainable supply chain management (SSCM) has received considerable attention both in academia and industry. Supplier selection plays an important role in the successful implementation of sustainable supply chain management, because it can influence the performance of SSCM. Sustainable supplier selection is a typical multi-criteria decision-making problem involving subjectivity and vagueness. Although some previous researches of supplier selection use fuzzy approaches to deal with vague information, it has been criticized for requiring much priori information and inflexibility in manipulating vagueness. Moreover, the previous methods often omit the environmental and social evaluation criteria in the supplier selection. To manipulate these problems, a new approach based on the rough set theory and ELECTRE (ELimination Et Choix Traduisant la REalité) is developed in this paper. The novel approach integrates the strength of rough set theory in handling vagueness without much priori information and the merit of ELECTRE in modeling multi-criteria decision-making problem. Finally, a case study of sustainable supplier selection for solar air-conditioner manufacturer is provided to demonstrate the application and potential of the approach.

Smart industrial product-service system of systems (SiP-S3) is a new extension of industrial PSS via smart technology and SoS (system of systems) engineering. A framework with revised rough-DEMATEL method is proposed to capture and evaluate requirements for SiP-S3. From the various interactions in value stream mapping of SiP-S3, business, functional and non-functional requirements can be captured and converged into SiP-S3 requirements. Due to the interrelation between requirements of SiP-S3 and uncertainty of expert judgments, rough-DEMATEL is adopted but is revised in two aspects. Generally, SiP-S3 requirement items are plentiful, a set of programming code for rough set approximation is firstly given to reduce manual calculation burden. Moreover, compared to multiple operators of modified-CFCS (converting fuzzy values into crisp scores) plus SVL (single vector-length), a feasible and simpler operator of AVL (average vector-length) on rough-prominence and rough-relation is firstly devised to prioritise requirements. As such, roughness can be remained till at the end of calculation procedure to avoid uncertain assessment information loss. Surface mount technology (SMT) is not trivial in electronic manufacturing service industry, an illustrative case study of SMTE-S3 (SMT equipment-service system of systems) is demonstrated to verify feasibility and potential of proposal methodological framework.

The trend towards socialization, personalization and servitization in smart manufacturing has attracted the attention of researchers, practitioners and governments. Social manufacturing is a novel manufacturing paradigm responding to this trend. However, the current cyber–physical system (CPS) merges only cyber and physical space; social space is missing. A cyber–physical–social system (CPSS)-based smart manufacturing is in demand, which incorporates cyber space, physical space and social space. With the development of the Internet of Things and social networks, a large volume of data is generated. A data-driven view is necessary to link tri-space. However, there is a lack of systematical investigation on the integration of CPSS and the data-driven view in the context of social manufacturing. This article proposes a seven-layered framework for a data-driven CPSS (D-CPSS) along the data–information–knowledge–wisdom (DIKW) pyramid under a social manufacturing environment. The evolution, components, general model and framework of D-CPSS are illustrated. An illustrative example is provided to explain the proposed framework. Detailed discussion and future perspectives on implementation are also presented.

Enterprise information systems play a significant role in the Industry 4.0 era and are the crucial component to realize smart manufacturing systems. However, traditional enterprise information systems have some limits: (1) lack of complete information, (2) only satisfy limited business needs, and (3) lack of seamless integration, business intelligence, value-driven processes, and dynamic optimization. Clearly, the existing enterprise information systems are unable to satisfy the requirements for smart manufacturing systems: (1) autonomous operation, (2) sustainable values, and (3) self-optimization. In addition, smart manufacturing systems have become more efficient and effective, demanding for seamless information flow in enterprise information systems, knowledge, and data-driven accurately decision. Therefore, a new enterprise information systems framework is needed to bridge gaps between the requirements for traditional manufacturing system and smart manufacturing system. In this article, the integrative framework is proposed based on the business process reengineering, lean thinking, and intelligent management methods, with inclusion of six enterprise information systems aspects to provide upgrading guidelines from traditional manufacturing to smart manufacturing. The procedure of this method contains three steps: (1) it identifies requirements and acquires best practices using AS-IS model, (2) it redesigns six aspects of enterprise information systems using TO-BE model, and (3) it proposes a new enterprise information systems framework. Finally, the proposed framework is validated by real cases.

The integration and application of a warehouse system and manufacturing system has become a manufacturing problem for enterprises. The main reason is that the information control system based on automation and stereo warehouse is inconsistent with the production and management information system of the enterprise in terms of business, data, functions, etc. Based on this, this paper studies the implementation of an automated warehouse based on the integration of ERP (enterprise resource planning) and WMS (warehouse management system) with the method and technology of the intermediate table. Moreover, MES (manufacturing execution system) is the brain and the core part of a sustainable digital factory. The enterprise adopts advanced intelligent and information technology to build and deploy the MES, realize fine management and agile production, and meet the personalized needs of the market. Therefore, this paper studies the implementation path and effect based on MES from an industrial realization to construct a sustainable digital factory. The research results of this paper can improve industrial efficiency and reduce costs for enterprises in storage capacity, handling capacity, response rate, rate of error, number of operators, etc.