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In Industry 4.0, the production data obtained from the Internet of Things has reached the magnitude of big data with the emergence of advanced information and communication technologies. The massive and low-value density of big data challenges traditional clustering and correlation analysis. To solve this problem, a big data-driven correlation analysis based on clustering is proposed to improve energy and resource utilisation efficiency in this paper. In detail, the production units with abnormal and energy-intensive consumption can be classified by using clustering analysis. Additionally, feature extraction is carried out based on clustering analysis and the same cluster data is migrated to the training data set to improve correlation analysis accuracy. Then, correlation analysis can balance the relationship between energy supply and demand, which can reduce carbon emission and enhance sustainable competitiveness. The sensitivity analysis results show that the feature extraction method can improve the correlation analysis accuracy compared to the original analysis model. In conclusion, the big data-driven correlation analysis based on clustering can uncover the potential relationship between energy consumption and product yield, thus improving the efficiency of energy and resources. • An architecture of big data clustering and correlation analysis is proposed. • A PCA-guided ISODATA is proposed for data pre-processing and processing. • The feature extraction method has been proposed based on clustering. • A correlation analysis model is established for data mining. • The models can improve the scheduling efficiency of energy and resources. [ABSTRACT FROM AUTHOR]

Cleaner production is a green production way to minimize emissions and waste as well as maximize product output. Cleaner production has proven itself as an effective way to reduce energy consumption and improve material utilization during the whole manufacturing process. However, the implementation of cleaner production strategy is facing barriers due to the lack of applying advanced technologies such as cloud manufacturing, Internet of Things, and cyber-physical system. Based on these advanced technologies, this paper presents an architecture of energy-cyber-physical system enabled management for energy-intensive manufacturing industries to promote the implementation of cleaner production strategy. An energy-cyber-physical system enabled green manufacturing model for the future smart factory is proposed. Then the qualitative and quantitative synergetic models based on energy-cyber-physical system are developed for cleaner manufacturing. Finally, an application of a partner company is presented to demonstrate the proposed framework and synergistic models. The results show that energy consumption can be greatly reduced using synergistic evaluation models. Further managerial implications are summarized to increase the level of synergy of energy-cyber-physical system, which can achieve cleaner production strategy by making energy-efficient decisions from different departments. • An architecture of energy-CPS enabled management is proposed to save energy. • Energy-CPS enabled intelligent model is proposed for green manufacturing. • Qualitative and quantitative synergetic models are developed for cleaner production. • The energy consumption can be reduced through the synergistic evaluation models. [ABSTRACT FROM AUTHOR]

• A data-driven cleaner production strategy is proposed for energy-intensive industries. • The data-driven strategy is explained by the cleaner production data lifecycle. • The implementation models are developed to illustrate the proposed strategy. • The effectiveness of the proposed strategy is discussed for companies in China. Cleaner production is an effective strategy for improving material utilization, reducing energy consumption, maximizing product output, and minimizing emissions during the entire manufacturing processes. An increasing number of enterprises promote the implementation of cleaner production. In the Industry 4.0 context, product-embedded information devices are widely used to capture product lifecycle data. Product lifecycle management provides an opportunity to achieve cleaner production strategy. This paper presents a data-driven cleaner production strategy by product lifecycle management for energy-intensive manufacturing industries. The data-driven strategy is proposed and explained by the lifecycle of cleaner production data. Implementation models are developed to illustrate the proposed data-driven cleaner production strategy. Furthermore, two case studies from Southern and Northern China are presented to demonstrate the strategy. Results show that the unit energy consumption and the energy cost of production in Company A are reduced by at least 3% after applying the proposed strategy. In addition, the "cradle-to-gate" lifecycle data analysis indicates that the costs of environmental pollution in Company B are diminished significantly. Finally, energy-intensive manufacturing companies in China are compared and evaluated, and the effectiveness of the proposed strategy is discussed. [ABSTRACT FROM AUTHOR]