Call for papers for a special volume of the Journal of Cleaner Production: Sustainable product lifecycle management based on smart enabling technologies
摘要截稿:
全文截稿: 2018-10-30
影响因子: 7.246
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 环境科学与生态学 - 1区
• 小类 : 工程:环境 - 1区
• 小类 : 环境科学 - 1区
• 小类 : 绿色可持续发展技术 - 1区
Overview
Sustainable production is a more competitive strategy for manufacturing enterprises as its implementation can help manufacturers to achieve overall development plans, reduce resource use and pollution along the whole lifecycle (Roy and Singh, 2017). The recent advance in smart enabling technologies, such as Internet of Things (IoT), Cyber-Physical System (CPS), Cloud Computing (CC), Artificial Intelligence (AI), Big Data Analytics (BDA), Digital Twin, etc. (Kang et al., 2016; Mittal et al., 2016) have greatly stimulated the development of smart manufacturing. As pointed by Kusiak (2017), smart manufacturing can make industry more efficient, profitable and sustainable.
Over the past years, industries together with researchers and policy makers worldwide have increasingly advocated an upcoming fourth industrial revolution. Many countries are developing towards this goal in the coming years. For example, the German government promotes the computerization of manufacturing industries in their Industry 4.0 (I4.0) program (Kagermann et al., 2013), while in the United States, smart manufacturing initiatives such as the industrial internet (Evans and Annunziata, 2012), drive and facilitate the broad adoption of manufacturing intelligence. Other major manufacturing countries such as Japan and Korea have established national programs on smart manufacturing, namely smart factory (Thoben et al., 2017). Furthermore, Sweden started the research and innovation projects of production (Produktion 2030) (Vinnova, 2013), and the Chinese government has implemented the strategy of China Manufacturing 2025 (The state council, 2015).
In smart manufacturing environment, with the increasing availability of sensors and smart machine tools, massive data are available in manufacturing systems from the system-level to the operation-level with various degrees of complexity, even can be obtained during all phases of product lifecycle. Since the lifecycle big data are linked to real-time events, they can support the smart manufacturing and contribute significantly in cleaner production if analysed properly. Using these data to optimize the process of product lifecycle management (PLM) is an increasingly important objective for manufacturing enterprises to develop more sustainable operations, to achieve greener and more sustainable production (and service/maintenance), and to improve their competitive advantage, which is considered as the ultimate goal for I4.0. For example, Hazen et al. (2016) explored the role of big data for supply chain sustainability. The role of big data in speeding up delivery time and improving Research & Development for semiconductor industry was analysed by Jacobson and Santhanam (2016). The impact of big data on world-class sustainable manufacturing involves green product design and green production were explored by Dubey et al. (2016).
In 2014, the International Journal of Production Economics sponsored a special issue on ‘Big data for service and manufacturing supply chain management (SCM)’ (Huang et al., 2015), and the Computers & Industrial Engineering sponsored a special issue on ‘Big Data and Predictive Analytics Application in Supply Chain Management’ (Gunasekaran, 2015). These two special issues were collectively devoted to publishing emerging technologies, theories, and significant insights related to big data in service and manufacturing SCM, aiming to transfer these two sectors into a level that is more efficient and economic. In addition, by using big data and predictive analytics to take the current supply chain theory and practice to the next level of excellence in terms of supporting suitable supply design and operations. To our best knowledge, most articles related to big data in other publications such as IEEE Transactions on Services Computing, IEEE Transactions on Industrial Informatics, Information Sciences and Information Fusion etc. were mainly focus on the issues such as security, privacy, computation and communication of big data, and the application of big data in social network, health care, transportation system, e-commerce, geographic information system.
In the Journal of Cleaner Production (JCLP), majority related articles were published in Special Volume on Improving natural resource management and human health to ensure sustainable societal development based upon insights gained from working within ‘Big Data Environments’(Song et al., 2017). Many of them focused on economic and social applications of big data. Only a few of them discussed big data in smart manufacturing. Among all the articles published on JCLP, very few articles addressed the other highly relevant technologies like IoT, AI, Cloud, etc. Big data related articles Zhang et al. (2017a) and Zhang et al. (2017b) were two of the very few articles published on JCLP systematically discuss this topic and received a lot of attention, particularly Zhang et al. (2017b) was ranked by Essential Science Indicators (ESI) as hot paper and highly cited paper in the field of Engineering.
Based on abovementioned publications and special issues, there are some issues that we see now as a team that are not properly addressed. These issues have been strong and challenging to carry on but have not been properly covered until now. For example:
- Many existing publications only focused on applying the smart enabling technologies to facilitate the implementation of smart manufacturing paradigm, however, the sustainability aspect of the whole PLM processes in the context of smart manufacturing were seldom involved.
- Many other special issues only focused on applying one of the smart enabling technologies, i.e. big data, to improve and optimize the decision-making processes of specified lifecycle stages. The research on integrated applications of the various smart enabling technologies, to enhance the sustainability of the lifecycle management was almost vacant.
- The existing research on smart technologies enabled PLM only involved a limited lifecycle stages (e.g. production and maintenance stages). Few studies have performed research covering smart enabling technologies in the whole lifecycle (especially lacking in design and recovery stages) of decision-making processes.
These issues lead to some research questions such as:
- What are the important elements and enablers for sustainable smart manufacturing and sustainable PLM?
- How to effectively utilize the smart enabling technologies to realize the sharing and integration of the data and manufacturing in different lifecycle stages, and then to facilitate sustainable PLM?
- What benefits can be brought to the involved industrial communities by penetrating and using smart enabling technologies into the whole lifecycle?
Aiming to fill these gaps, this special issue attempts to bring together researchers, industry engineers and administrators resorting to the state-of-the-art theories, methods, technologies as well as ideas to effective integration and application of various smart enabling technologies in the whole lifecycle management and decision-making processes, to uncover the real potential of lifecycle big data, to achieve data and knowledge sharing among all lifecycle stages, and finally to promote the realization of sustainable production and sustainable PLM.
Under the promotion of the smart enabling technologies, data and knowledge share among various lifecycle management sectors can be effectively facilitated, and then more reasonable and precise manufacturing and lifecycle management and decision-making can be achieved. In an effort to increase the awareness and highlight the works in which lifecycle big data forms the cornerstone of future industries, JCLP offers a special volume on theory and methodology for lifecycle big data-driven decision-making in smart manufacturing to improve the effectiveness and efficiency of product/service design, of sustainable and cleaner production, of production resource and service optimization, of smart and energy-aware maintenance/service decision-making, in terms of quality, productivity, production risk, cost, time, service level, resource and energy consumption, etc.
Theme 1: Design
In the context of smart manufacturing, the isolated lifecycle data that influence the product, service, and smart manufacturing system design can be integrated and analysed to generate important insights about product and service design, and manufacturing system improvements and innovations. Therefore, the use of BDA offers further opportunities to accurately perceiving and predicting market demands, to accelerate their development, and to design a sustainable product, product service system (PSS), and/or manufacturing system. It can also help designers to identify the most important and valuable features based on concrete customer inputs as well as designs that minimize production costs, and harness consumer insights to reduce development costs. Furthermore, BDA can also assist designers in improving product and service design for purposes, such as design for reliability, maintenance, remanufacturing, environment, reuse, recycle, and so on. For this Special Volume, authors are invited to submit papers about innovative strategies, methodologies and models for product/service/manufacturing system design. Topics of interest in this theme include but are not limited to:
- Theoretical and conceptual frameworks, foundations and origins of the smart manufacturing paradigm
- Trans-disciplinary approaches that involve different stakeholders to design smart manufacturing
- Eco-design and Eco innovation for sustainability
- Sustainability innovations & Research and Development
- New concepts, models and methods for sustainable product, sustainable PSS, smart PSS, greener smart manufacturing system design
- Design approaches for smart PSS
- Digital twin-driven product, manufacturing system and service design
- Innovative methodologies and smart methods for design scheme selection and optimization in the conceptual design phase
- Innovative strategies, methodologies and models for design for reliability, maintenance, remanufacturing, environment, reuse, recycle
Theme 2: Production/manufacturing
In the smart manufacturing environment, there are dozens of monitoring and control points deployed along the whole production processes, i.e. from the moment raw materials are delivered to the shop-floor to the moment final products are packaged. Therefore, large amount of production data is produced and collected. Manufacturers can analyse these data by using BDA to improve the management and the production performance of the overall manufacturing processes for complex products, such as reducing process flaws, optimizing process parameters and shop-floor logistics, improving production quality and productivity, etc. Such managerial improvement and optimization may greatly contribute to reducing energy consumption, waste, carbon emission and environmental impact. Currently, theories and applications of the innovative ways of manufacturing can be further studied and analysed for the better resource efficiency, e.g., the co-engaging manufacturing, collaborative manufacturing, additive manufacturing, green supply chain, etc. Topics of interest in this theme include but are not limited to:
- Innovative strategies and methodologies for production big data monitoring, acquisition and data quality management
- Innovative strategies and models for sustainable production, green manufacturing, co-engaging manufacturing, collaborative manufacturing, additive manufacturing, green supply chain management
- Real-time decision-making strategies and models for material handling and shop floor logistic optimization
- New data mining, knowledge discovery, machine learning, deep learning models and methods for manufacturing big data analyse
- Smart and innovative strategies and methods for production performance optimization
- Data-based forecasting methods and predictive manufacturing technology
- Data-driven intelligence production decision support systems
- Digital twin job-shop
- Big data and AI driven manufacturing intelligence
- CPS-based smart product line and factory
- Energy-aware job-shop scheduling
- Cloud-based production resource and service management
- Manufacturing big data visualization
- Industrial case studies on big data
Theme 3: Service and maintenance
In the smart manufacturing environment, the big data of product operation status can be monitored and gathered by the manufacturers and customers in a timely fashion. These big data are an important asset for maintenance and service decision-making. For instance, by analysing the product operation status data, manufacturers and customers can determine whether product performance is decreasing. These analyses can help manufacturers and customers to accurately predict when the products will fail. Therefore, the early fault warning service, predictive maintenance service, and intelligent spare part prediction service can be provided. These services can help manufacturers and customers to transition to more sustainable production and consumption. Meanwhile, sudden breakdowns of the product and excessive production can be effectively avoided, and material consumption can also be reduced. These can contribute to enhanced productivity, extending the service life of product, reducing production and maintenance costs, and finally achieving sustainable production and reducing environmental impact. Topics of interest in this theme include but are not limited to:
- Smart methods and models for fault identification, diagnosis and prediction
- Smart methods and models for spare parts management
- Innovative strategies and modes for maintenance service
- New models and methods for reliability analysis of system level
- New business offerings and PSS models
- Smart services and solutions integrated in PSS
- Innovative strategies for developing and implementing sustainable production and consumption
- Industrial case studies on lifecycle big data based fault identification, diagnosis and prediction
- Recovery and reuse strategies and processes for smart PSS
- Influence of BDA in maintenance and service energy consumption analysis