Sustainable Manufacturing and Service Economics最新文献

The supply chain (SC) as the most significant factor influencing competitive advantage has a great impact on the organization's life and progress. Considering the expiration date of products in supply chain modeling is essential for ensuring operational efficiency, reducing waste, maintaining product quality, and ultimately driving business success. Therefore, this paper provides a two-level supply chain including manufacturer and distributor then chain modeling with considered conditions and applying the concept of Markowitz's theory. Finally, drawing the optimal cost curve of producer and distributor as an efficient frontier. For this purpose, the main contribution of the paper is determining the optimal cost of each level of the chain if the cost of another level is known and increasing the power of analysis. The proposed model applied in Darupakhsh Manufacturing Company and Mahya Daroo Distribution Company, a two-echelon supply chain design, including a manufacturer and a distributor is developed. At first, this chain is modeled, and then, implementing the concept of Markowitz theory, the optimal cost curve of producer and distributor is drawn as an efficient frontier. Findings show it is found that the optimal cost of the two echelons of the chain is inversely correlated and decreases with the increase of another one. Furthermore, it is found that the producer warehouse limit is redundant and has no effect on the final response. Also, with the increase in the cost of the manufacturer, the number of deliveries decreases, and instead, the number of products in each delivery increases, which results in a reduction in the costs of the distributor.

The goal of this review study is to look into the most important sustainable practices that fresh food startups can use to improve their resilience and environmental responsibility. It also aims to identify the difficulties and impediments these companies face when putting these sustainable strategies into effect. Fresh food startups can integrate several sustainable techniques and innovations into their operations, as identified by the study. These include using IoT technology to enhance supply chain management and implementing packaging with a modified environment to keep goods fresher for longer. The review also emphasizes the difficulties these firms have had putting these strategies into practice. The review's findings have important real-world ramifications for fresh food entrepreneurs. Through the development and improvement of their sustainability initiatives, they can make their business operations more robust and ecologically conscious. This review adds to the corpus of current knowledge by methodically analyzing the most recent advancements in fresh food supply chains, particularly with regard to startups. It illuminates the particular difficulties they encounter in promoting sustainability, making it an important and novel contribution to the area.

The world is adopting the Industry 5.0 paradigm to increase human centricity, sustainability, and resilience in efficient, optimized, and profitable manufacturing systems. With benefits, however, come increased risks of economic and physical loss, driving the need for continuous improvement of Industry 5.0 cybersecurity. Implementation and advancement of adequate cybersecurity have created challenges that have been identified in the literature. In this study, key Industry 5.0 cybersecurity challenges and related sub-challenges are highlighted based on a literature review. Graph Theory and Matrix Approach (GTMA) is employed to analyze the challenges and determine relative importance based on permanent values of the variable permanent matrix (VPM). The results identify the most important Industry 5.0 cybersecurity challenges and reveal Industry 5.0 firms should primarily concentrate on supply chain vulnerabilities to decrease data loss and hacking in the organization's supply chain network. This study also recommends that executives and lawmakers acquire knowledge regarding cybersecurity challenges and prepare to deal with them. Addressing these and other subsequently prioritized challenges—the top five rounded out with emergent cybersecurity trends, non-availability of cybersecurity curriculum in education, embedded technical constraints, and absence of skilled employees and training—will lead the methodical development of holistic, robust cybersecurity programs. Firms accepting of this reality may implement such programs to mitigate evolving cyber-risk towards harnessing and sustaining the benefits of novel Industry 5.0 technologies.

Analyzing design principles and supply chain sustainability are critical for organizational success in today's competitive marketplace. The process of evaluating an organization's supply chain design principles and manufacturing sustainability entails incorporating various sources of information, which are typically uncertain, incomplete, and subjective in nature. Particularly when various organizations are confronted with significant principles such as a lack of interoperability, decentralization, virtualization, real-time and capabilities, service orientation, and so on. Using graph theory and a matrix approach (GTMA), this article attempts to analyze the interaction between design principles-related factors with a focus on operational excellence, growth and sustainability in supply chains by considering the business of Industry 4.0. We have considered electrical equipment manufacturing Industry 4.0 as test ground. Based on a literature review and expert opinions, we identified several key principles, and then explored the most significant one using graph theory and a matrix approach. The findings indicate that autonomy has been evaluated as the most significant challenge to manage design principles most effectively for sustainable manufacturing in the Industry 4.0 context. Therefore, organizations must enhance autonomous systems capable of operating and making decisions alone, without relying on external instructions or support, in order to achieve sustainability. This and the other identified principles to achieving corporate supply chain sustainability are ranked in this paper. These findings will be useful for managers and policymakers managing the interaction between people and processes, as well as corporate sustainability management in Industry 4.0-based supply chain organizations. The unique contributions of this paper can aid in the improvement of design principles and the sustainability of emerging economies' Industry 4.0 perspectives.

Digital maintenance has the potential to leverage data and advanced analytics to forecast, prevent, diagnose, and rectify equipment problems. To better fulfill the maintenance objective, digital maintenance combines traditional maintenance functionalities with modern digital tools and techniques. It also serves as a sustainable and strategic component for achieving several corporate goals, including profitability and overall business performance. Despite the immense value of sustainable digital maintenance, only a few industries have adopted and started using it. Early adopters benefited greatly from the early and widespread transformation of their maintenance and reliability functions. However, adoption remains oppressive for most organizations. This survey focuses on maintenance users and their company's beliefs in the transformation of sustainable digital maintenance services by establishing a technology acceptance model and exploring different intent factors. According to the results and analysis, experienced users have a strong and positive attitude towards the adoption of sustainable digital maintenance. They feel hesitant towards their immediate use due to their non-readiness and the lack of facilitating conditions such as the resources and knowledge required for it. Additionally, digital maintenance services must be sustainable, with a prime focus on the environment, economy, and safety.

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.