Sustainable Manufacturing and Service Economics最新文献

The growing demand for sustainable materials as substitutes for conventional materials has led to the need for sustainable manufacturing practices that can effectively balance the use of limited resources and reduce environmental impact while maintaining economic viability and promoting human welfare. Therefore, the use of recycled polylactic acid (rPLA) in 3D printing could be a promising solution for reducing the cost and environmental impact of the use of virgin PLA in 3D printing. However, the low strength of recycled PLA-printed components remains a challenge. In addition, the use of PLA in 3D printing may pose environmental, cost, and social issues. Therefore, it is necessary to understand the mechanical properties and sustainability potential of recycled PLA. Hence, this study aimed to provide an overview of the potential use of recycled PLA in 3D printing. To achieve this goal, this study followed a systematic review approach and analysed published academic research papers to discuss the degradation of thermal and mechanical properties, challenges and opportunities of PLA recycling, and sustainability aspects of additively manufactured PLA products. Studies have shown that recycled PLA can be an alternative to virgin PLA if its properties can be appropriately modified and controlled. Researchers have used different methods to upgrade the properties of recycled PLA, such as using virgin and recycled waste blends, altering the printing process parameters, and utilising additives. In addition, the sustainability implications of using recycled PLA for 3D printing have not been adequately discussed. The findings indicate that the majority of research has concentrated on evaluating the environmental aspect, while paying scant attention to economic and social dimensions. Further research is required to understand the environmental, economic, and social impacts of recycled PLA on 3D printing. The findings of this study will assist practitioners and academics in thinking about using recycled materials and adapting them to obtain desired qualities.

Carbon policies are often limited to specific regions. To avoid stricter carbon emission requirements, companies relocate production to regions without carbon policies that offer a higher degree of flexibility. This effect is known as carbon leakage. To prevent carbon leakage, carbon tariffs are imposed on carbon emissions imported into regulated regions. We present a new model formulation for the design of a global supply network subject to carbon tariffs and location-specific carbon policies. Relevant carbon emissions are captured by a product carbon footprint. This model plans the locations of manufacturing plants and distribution centers and the transportation between them. In addition, we consider the choice of production technologies to enable carbon reduction. The objective is to minimize the net present value. To solve this supply network design model, we apply a fix-and-optimize heuristic. Our numerical study demonstrates that the heuristic provides high-quality solutions in a reasonable time frame. We indicate that combining carbon tariffs with location-specific carbon policies fundamentally changes the economic and environmental consequences for the network design. In addition, we examine how carbon tariffs and location-specific carbon policies affect the choice of carbon-reducing production technologies.

Industry 4.0 emphasizes real-time data analysis for understanding and optimizing physical processes. This study leverages a Predictive Maintenance Dataset from the UCI repository to predict machine failures and categorize them. This study covers two objectives namely, to compare the performance of machine learning algorithms in classifying machine failures, and to assess the effectiveness of deep learning techniques for improved prediction accuracy. The study explores various machine learning algorithms and finds the XG Boost Classifier to be the most effective among them. Long Short-Term Memory (LSTM), a deep learning algorithm, demonstrates its superior accuracy in predicting machine failures compared to both traditional machine learning and Artificial Neural Networks (ANN). The novelty of this study is the application and comparison of machine learning and deep learning models to an unbalanced dataset. Findings of this study hold significant implications for industrial management and research. The study demonstrates the effectiveness of machine learning and deep learning algorithms in predictive maintenance, enabling proactive maintenance interventions and resource optimization.

Digital technologies offer diverse opportunities for small and medium-sized enterprises (SMEs). However, for an effective use of these technologies, a well-defined managerial strategy and implementation roadmap are essential. These elements ensure the efficient application of digital tools, ultimately leading to improved organizational performance. Organizations striving for business excellence aim to establish sustainability, drive transformation, and continuously improve their adaptable, dynamic capabilities. While customer requirements and needs should remain a primary focus, it is equally important to assess potential internal and external incidents through various scenarios.

Systems Thinking approaches have been promisingly used in business model research, innovation, and development in helping to understand the complexity inherent in dynamic situations. Despite its potential, there is a scarcity of literature exploring Systems Thinking and associated network analysis specifically addressing digital transformation techniques in SMEs.

This research study aims to fill this gap by providing a comprehensive insight into digital transformation strategies for SMEs. Employing a results-oriented, strategic Systems Thinking simulation, the study explores the identification of digital transformation strategies by evaluating the interrelations and performance of various departmental functions. With that best actions for achieving an effective, stable, and enhancing organization could be determined.

The integration of blockchain technology in the Industrial Internet of Things (IIoT) for sustainable supply chain management in the context of Industry 4.0 offers several potential benefits. A public and auditable record of the environmental impact of each supply chain stage can be made using blockchain technology. A more streamlined and effective supply chain is made possible by blockchain's decentralized structure. Delays, mistakes, and the need for middlemen are decreased by real-time access to a shared ledger. IIoT devices like sensors and RFID tags can provide real-time data on the location, condition, and environmental parameters of goods. Blockchain can then be used to record and incentivize sustainable practices, such as reducing energy consumption or minimizing waste. The integration of blockchain with IIoT can develop the supply chain management for enabling real-time tracking of goods, optimizing inventory management, and ensuring compliance with sustainability standards. The paper provides a comprehensive overview of the key challenges facing traditional supply chains and how the combined use of Blockchains and IIoT technologies. The review also evaluates the environmental, social, and economic implications of adopting Blockchain-enabled IIoT solutions in supply chain operations. Furthermore, the review assesses the current state of research and development, identifying gaps in existing literature and proposing avenues for future exploration. As a results, by highlighting the synergies between these technologies, it seeks to inspire further innovation and adoption, ultimately fostering a more resilient, transparent, and environmentally conscious industrial ecosystem.

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.