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.

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders’ functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China.

In this article, an integrated method is proposed for evaluating the requirements of smart manufacturing systems under the era of Industry 4.0 and Industrial Internet of Things. This method uses systematic research on identifying, classifying, and evaluating the requirements of smart manufacturing systems with uncertainty, multi-users, and multi-disciplines. The results of this article provide a procedure to capture the preferential SMSs-R and the framework of SMSs-R. First, this method provides a comprehensive requirement of smart manufacturing identified by questionnaire survey, document reviews, and the affinity diagram method. Second, the classification of SMSs-R considers the multi-user preference by the fuzzy Kano model. Third, the best non-fuzzy performance and fuzzy analytic hierarchy process method is used for evaluating and ranking the SMSs-R factor to solve the uncertain, vague, and preferential questions. Finally, we validated the effectiveness of the method through a real discrete manufacturing enterprise case.