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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]

Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainable production. In Industry 4.0, various advanced technologies are used to achieve smart manufacturing, but the sustainability of production is often ignored without considering ESG performance. This study proposes a strategy of edge-cloud cooperation-driven smart and sustainable production to realise data collection, preprocessing, storage and analysis. In detail, kernel principal component analysis (KPCA) is used to decrease the interference of abnormal data in the evaluation results. Subsequently, an improved technique for order preference by similarity to ideal solution (TOPSIS) based on the adversarial interpretative structural model (AISM) is proposed to evaluate the production efficiency of the manufacturing workshop and make the analysis results more intuitive. Then, the architecture and models are verified using real production data from a partner company. Finally, sustainable analysis is discussed from the perspective of energy consumption, economic impact, greenhouse gas emissions and pollution prevention. • An edge-cloud cooperation-driven sustainable manufacturing strategy is proposed. • A KPCA algorithm is used for data processing. • An improved TOPSIS-AISM algorithm is established for data mining. • The effectiveness of the models and the management implications are discussed. [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]

Improving energy and resource efficiency in manufacturing is an important goal for enterprises to sustain their competitive advantages. Predictive production planning is a new solution to achieve such goal, following the direct improvement of energy efficiency and indirect energy savings through better scheduling. With the emergence of new information and communication technologies under the background of Industry 4.0, the amount of various energy and resource data obtained through Internet of Things is reaching the magnitude of big data. It poses a challenge to traditional data processing and mining methods for predictive production. To solve this challenge, in this paper, the big data driven predictive production planning is proposed to improve energy and resource efficiency for energy-intensive manufacturing industries. Additionally, the cube-based energy consumption models and long short-term memory based energy prediction model are established for data preprocessing and mining correspondingly. An industrial case study is presented to process energy big data and predict energy consumption parameters and production status. The performance evaluation results indicate that the proposed long short-term memory models outperform back propagation neural network, autoregressive moving average and support vector regression. Based on data preprocessing and forecasting results, the energy and resource efficiency could be improved during the whole manufacturing process for energy-intensive manufacturing industries. • Big data driven predictive production planning is proposed for cleaner production. • The cube-based energy consumption models are established to preprocess data. • The long short-term memory based energy prediction models are used for data mining. • Models can accurately predict energy and production status with periodicity. • The long short-term memory models outperform traditional forecasting methods. [ABSTRACT FROM AUTHOR]