Sustainable Manufacturing and Service Economics最新文献

While industries globally strive to meet the 1.5-degree target set forth in the Paris Agreement, the transport sector, as the largest emitter of carbon, has yet to sufficiently reduce its emissions. This study utilizes panel data spanning from 2007 to 2022 in China and employs a time-varying difference-in-differences (DID) method to examine the causal impact of the carbon emission trading scheme (ETS) on green total factor productivity (GTFP) within the transport industry. Our empirical analysis yields several key findings: First, the implementation of the ETS policy significantly enhances GTFP in the transport sector within pilot areas. Second, decomposition of GTFP indicators reveals that the ETS primarily improves green scale efficiency and fosters green innovation, driven by technological advancements and optimal resource allocation. Third, heterogeneity analysis demonstrates a notably stronger positive effect of the ETS on transport sector GTFP in the eastern region, with insignificant impacts observed in the central and western regions. Through rigorous robustness tests, these conclusions are consistently upheld. In sum, this paper provides robust empirical evidence supporting the efficacy of ETS in reducing emissions and presents valuable policy implications for fostering the green transition of the transport sector.

Supply chain models frequently tackle manufacturing issues but must also account for the distinctive nature of the disease. Conversely, most epidemiological models solely concentrate on the disease’s spread but must address logistical challenges. The medical supply chain encounters numerous problems during a pandemic, requiring adaptation through pivoting strategies. For instance, when the COVID-19 outbreak began, several nations prohibited the export of medical supplies, including personal protective equipment (PPE). Consequently, in times of crisis, many countries adopt a localization strategy that encourages domestic companies to adapt their operations and produce medical items. Nevertheless, an interconnected system is essential to align suppliers with the actual demand for medical supplies. This study focuses on the design of a game model for the supply chain that considers manufacturers’ equilibrium behaviors in response to the real demand for medical items. We propose a game model that incorporates both the medical supply chain and the unique characteristics of pandemics. Various decisions are taken into account, such as production volume, actual demand for medical products, price, distribution of medical supplies, and investment costs in manufacturing technologies. To determine the Nash Equilibrium solutions for the proposed game model, the Variational Inequality (VI) theory is implemented.

Green supply chain management (GSCM) is an emerging concept of modern supply chain management (SCM) that integrates eco-friendly and ethical environmental concerns with the traditional supply chain by reducing the negative impacts of unsustainable manufacturing practices. Developed countries have already adopted different sustainable SCM practices. However, despite being one of the significant sources of export earnings in emerging economies like Bangladesh, the apparel manufacturing industry is still lagging in the case of GSCM implementation. This study, thereby, utilized an integrated multi-criteria decision-making (MCDM) approach, including gray theory and decision-making trial and evaluation laboratory (DEMATEL) method to identify, prioritize, and examine the relations among the critical success factors (CSFs) to implement GSCM practices in the Bangladeshi apparel manufacturing industry. The study initially identified the CSFs from the literature review. After expert validation, sixteen significant CSFs were finally analyzed by the gray-DEMATEL method. The findings revealed that 'demand from buyers', 'economic and tax benefits', and 'government rules and regulations' are the three most prominent CSFs to implement GSCM practices in the apparel manufacturing industry. The cause-effect relations among the CSFs were later explored, which indicated 'Economic and tax benefits' to be the most influencing and 'Supplier training and cooperation' to be the most influenced CSF. The study insights can potentially guide apparel industry managers in successfully implementing GSCM practices toward achieving long-term sustainability and sustainable development goals (SDGs).

Disruptive events with damaging consequences afflict supply chains across industries. The survival of the business and its consequent recovery depend on the supply chain's resilience. This exploratory article discusses how technology-driven real-time decision-making in a connected supply chain achieves intended business outcomes of resilience, agility, and visibility. Based on an Integrative Literature Review and adopting a Design Science Research Methodology (DSRM), we propose a distributed approach for real-time inferencing in edge near the data sources for rapid autonomous decisioning and recovery planning under disruption. We develop a framework for building resilience in the supply chain using real-time distributed information sharing in a collaborative partner ecosystem. Visibility across the supply chain is ensured with a Digital Control Tower by making information available to any connected node for synchronized action. The important contribution of this research is building a real-time decision framework for sustainable resilience-building in resource-constrained organizations unable to invest in big data and enterprise systems. A set of design propositions following the CIMO framework is expected to help scholars and practitioners alike. A research agenda is provided for the researchers to take forward hypothesis formulation and empirical validation on the basis of the propositions. (194 words)

A virtual representation of a physical procedure or product is called digital twin which can enhance efficiency and reduce costs in manufacturing process. Utilizing the digital twin, production teams can examine various data sources and reduce the number of defective items to enhance production efficiency and decrease industrial downtime. Digital Twin can be utilized to visualize the asset, track changes, understand and optimize asset performance throughout the analysis of the product lifecycle. Also, the collected data from digital twin can provide the complete lifecycle of products and processes to optimize workflows of part production, manage supply chain, and manage product quality. The application of digital twin in smart manufacturing can reduce time to market by designing and evaluating the manufacturing processes in virtual environments before manufacture. Comprehensive simulation platforms can be presented using digital twins to simulate and evaluate product performances in terms of analysis and modification of produced parts. Commissioning time of a factory can also be significantly reduced by developing and optimizing the factory layout using the digital twin. Also, the productivity of part manufacturing can be enhanced by providing the predictive maintenance and data-driven root-cause analysis during part production process. In this paper, application of digital twin in smart manufacturing systems is reviewed to analyze and discuss the advantages and challenges of part production modification using the digital twin. So, the research field can advance by reading and evaluating previous papers in order to propose fresh concepts and approaches by using digital twins in smart manufacturing systems.

Growing corporate social responsibility requirements have compelled manufacturing organizations to embed sustainability in their business models. Consequently, firms focus on designing and producing products using sustainable means to bring new products to the market that are environmentally sustainable and socially responsible. However, to satisfy investors, it is also necessary to focus on financial priorities. This research presents a model with relationships between an organization's economic orientation, sustainable product design activities, and firm innovation performance to study how financial priorities affect sustainability initiatives in new product development and innovation outcomes. The empirical evidence is drawn from a panel survey of 282 US manufacturing firms. The results suggest strong interrelationships among the three constructs under investigation. The practitioners can pursue economic orientation and still focus on sustainable product design to achieve innovation performance.

Warehouses are crucial components of the logistics industry because their operational efficiency determines the operational efficiency of the logistics. A warehouse was traditionally thought to be a location where inventory was stored and held before being shipped to customers or distributed to retailers for sale. With the introduction of Industry 4.0 technologies, the warehouse's role has changed dramatically, the scope of warehouse operation has expanded, and the concept of smart warehouse, which denotes increased automation of traditional warehouse functions, has been introduced. Smart warehouses aim to improve overall service quality, productivity, and efficiency while lowering costs and failures. It has emerged as a result of smart technologies, igniting a wave of industry transformation with the potential to bring about dramatic changes. We used the bibliometric analysis method in this paper to analyse and draw conclusions from 295 articles retrieved from the Scopus database between 2017 and June 2022. The methodology used in this paper is divided into four steps: data collection, data analysis, data visualisation, and interpretation. The current study aims to provide a comprehensive understanding of smart warehouses using the Bibliometric R-package and VOS viewer software.

The present research investigates the influence of B2B firms' supply chain resilience orientation on achieving sustainable supply chain performance via firms' adaptive capability. Furthermore, this research also tests the moderating role of B2B firms' customer engagement between adaptive capability and sustainable supply chain performance. The proposed conceptual model was analyzed using Partial Least Squares (PLS)-Structured Equation Modeling (SEM) by applying survey data collected from 276 samples of 138 B2B firms. Our results indicate that B2B firms' supply chain resilience orientation positively and significantly influences sustainable supply chain performance via B2B firms' adaptive capability. Based on our analysis, the positive effect of B2B firms' adaptive capability on sustainable supply chain performance is evident only in high customer engagement. Drawing from the literature and theoretical paradigm, the first contribution of this study is the formation of a conceptual model of sustainable supply chain performance in the context of B2B firms. The second key contribution is examining how B2B firms' adaptive capability is influenced by both B2B firms' supply chain resilience orientation and sustainable supply chain performance. The outcome of this study also adds further theoretical insight by analyzing the moderating effect of B2B firms' customer engagement on the relationship between adaptive capability and sustainable supply chain performance. The findings from this research play a significant role in understanding the importance of next-generation supply chains that require B2B firms to invest considerable resources in achieving sustainable supply chain performance to remain competitive in their respective industry. Therefore, B2B firms need to embrace supply chain resilience orientation to achieve sustainable supply chain performance in response to the pandemic resulting from COVID-19.

Due to global warming and the salinity of drinkable natural resources, water scarcity has become a significant impediment to the development of many regions of the world, prompting the development of novel desalination techniques for brackish and seawater. The only way to satisfy society's freshwater demands is to transform abundant seawater into potable water by desalination. Numerous research efforts have been effective in establishing large desalination plants. However, significantly fewer attempts are made for dry regions where low-cost, maintenance-free, and low-operational-cost approaches are required. Both rapidly developing and underdeveloped nations struggle to provide their populations with pure drinking water. In the desalination sector, CO₂ emissions and significant ecological problems have increased. The desalination sector may be sustainable by integrating renewable energy and using proper brine disposal techniques. In this review, various desalination systems that incorporate renewable energy sources, with an emphasis on solar energy, are examined. The main objective of this paper is to explain what solar desalination is, why it is performed, and what techniques can be used to make desalination more structured and cost-effective. In addition, this study provides a comprehensive review of all the solar desalination systems, indirect and direct, along with plant-specific technical data. In addition, the efforts that have been made to evaluate the economic viability of each desalination technology and the elements that determine its cost are discussed.

Artificial Intelligence (AI) and Machine learning (ML) represents an important evolution in computer science and data processing systems which can be used in order to enhance almost every technology-enabled service, products, and industrial applications. A subfield of artificial intelligence and computer science is named machine learning which focuses on using data and algorithms to simulate learning process of machines and enhance the accuracy of the systems. Machine learning systems can be applied to the cutting forces and cutting tool wear prediction in CNC machine tools in order to increase cutting tool life during machining operations. Optimized machining parameters of CNC machining operations can be obtained by using the advanced machine learning systems in order to increase efficiency during part manufacturing processes. Moreover, surface quality of machined components can be predicted and improved using advanced machine learning systems to improve the quality of machined parts. In order to analyze and minimize power usage during CNC machining operations, machine learning is applied to prediction techniques of energy consumption of CNC machine tools. In this paper, applications of machine learning and artificial intelligence systems in CNC machine tools is reviewed and future research works are also recommended to present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes. As a result, the research filed can be moved forward by reviewing and analysing recent achievements in published papers to offer innovative concepts and approaches in applications of artificial Intelligence and machine learning in CNC machine tools.