Applying emergy and decoupling analysis to assess the sustainability of China’s coal mining area
استفاده از تحلیل اضطراری و جداسازی برای ارزیابی پایداری منطقه استخراج زغال سنگ چین-2020
The sustainable development of coal mining area continues to be one of the most topical issues in the world. Taking Shainxi Province as a case, this study applies emergy and decoupling analysis to build a multi-index sustainability evaluation system and constructs an emergy decoupling index to investigate the sustainability of a coal mining area in China during 2006e2015. It overcomes the problem of the unification of the traditional evaluation index system and integrates the influence of economic development, resources, the environment, and energy. The study finds that the coal mining area still depends on its coal resources. The sustainability of the coal mining area is still at a low level, and it is not sustainable in the long term. The economic growth still has a strong negative decoupling from the environmental loss. Energy management system and circular economic system should be built to improve the coal mining area’s sustainability. In the long run, the coal mining industry should gradually be abandoned. Based on China’s growing energy consumption, the findings of this study may not only serve as a reference for management to improve the sustainability of the coal mining areas but also to address China’s energy shortage problem.
Keywords: Sustainability | Emergy analysis | Decoupling | Coal mining area
Economic feasibility valuing of deep mineral resources based on risk analysis: Songtao manganese ore - China case study
ارزیابی امکان سنجی اقتصادی منابع معدنی عمیق بر اساس ریسک تجزیه و تحلیل: سنگ معدن منگنز Songtao - مطالعه موردی چین-2020
The exploitation of deep mineral resources is an inevitable choice under economic development and resource shortage. Assessing the economic feasibility of deep mineral resource exploit projects is a prerequisite for resource industry development. Mining industry have some problems influence its economic feasibility, including long mining period, high infrastructure investment and lack flexibility, and have risks of geology instability and economic reserve degrade. On the other hand, with the increase of the buried depth of mineral resources, some problems have intensified the uncertainty of the profit of deep resource utilization project, such as high stress, high lithology, high temperature environment, and increase of upgrading cost. Net Present Value (NPV) and Internal Rate of Return (IRR) are traditional economic evaluation means which difficult to identify and assess risks precisely. Decoupled Net Present Value (DNPV) provides an efficiency tool to separate the time value and risk cost which is helpful to finds the real value of projects. A manganese mining project which is located Guizhou province, China is analyzed, paper choices several mainly risks of influence expected revenue to analysis project feasibility based on the DNPV technology, which includes the thickness of ore body, ore grade, market price, operation cost and nature disaster. The cost of potential environmental risk (carbon emission cost) also is analyzed. Paper constructs a risk management framework by risk identify, assess and classification, and analyzes the corresponding measures to reduce risk costs. The mainly risk cost of study case from market price shock and unexpected ore grade decline, which accounting for 80% of the total risk cost. In the process of deep mineral resources exploit, effective cost control measures can reduce the risk cost to a certain extent, including improving productivity, reducing unit cost of ore, improving mine sustainability and exploration accuracy. Green mineral construction is a feasible direction of deep resource utilization. For improve the accuracy of economic feasibility evaluation of deep mineral resources utilization, further improvement is needed in the selection and construction of different risk assessment model.
Keywords: Deep mining | Risk value assess | DNPV | Risk management | Songtao manganese
Manufacturing big data ecosystem: A systematic literature review
ساخت اکوسیستم داده های بزرگ: مروری بر ادبیات سیستماتیک-2020
Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.
Keywords: Smart manufacturing | Big data | Cloud computing | Cloud manufacturing | Internet of things | NoSQL
System architecture for blockchain based transparency of supply chain social sustainability
معماری سیستم برای شفافیت مبتنی بر بلاکچین پایداری اجتماعی زنجیره تأمین-2020
Social sustainability is a major concern in global supply chains for protecting workers from exploitation and for providing a safe working environment. Although there are stipulated standards to govern supply chain social sustainability, it is not uncommon to hear of businesses being reported for noncompliance issues. Even reputable firms such as Unilever have been criticized for production labor exploitation. Consumers now increasingly expect sellers to disclose information on social sustainability, but sellers are confronted with the challenge of traceability in their multi-tier global supply chains. Blockchain offers a promising future to achieve instant traceability in supply chain social sustainability. This study develops a system architecture that integrates the use of blockchain, internet-of-things (IoT) and big data analytics to allow sellers to monitor their supply chain social sustainability efficiently and effectively. System implementation cost and potential challenges are analyzed before the research is concluded.
Keywords: Blockchain | Social sustainability | Multi-tier supply chain | Supply chain sustainability | Traceability
Role of big data and social media analytics for business to business sustainability: A participatory web context
نقش تجزیه و تحلیل داده های بزرگ و رسانه های اجتماعی برای پایداری تجارت از مشاغل: زمینه وب مشارکتی-2020
The digital transformation is an accumulation of various digital advancements, such as the transformation of the web phenomenon. The participatory web that allows for active user engagement and gather intelligence has been widely recognised as a value add tool by organisations of all shapes and sizes to improve business productivity and efficiency. However, its ability to facilitate sustainable business-to-business (B2B) activities has lacked focus in the business and management literature to date. This qualitative research is exploratory in nature and fills this gap through findings arising from interviews of managers and by developing taxonomies that highlight the capability of participatory web over passive web to enable different firms to engage in business operations. For this purpose, two important interrelated functions of business i.e. operations and marketing have been mapped against three dimensions of sustainability. Consequently, this research demonstrates the ability of big data and social media analytics within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities. The research findings will be useful for both academics and managers who are interested in understanding and further developing the business use of participatory web tools to achieve business sustainability. Hence, this may be considered as a distinct way of attaining sustainability.
Keywords: Participatory web | Marketing and operations | Big data | Social media analytics | Business sustainability | Business-to-business (B2B)
Blockchain-based life cycle assessment: An implementation framework and system architecture
ارزیابی چرخه زندگی مبتنی بر بلاکچین: چارچوب پیاده سازی و معماری سیستم-2020
Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology oﬀers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with internet-of-things (IoT) and big data analytics and visualization can help organizations achieve operational excellence in con- ducting LCA for improving supply chain sustainability. This research develops a framework to guide the im- plementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, IoT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications.
Keywords: Blockchain | Life cycle assessment | Supply chain sustainability | Environmental sustainability | Operational excellence
Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
تجزیه و تحلیل داده های بزرگ به عنوان یک رویکرد برتری عملیاتی برای افزایش عملکرد پایدار زنجیره تأمین-2020
Operations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.
Keywords: Big data analytics | Operational excellence | Dynamic capability view | Supply chain sustainability | Learning performance
Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications
دستیابی به عملکرد پایدار در زنجیره تأمین کشاورزی داده محور: مروری بر تحقیقات و کاربردها-2020
The lack of industrialization, inadequacy of the management, information inaccuracy, and inefficient supply chains are the significant issues in an agri-food supply chain. The proposed solutions to overcome these challenges should not only consider the way the food is produced but also take care of societal, environmental and economic concerns. There has been increasing use of emerging technologies in the agriculture supply chains. The internet of things, the blockchain, and big data technologies are potential enablers of sustainable agriculture supply chains. These technologies are driving the agricultural supply chain towards a digital supply chain environment that is data-driven. Realizing the significance of a data-driven sustainable agriculture supply chain we extracted and reviewed 84 academic journals from 2000 to 2017. The primary purpose of the review was to understand the level of analytics used (descriptive, predictive and prescriptive), sustainable agriculture supply chain objectives attained (social, environmental and economic), the supply chain processes from where the data is collected, and the supply chain resources deployed for the same. Based on the results of the review, we propose an application framework for the practitioners involved in the agri-food supply chain that identifies the supply chain visibility and supply chain resources as the main driving force for developing data analytics capability and achieving the sustainable performance. The framework will guide the practitioners to plan their investments to build a robust data-driven agri-food supply chain. Finally, we outline the future research directions and limitations of our study.
Keywords: Agriculture supply chain | Food supply chain | Sustainability | Sustainable performance | Supply chain visibility | Big data | Blockchain | Data analytics | Supply chain resources
Use of a big data analysis technique for extracting HRA data from event investigation reports based on the Safety-II concept
استفاده از روش تجزیه و تحلیل داده های بزرگ برای استخراج داده های مجموعه فعالان حقوق بشر از رویداد گزارش تحقیقات بر اساس مفهوم ایمنی-II-2020
The safe operation of complex socio-technical systems including NPPs (Nuclear Power Plants) is a determinant for ensuring their sustainability. From this concern, it should be emphasized that a large portion of safety significant events were directly and/or indirectly caused by human errors. This means that the role of an HRA (Human Reliability Analysis) is critical because one of its applications is to systematically distinguish error-prone tasks triggering safety significant events. To this end, it is very important for HRA practitioners to access diverse HRA data which are helpful for understanding how and why human errors have occurred. In this study, a novel approach is suggested based on the Safety-II concept, which allows us to collect HRA data by considering failure and success cases in parallel. In addition, since huge amount of information can be gathered if the failure and success cases are simultaneously involved, a big data analysis technique called the CART (Classification And Regression Tree) is applied to deal with this problem. As a result, it seems that the novel approach proposed by combining the Safety-II concept with the CART technique is useful because HRA practitioners are able to get HRA data with respect to diverse task contexts.
Keywords: Human reliability analysis | Nuclear power plant | Safety-II | Classification and regression tree | Event investigation report
Identification of drivers, benefits, and challenges of ISO 50001 through case study content analysis
شناسایی درایورها ، مزایا و چالشهای ISO 50001 از طریق تجزیه و تحلیل محتوای موردی-2020
An expanding body of research is defining drivers, benefits, and challenges of adopting ISO 50001 energy management systems. The Clean Energy Ministerial’s Energy Management Leadership Awards program requires ISO 50001-certified organizations to develop case studies of their implementation experience. 72 recent case studies spanning multiple economic sectors provide a unique global look at implementation from certified organizations’ perspectives. This dataset was investigated through content analysis of phrases related to motivations and goals, the role of management and the organization, benefits achieved, keys to success, and challenges. This paper presents findings from this quantitative analysis of “codes” assigned to phrases that capture their meaning. While organizations adopted ISO 50001 for different motives and saw myriad benefits beyond energy savings and associated greenhouse gas emissions reductions, commonalities exist. The most frequently identified drivers are existing values and goals, environmental sustainability, and government incentives or regulations. Findings also include: obtaining and sustaining top management support is critical; top benefits mentioned are cost savings, productivity, and operational improvements; and the primary barrier is lacking a culture of energy management. Policymakers and others looking to accelerate ISO 50001 uptake can use these findings to highlight benefits and incentives that will resonate with corporate decisionmakers worldwide
Keywords: Energy management | ISO 50001 | Content analysis | Energy savings | Greenhouse gas emissions