Search Results (394)

The growing need to improve the management of end-of-life vehicle (ELV) waste and mitigate its environmental impact is a global concern. One promising approach to enhancing the recyclability of these vehicles is leveraging synergies between the automotive and construction industries as part of a circular economy strategy. In this context, ELV waste emerges as a valuable source of secondary raw materials, enabling the development of sustainable innovations that capitalize on its physical and mechanical properties. This paper aims to develop and evaluate construction industry composites incorporating waste from ELV carpets, with a focus on maintaining or enhancing performance compared to conventional materials. To achieve this, an experimental program was designed to assess cementitious composites, specifically subfloor mortars, incorporating automotive carpet waste (ACW). The results demonstrate that, beyond the physical and mechanical properties of the developed composites, the dynamic stiffness significantly improved across all tested waste incorporation levels. This finding highlights the potential of these composites as an alternative material for impact noise insulation in flooring systems. From an academic perspective, this research advances knowledge on the application of ACW in cement-based composites for construction. In terms of managerial contributions, two key market opportunities emerge: (1) the commercial exploitation of composites produced with ELV carpet waste and (2) the development of a network of environmental service providers to ensure a stable waste supply chain for innovative and sustainable products. Both strategies contribute to reducing landfill disposal and mitigating the environmental impact of ELV waste, reinforcing the principles of the circular economy. Full article

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials. This formulation allowed us to employ analytical tools from statistical mechanics to identify optimal ordering strategies via the minimization of a free energy functional. Our approach not only sheds new light on the structural characteristics of optimal planning but also introduces the concept of configurational entropy as a measure of decision variability and robustness. Numerical simulations and analytical approximations demonstrate the efficacy of the lattice gas model in capturing key features of the problem and suggest promising avenues for extending the framework to more complex settings, including multi-item systems and time-varying demand. This work represents a significant step toward bridging physical sciences with supply chain optimization, offering a robust theoretical foundation for both future research and practical applications. Full article

Sustainable Supply Chain Management (SSCM) emerges as a vital catalyst for inclusive growth and sustainable development, particularly in emerging economies where the manufacturing sector is central to economic progress. This study offers an in-depth analysis of the current research landscape on SSCM in the context of developing nations, outlining key theoretical frameworks and advocating for a solid conceptual foundation alongside a structured agenda for future research initiatives. This study employs a structured literature review technique to analyze 92 published articles indexed by Scopus from 2013 to 2024, revealing a burgeoning trend in the subject of global supply chains in developing nations. The analysis identifies key keywords such as “sustainable supply chain management,” “manufacturing industries,” “inclusive growth,” and “supply chain and sustainability,” and develops a conceptual model that elucidates how SSCM practices can be effectively integrated into manufacturing sectors to facilitate equitable growth and enhance business competitiveness. This work’s novelty lies in employing a systematic literature review to develop a holistic SSCM conceptual framework constructed upon six primary drivers: business model innovation, inclusive SSCM, corporate governance and leadership, technological and innovation capabilities, policy and regulatory environment, and circular feedback. This model addresses the ambiguity surrounding SSCM and inclusive growth, providing a robust foundation for future research and performance measurement. This study contributes to the field by providing a practical and theoretically grounded framework for researchers, policymakers, and practitioners seeking to implement impactful and effective SSCM initiatives in developing nations’ manufacturing sectors to promote inclusive growth and sustainable development. Full article

With the advancement of Agriculture 4.0, intelligent systems and data-driven technologies offer new opportunities for pork supply-demand balance regulation, while also confronting challenges such as production cycle fluctuations and epidemic outbreaks. This paper introduces a knowledge-driven smart system for pork supply-demand regulation, which integrates essential components including a knowledge base, a mathematical-model-based expert system, an enhanced optimization framework, and a real-time feedback mechanism. Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. By harnessing dynamic decision-making capabilities of reinforcement learning (RL), we design an optimization architecture centered on the Q-learning mechanism and dual-strategy pools, which is integrated into the honey badger algorithm to form the RL-enhanced honey badger algorithm (RLEHBA). This innovation achieves an efficient balance between exploration and exploitation in model solving and improves system adaptability. Numerical experiments demonstrate RLEHBA’s superior performance over State-of-the-Art algorithms on the CEC 2017 benchmark. A case study of China’s 2026 pork regulation confirms the system’s practical value in stabilizing the supply-demand balance and optimizing resource allocation. Finally, some targeted managerial insights are proposed. This study constructs a replicable framework for intelligent livestock regulation, and it also holds transformative significance for sustainable and adaptive supply chain management in global agri-food systems. Full article

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article

Despite growing interest in integrating Lean Construction (LC) and Building Information Modeling (BIM) to advance sustainability in the Architecture, Engineering, and Construction (AEC) industry, research remains fragmented and lacks a unified implementation framework. This study bridges this gap by conducting a systematic literature review (2010–2024) of 96 journal articles to (1) analyze research trends in BIM-LC integration; (2) evaluate its benefits for sustainable built environments; and (3) identify barriers to adoption. A key contribution is the development of a novel four-dimensional BIM-LC integration framework, encompassing information integration, supply chain management, waste management, and life cycle management, which synergizes LC principles with BIM’s technical capabilities to reduce waste, enhance resource efficiency, and support carbon neutrality goals. The findings reveal that while BIM-LC integration significantly improves construction productivity and reduces environmental impacts, technical challenges in data interoperability and fragmented lifecycle management persist. Actionable solutions are further proposed, including semantic model standardization, AI-driven supply chain resilience, and circular economy integration. This framework provides both scholars and practitioners with a roadmap to advance BIM-LC adoption for sustainable construction. Full article

The advent of digital technology has transformed traditional service management approaches and offers new opportunities for the supply chain network to resist these interruptions. However, current research on how digital service innovation directly affects supply chain resilience is limited. This study constructs a theoretical research framework to explore how digital service innovation promotes supply chain resilience in the manufacturing industry, using a network embeddedness perspective. Through extensive survey data, the research demonstrates that digital service innovation enhances resilience by fostering two types of network embeddedness, namely relational and structural embedding, which in turn enhances resistance to interruption. IT support capabilities further reinforce the relationship between digital service innovation and relational embedding and structural embedding, and enhance the overall impact on resilience. This paper is among the first to integrate digital service innovation into supply chain resilience research, unveiling a network embedding approach for enhancing the ability to respond to supply chain network disruptions. Full article

This study takes the dual-channel fresh product supply chain involving the participation of third-party logistics (3PL) as the background to explore how 3PL makes choices between homogeneous and differentiated logistics service strategies and how the supply chain formulates optimal decisions under different logistics service strategies to achieve maximum benefits. This paper constructs a sequential game model of the three-tier supply chain composed of 3PL, a supplier, and a retailer; uses the consumer utility function to describe market demand; and considers different logistics service strategies adopted by 3PL. It compares and analyzes the equilibrium strategies under the traditional retail channel (O Model), the homogeneous cold-chain service dual-channel model (D1 Model), and the differentiated cold-chain service dual-channel model (D2 Model). The results show the following: (1) The D1 Model reduces the transportation cost of the supply chain through economies of scale. Under the D2 Model, the transportation and sales prices of the offline channels are higher than those of the online channels, while the online marketing effort is higher than that of the offline channels. (2) The profits generated by the dual-channel models (D1 Model and D2 Model) are both higher than those of O Model. In most cases, the D1 Model generates the highest system profit. However, in scenarios where consumers are highly sensitive to freshness and marketing efforts, the system profit of the D2 Model is higher than that of the D1 Model. (3) The supply chain has lower pricing and effort input when consumers are more sensitive to prices and higher pricing and effort input when consumers are more sensitive to freshness. These findings contribute valuable insights to the field of supply chain management, particularly in the context of fresh product supply chains involving 3PL. They underscore the importance of considering consumer behavior and logistics service strategies in optimizing supply chain performance and highlight the potential trade-offs between standardization and differentiation in logistics services. Full article

The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human resource management. This study proposes a resilience-based AX strategy and implementation system that allows domestic container-terminal companies to proactively respond to the upcoming changes in the global supply chain, thus securing sustainable competitiveness. In particular, we aim to design an AI-based governance model to establish a trust-based logistics supply chain (trust value chain). As a research method, the core risk factors of AX processes were scientifically identified via text-mining and fault-tree analysis, and a step-by-step execution strategy was established by applying a backcasting technique based on scenario planning. Additionally, by integrating social control theory with new governance theory, we designed a flexible, adaptable, and resilience-oriented AI governance system. The results of this study suggest that the AI paradigm shift should be promoted by enhancing the risk resilience, trust, and recovery of organizations. By suggesting AX strategies and policy as well as institutional improvement directions that embed resilience to secure the sustainable competitiveness of AI-based smart ports in Korea, this study serves as a basis for establishing strategies for the domestic container-terminal industry and for constructing a global leading model. Full article

Background: In any country, supply chain management is crucial to the economy. Big data-driven (BDD) implementation can be used in different disciplines, especially in construction supply chain management (CSCM). While BDD has a lot of opportunities for optimizing workflows, reducing costs, and improving collaboration among stakeholders to enhance efficiency and decision-making, its adoption is fraught with significant barriers. Thus, identifying these challenges is an important research concern. Methods: This study adopts a systematic review methodology aligned with PRISMA guidelines, combining bibliometric and thematic analyses to explore the integration of BDD approaches in CSCM. A comprehensive search of the Scopus database was conducted, focusing on articles published between 2014 and 2024 with a multi-phase screening process until 62 relevant studies were adopted. Results: This study summarizes the challenges associated with integrating BDD into CSCM and presents solutions to solve them and a framework for implementing BDD in CSCM. Moreover, providing future directions that require further consideration and research. Conclusions: By overcoming these barriers, the construction supply chain will be able to adopt big data for improving efficiency and reshaping CSCM. This study provides a clear view of how CSCM scholars and practitioners should develop along with promising research on BDD. Full article

Industry 4.0 technologies offer significant opportunities to enhance sustainable production and circular economy practices in the face of challenges arising from climate change. Considering the growing interest in this field, the literature review exposed that, particularly in the case of the Internet of Things (IoT), there is a need for empirical assessments of the impact of this technology on sustainability and circularity. This paper presents the validation process of an original assessment tool that evaluates IoT’s alignment with the socioenvironmental and circular context of manufacturing organizations and their supply chains. Emphasis is placed on the construct titled “IoT Technology Expectations”. After systematically conducting a literature review, this study employed the Delphi method in conjunction with statistical analyses to refine or formulate new indicators or statements based on expert consensus, validating the proposed assessment tool. The findings of this research contribute to management practices by providing an instrument to assess the current stance of top management and other key managers (production, project and supply chain) on IoT use in manufacturing operations or supply chains from a socioenvironmental and circular perspective. The instrument serves as a starting point for exploring IoT’s potential in circular economy practices. Academically, it provides a detailed explanation of the Delphi method and its application and outcomes. Full article

Driven by the global energy transition and the pursuit of “dual carbon” goals, sustainability risks within the coal supply chain have emerged as a central obstacle impeding the low-carbon transformation of high-carbon industries. To address the critical gap in systematic and multidimensional risk assessments for coal supply chains, this study proposes a hybrid framework that integrates the analytic hierarchy process (AHP) with the fuzzy comprehensive evaluation (FCE) method. Utilizing the Delphi method and the coefficient of variation technique, this study develops a risk assessment system encompassing eight primary criteria and forty sub-criteria. These indicators cover economic, operational safety, ecological and environmental, management policy, demand, sustainable supply, information technology, and social risks. An empirical analysis is conducted, using a prominent Chinese coal enterprise as a case study. The findings demonstrate that the overall risk level of the enterprise is “moderate”, with demand risk, information technology risk, and social risk ranking as the top three concerns. This underscores the substantial impact of accelerated energy substitution, digital system vulnerabilities, and stakeholder conflicts on supply chain resilience. Further analysis elucidates the transmission mechanisms of critical risk nodes, including financing constraints, equipment modernization delays, and deficiencies in end-of-pipe governance. Targeted strategies are proposed, such as constructing a diversified financing matrix, developing a blockchain-based data-sharing platform, and establishing a community co-governance mechanism. These measures offer scientific decision-making support for the coal industry’s efforts to balance “ensuring supply” with “reducing carbon emissions”, and provide a replicable risk assessment paradigm for the sustainable transformation of global high-carbon supply chains. Full article

Construction industry largely produces long-life, unique, and inflexible products; and combined with dispersed supply chains, it makes material tracking difficult. Thus, to achieve a circular economy (CE) in construction, there is a need for managing material information at the asset level to support reuse and recovery. This study explores the solutions for a CE in construction, and adopts a critical review, and a systematic search and review process. Initially the critical review for CE solutions revealed that maintaining authentic material information via material passports (MPs) and adopting industrialised construction (IC) for resource efficiency and flexibility are the key actions for CE implementation. As initial findings suggested the implementation of MPs in IC as imperative for a CE in construction, it was deemed necessary to develop a framework for MPs’ creation and management in IC. Thus, a further critical review was conducted to explore MPs and IC in detail, and a systematic search and review process extracted the actual information that goes into MPs, which was further categorised under various IC lifecycle processes at different stages of lifecycle, to present the incorporation of MPs into IC. The knowledge of MP processes and information in IC from this review is the vital component for the development of a necessary information management framework for MPs. This study can also be a basis for further research on the application of digital technologies and managerial actions required to realise operational MPs in IC, which is required for material circularity in construction. Full article

Value chains are facing different challenges, caused by emerging technologies as well as Industry 4.0 and Industry 5.0 principles. In order to successfully deliver valuable products to their customers, firms have to adapt, transform, and continuously improve their operational processes. Digital technologies will enable digital supply chains which will be decentralized and composed of autonomous modules. Although the elements of the value chain are independent, this paper shows how they affect each other’s performance. In this study, a model which shows how human resources management impacts customers’ satisfaction is presented. Additionally, this model reveals direct and indirect relations between human resources management, processes, supply chain actors, and customers. The conducted study was based on the variance-based method, while the model was constructed using the “PLSPM” package in R software. Additionally, the confirmatory factor analysis was applied for assessing the construct constitution. Taking into account that “A chain is only as strong as its weakest link”, firms can use these findings to seek for performance indicators and problem causes across the whole value chain and not only in one of its elements. Full article

As key nodes in the energy transmission network, oil and gas pipeline stations are crucial in ensuring national energy security and stable economic development. The traditional construction mode of “on-site prefabrication and installation” has problems, such as low efficiency, high cost, and large quality fluctuations, which make it difficult to meet current construction needs. Factory prefabrication technology for pipelines has become a key path to solving industry pain points. This article focuses on the factory prefabrication technology of oil and gas station pipelines. By integrating key technologies, such as 3D modeling, automated welding, modular transportation, and intelligent detection, the visualization and digitization of station pipeline design are achieved, providing a basis for prefabrication and processing. They also improve welding quality and efficiency through automated welding technology and non-destructive testing technology. Through research on the planning and construction of prefabrication factories, construction organization and quality management, supply chain management, and information technology applications, real-time monitoring and information management of the construction process have been achieved. Case analysis shows that factory prefabrication can achieve a prefabrication rate of 70% for DN50–DN600 pipelines in the station, 80% for automated welding seams, a total construction period reduction of about 30%, a one-time welding qualification rate of over 96%, and a significant cost reduction, reflecting the significant advantages of factory prefabrication in terms of construction period, quality, and cost. Further research has clarified that factory prefabrication technology can effectively improve the efficiency, quality, and economic benefits of pipeline construction in oil and gas stations, promote the transformation of construction towards a high-efficiency, low-carbon, and sustainable direction, and provide support for the strategic goal of “One National Network”. Full article