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Under the promotion of sustainable development goals, cleaner production (CP) has become an important strategy for energy-intensive manufacturing industries to maintain their competitiveness. Studies have shown that the implementation of Industry 4.0 (I4.0) can effectively promote the CP process in manufacturing. However, existing studies often focus on specific scenarios, limiting a comprehensive assessment of the industry's overall status. This paper aims to provide a comprehensive overview of the main research areas of I4.0 and its key impacts on CP by employing systematic mapping studies. By reviewing 121 studies retrieved from the Web of Science, a hierarchical analysis framework centered on the product life cycle (PLC) has been introduced. This framework provides a holistic analysis of sustainable intelligent manufacturing, summarizing the application of I4.0 and its impact on CP across the PLC stages. The main findings reveal a growing focus on I4.0 and CP, with the manufacturing and maintenance stages of the PLC being primary research scenarios. Moreover, attention should be directed towards integrating clean technologies, bolstering industrial data security, fostering circular economy practices, and exploring emerging fields like Industry 5.0. Moreover, to better explain the impact of this study, management implications are also provided from the three dimensions of theory, practice, and policy. These viewpoints and conclusions are of great significance for guiding future research and practical applications. • A comprehensive review of Industry 4.0 and cleaner production is conducted. • Systematic mapping and bibliometric methods are employed. • A hierarchical framework based on product life cycle is proposed. • The current challenges and future prospects are discussed. • Management implications from theory, practice, and policy are provided. [ABSTRACT FROM AUTHOR]

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]

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]

The utilization of advanced industrial informatics, such as industrial internet of things and cyber-physical system (CPS), provides enhanced situation awareness and resource controllability, which are essential for flexible real-time production scheduling and control (SC). Regardless of the belief that applying these advanced technologies under electricity demand response can help alleviate electricity demand–supply mismatches and eventually improve manufacturing sustainability, significant barriers have to be overcome first. Particularly, most existing real-time SC strategies remain limited to short-term scheduling and are unsuitable for finding the optimal schedule under demand response scheme, where a long-term production scheduling is often required to determine the energy consumption shift from peak to off-peak hours. Moreover, SC strategies ensuring the desired production throughput under dynamic electricity pricing and uncertainties in manufacturing environment are largely lacking. In this research, a knowledge-aided real-time demand response strategy for CPS-enabled manufacturing systems is proposed to address the above challenges. A knowledge-aided analytical model is first applied to generate a long-term production schedule to aid the real-time control under demand response. In addition, a real-time optimization model is developed to reduce electricity costs for CPS-enabled manufacturing systems under uncertainties. The effectiveness of the proposed strategy is validated through the case study on a steel powder manufacturing system. The results indicate the exceptional performance of the proposed strategy as compared to other real-time SC strategies, leading to a reduction of electricity cost up to 35.6% without sacrificing the production throughput. [ABSTRACT FROM AUTHOR]

The circular economy plays an important role in energy-intensive industries, aiming to contribute to ethical sustainable societal development. Energy demand response is a key actor for cleaner production and circular economy strategy. In the Industry 4.0 context, the advanced technologies (e.g. cloud computing, Internet of things, cyber-physical system, digital twin and big data analytics) provide numerous opportunities for the implementation of a cleaner production strategy and the development of intelligent manufacturing. This paper presented a framework of data-driven sustainable intelligent/smart manufacturing based on demand response for energy-intensive industries. The technological architecture was designed to implement the proposed framework, and multi-level demand response models were developed based on machine, shop-floor and factory to save energy cost. Finally, an application of ball mills in a slurry shop-floor of a partner company was presented to demonstrate the proposed framework and models. Results showed that the energy efficiency of ball mills can be greatly improved. The energy cost of the slurry shop-floor saved approximately 19.33% by considering electricity demand response using particle swarm optimisation. This study provides a practical approach to make effective and energy-efficient decisions for energy-intensive manufacturing enterprises. • A framework of data-driven sustainable intelligent manufacturing is proposed. • The framework is to achieve a circular economy for energy-intensive industries. • Multi-level demand response models based on PSO are developed to save energy costs. • The framework and models can benefit the environment, economy and society. [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]

• A new model of integrated manufacturing and material handling system is proposed. • The implications of fast charging on electricity demand are investigated. • Optimal schedules to reduce energy cost under production constraints are obtained. • The proposed demand response strategy can save up to 14.2% of the energy cost. Electricity demand response is proved to be a viable approach to address the peak demand, improve the power grid reliability, and facilitate the grid integration of renewable resources. The implementation of demand response management in the manufacturing sector, which is one of the main energy consumers, has received significant attention. The existing studies in this field have traditionally focused on maximizing the benefits of demand response through machine control and scheduling. However, due to the widespread adoption of fast-charging infrastructure for material handling equipment (MHE), the high-power demand of fast charging and the close coordination between the machines and MHE pose much-increased complexity in the energy management, which necessitates a more comprehensive demand response scheduling method for the integrated manufacturing and material handling system. In this study, an analytical manufacturing and material handling system model is proposed to obtain cost-effective production schedules under demand response. The interactions between machines and MHE, production throughput requirement, battery charging characteristic, and time-varying electricity pricing are jointly considered in the integrated model to help manufacturers reap substantial benefits of demand response. The effectiveness of the proposed method is validated through numerical case studies, where the results indicate a 15.1% energy cost reduction compared to the benchmark scheduling scheme. [ABSTRACT FROM AUTHOR]

Nowadays, the thriving of the manufacturing ecosystems (ME) driven by the increasing competition in industrial markets, the ubiquitous implementation of intelligent systems, and the more frequent collaboration among manufacturing enterprises. During the practice of the system upgrade, it is increasingly noted that the redundancy of manufacturing resources and the inefficiency in resource configuration are the major obstacles to achieving satisfying value-creation within ME, which also result in cumbersome decision making (DM) in the problems of requirement-service configuration (RSC) and collaborative production. To address these issues, the research on resource recommendation and interaction is carried out. Firstly, the resource similarity models for autonomous resource filtering brace the whole DM mechanism in RSC and push the most suitable resource to the host automatically. Then, the interaction model provides a self-organized production mode without human intervention. The blindness, lag, and unfairness in the manual communication is eliminated by the Machine to Machine (M2M) interaction and automatic coordination. Besides, an NLP-based machine learning algorithm is introduced for quantifying semantic distance and measuring the differences between orders. Composed by these models, a total solution named Industry-Chat (I-Chat) emerges. With the help of that, production resources can be scheduled and managed autonomously and the order-based production processes could be promoted seamlessly. Thus, an improved industrial ecosystem with automatic DM and self-organization for future intelligent manufacturing is realized. The practicability of the research is verified by a case study. The results show that the production cost is reduced by 12%, the resource utilization rate is improved and its economic value is demonstrated. [ABSTRACT FROM AUTHOR]