Abstract Industry 4.0 and blockchain technology are transforming manufacturing processes with automation, real-time data exchange capabilities and promoting trust across the supply chain. However, the assessment of the readiness of Industry 4.0 and blockchain technology within the manufacturing sector is preliminary for the successful integration of these technologies and thus transforming manufacturing processes. While Industry 4.0 readiness is not a new concept, and several Industry 4.0 readiness models have been devised specifically for SMEs across developed countries, no blockchain-related readiness model could be traced in the literature. Additionally, developed countries are maturing in the adoption of advanced technologies, while developing countries have showcased challenges in embracing Industry 4.0 and blockchain technology. This study systematically reviews and compares the readiness factors for the adoption of Industry 4.0 and blockchain in the manufacturing sector to provide directions for future research for digital transformation. A total of 63 articles were reviewed and analysed. The findings reveal 33 dimensions with an overlap of 9 common readiness dimensions across Industry 4.0 and Blockchain readiness, while the rest of the dimensions are distinctive. Industry 4.0 readiness is primarily marked by integrating information technology across the business for data management and planning the strategic integration of advanced technologies. On the contrary, blockchain readiness is more contingent upon standardisation and infrastructure. The present research is among the few studies that offer a comparative analysis of the readiness dimensions identified for Industry 4.0 and blockchain. Manufacturing firms can use these insights for a holistic approach towards the adoption of Industry 4.0 and blockchain, ensuring a secure digital transformation.

Abstract Construction and demolition waste (CDW) are commonly disposed of in unlined landfills or inappropriately at irregular sites. Civil construction materials may contain hazardous substances that, if solubilized or leached, can negatively impact the environment and human health. Understanding the leaching behavior of CDW is essential for assessing its environmental performance and ensuring its safe reintegration into the construction supply chain. This study aimed to investigate the impact of paint presence on the leachate contamination potential. The method involved the UNE-EN 12457-3 compliance leaching test and column percolation tests conducted under both saturated and unsaturated conditions, using columns filled with CDW, with and without paint. The samples, regardless of the presence of the paint layer, were classified as non-hazardous according to the criteria established by the European Council. The results indicate that the presence of paint mainly influenced the apparent color, turbidity, and concentrations of Na + and K + in the leachate. Although various heavy metals are used in paints, especially as pigments, the presence of paint in CDW did not significantly influence the release of these metals into the leachate. The CDW leachates, regardless of the presence of paint, exhibited potential for groundwater contamination due to elevated levels of sulphate and total dissolved solids. Notably, CDW also demonstrated the capacity to remove Zn and Fe and CDW without paint was found to reduce water turbidity.

The prevalence of live-streaming selling greatly impacts consumer returns, which forms an urgent challenge for manufacturers to implement return freight insurance (RFI) policies and live-streaming operations appropriately. In the paper, we establish a supply chain where a manufacturer deploys an employee or influencer streamer to sell products on a live-streaming platform and introduces three kinds of RFI policies, namely no-RFI (NRFI) policy, manufacturers-RFI (MRFI) policy, and consumers-RFI (CRFI) policy. Given that the interaction-perception efficiency of streamers may positively or negatively affect the return rate, we explore the choice of streamer types and RFI policies, yielding several significant findings. First, with the MRFI policy, the manufacturer should deploy an influencer streamer at a low MRFI fee and high residual value; otherwise, it should deploy an employee streamer. Second, with the CRFI policy, the manufacturer should deploy an influencer streamer at a high CRFI fee or high product residual value; otherwise, it should deploy an employee streamer. Finally, regardless of which streamer is deployed, the manufacturer consistently adopts the MRFI policy at a low MRFI fee. However, when the MRFI fee exceeds a certain threshold, the CRFI fee and the interaction-perception efficiency affect the choice between CRFI and NRFI policies.

A data analytics-driven approach to backorder prediction using federated machine learning in industrial supply chains

This study evaluated the effectiveness and patient outcomes of a temporary switch from methadone to slow-release oral morphine (SROM) during COVID-19-related supply disruptions in Lithuania in 2022. Data from 231 patients at the Vilnius Branch of the Republican Centre for Addictive Disorders who received SROM for at least two days were retrospectively analyzed. The key metrics included methadone and SROM dosages, withdrawal severity (Clinical Opioid Withdrawal Scale (COWS)), and retention rates at 1, 3, 6, and 12 months post-switch. The data were compared by sex, methadone dosage group (low: 10-60mg/d, medium: 61-100mg/d, high: 101-150mg/d), and clinic attendance frequency. To contextualize long-term outcomes, retention rates were compared with annual program-level data from 2018 to 2024. Patients received SROM for an average of 8.4 days at an initial methadone-to-SROM ratio of 1:4, which increased to 1:5.23. Withdrawal symptoms were generally mild, peaking at a mean COWS score of 8.2. Women experienced more severe symptoms than men did. After two weeks of SROM therapy, methadone supplies were restored, and patients resumed their original treatment. The retention rates remained high at 1, 3, 6, and 12 months (97.8%, 96.1%, 93.5%, and 89.2%, respectively), with higher retention rates among patients with take-home doses, higher baseline methadone dosages, or longer treatment histories. Long-term program data confirmed that the temporary switch to SROM did not adversely affect overall treatment engagement compared with preceding and subsequent years. A temporary switch to SROM effectively managed methadone supply disruptions by serving as a viable substitute for methadone, causing minimal withdrawal symptoms and maintaining long-term retention. Coordinated clinical monitoring, institutional protocols, and supportive policy measures ensure continuity of care, emphasizing the value of flexible, personalized treatment strategies during crises.

ABSTRACT It is still unclear how different supply chain actors, both human and non‐human, implement technological changes to achieve sustainability outcomes by considering their dependencies on each other and their natural social networks, especially in emerging economies. From this point of view, this study aims to integrate two management theories, Actor‐Network Theory and Resource Dependence Theory, to explain how supply chain technologies can bridge sustainability outcomes for different supply chain actors. In this study, firstly, a theoretical structure that integrates Actor‐Network Theory and Resource Dependence Theory is proposed; secondly, supply chain technologies and sustainability outcomes are determined, and finally, a proposed framework for bridging supply chain technologies and sustainability outcomes through the lens of Actor‐Network Theory and Resource Dependence Theory is presented by using semi‐structured interviews. As a result of this study, a relationship between technology and sustainability outcomes was established for supply chain actors. It is revealed that technological needs to achieve sustainable outcomes vary for different supply chain actors. The main originality of this paper is its integration of actor‐network theory and resource dependence theory. A framework is developed to show resource dependencies between human and non‐human actors in the supply chain to achieve sustainability outcomes using supply chain technologies.

Natural gas (NG) plays a crucial role in current and future energy systems in the United States due to its abundance and affordability. In this study a life cycle analysis of the NG supply chain in the United States was conducted using Argonne's R&D GREET model, examining stages from recovery to distribution using reported field data processed and documented by National Energy Technology Laboratory. Supply chain emissions were evaluated across multiple spatial scales, including national average, overall regional production, region-to-region, and basin-to-region scenarios. The GHG intensity of the U.S. average NG supply chain was estimated at 10.3 kg CO2e/MMBtu (lower heating value), with a range across regions from 7.8 kg CO2e/MMBtu (Northeast) to 15.1 kg CO2e/MMBtu (Pacific). The analysis further assessed how upstream NG emissions influence the life cycle GHG emissions of key end-use applications, including electricity generation (0.044-0.086 kg CO2e/kWh from upstream NG in combined cycle facilities), hydrogen production (1.04-2.20 kg CO2e/kg H2 for steam methane reforming [SMR] and 1.06-2.23 kg CO2e/kg for autothermal reforming [ATR]), and transit bus operation utilizing compressed natural gas fuel (0.19-0.37 kg CO2e/mile) and hydrogen fuel (0.12-0.25 kg CO2e/mile for hydrogen produced in SMR and ATR).

The Carbon Border Adjustment Mechanism (CBAM) is a border tax implemented by highly carbon-regulation countries (HCRC) on imports from less carbon-regulation countries (LCRC). To analyse carbon reduction cooperation in transnational supply chains under CBAM, this study develops a biform differential game model involving a distributor in HCRC and two manufacturers (from HCRC and LCRC) supplying homogeneous products. Within this framework, supply chain members cooperate in carbon reduction while independently setting the wholesale prices or retail prices. The findings reveal a key distinction between cooperative and non-cooperative modes: under non-cooperation, pricing and emission reduction decisions remain static, whereas under cooperation, they dynamically evolve and gradually stabilise. Moreover, as the CBAM price increases, distributors in the cooperative model increase the LCRC product sales but reduce them in the non-cooperative setting. Consequently, under cooperation, the distributor’s profit increases with CBAM prices, while under non-cooperation, the profit follows an inverse U-shaped pattern. Finally, the cooperative mechanism enhances sales, emission reductions, and both LCRC and HCRC manufacturers’ profits compared to the non-cooperative model. However, the distributor achieves higher profits only when CBAM prices are sufficiently high, and thus should adopt the cooperative strategy under the same condition.

ABSTRACT Based on resource dependence theory (RDT), this study focuses on the impact of the geographical positioning of environmental background officials in a supply chain on focal firms' green innovation. We argue that officials' spatial locations, rather than merely their environmental expertise, influence the dependence between firms and governmental authorities. We differentiate between unidirectional dependencies and reliance of firms in provinces of suppliers and customers, and bidirectional dependencies corresponding to mutual reliance of firms and local officials. The empirical findings indicate that the local officials in the suppliers' and customers' provinces are active proponents of green innovation: the upstream officials ease the access to green raw materials, and the downstream officials impose on the goods and services procurement the green standards. However, local officials have a lesser effect because of the economic reliance in the local firms. Heterogeneity analyses reveal stronger effects in high‐pollution industries, private firms, and regions with higher institutional quality, highlighting boundary conditions of the mechanism. Additional analyses indicate that officials' political origins matter: centrally rotated officials have the most positive impacts, then there are inter‐provincial rotations, followed by the least powerful impacts of locally promoted officials.

Contraceptive stockouts are a major barrier to effective family planning (FP) service delivery in Nigeria, limiting access to modern methods and contributing to adverse reproductive health outcomes. Despite ongoing efforts to strengthen the supply chain, many health facilities continue to experience stockouts. A cross-sectional study was conducted in September 2024 across 1,050 service delivery points (SDPs) in Nigeria. Descriptive statistics and univariate mixed-effects logistic regression were used to explore associations between stockouts and facility characteristics, including location, supervision frequency, resupply methods, and logistics practices. Overall, 41.7 percent of SDPs reported at least one contraceptive stockout in the three months preceding the survey. Stockouts were slightly more common in rural facilities (56.8 percent) compared to urban facilities (43.2 percent), though this difference was not statistically significant (p = 0.53). Monthly supervisory visits were associated with significantly lower stockout rates (p = 0.014). Facilities relying on external agencies for resupply had 1.55 times higher odds of stockouts than those calculating needs internally (p = 0.058). Delays exceeding two weeks between ordering and delivery were the strongest predictor of stockouts (odds ratio: 1.76, 95 percent confidence interval: 1.257-2.474, p < 0.001). Improving supply chain efficiency, supervision frequency, and resupply models is critical to reducing contraceptive stockouts and enhancing FP service delivery in Nigeria.

Oceans are increasingly shaped by climate change, biodiversity loss, geopolitical tensions and maritime crime and insecurity. Climate-smart marine spatial planning (CSMSP) has emerged as a governance framework to integrate climate action, conservation, and equity into ocean planning. However, defense institutions–key actors in maritime security and major greenhouse gas emitters–remain absent from CSMSP discourse. This paper argues that integrating defense into CSMSP offers strategic and climate benefits: minimizing defense-driven offshore wind cancellation and thus accelerating approval, safeguarding environmental protection and undersea critical energy infrastructure, accelerating decarbonization through the military's green transition, and addressing the defense emissions gap. Yet, integration carries significant risks: power asymmetries, spatial exclusion, ecological harm, and militarization of green energy. To reconcile security imperatives with sustainability, transparency and equity, the paper proposes governance pathways: transparent data sharing, conflict-resolution and co-existence protocols, and defense marine zoning. “However, the paper warns that while green defense initiatives have climate benefits, there are reasons to curb our enthusiasm”. Specifically, rising global military spending and the resultant mineral-intensive extractivism to support war-readiness threaten to overshadow these benefits by locking in carbon-heavy supply chains and amplifying upstream emissions, environmental degradation, and social disposability. Aligning national security with climate security thus requires more than technological greening: it requires transparent cradle-to-grave emissions, and strategic restraint in defense spending, war-readiness, and material efficiency. Ultimately, integrating defense into CSMSP is not merely a technical exercise but a normative challenge that will determine whether ocean governance advances sustainability and transparency or succumbs to securitized emissions and green extractivism.

Background: The Port of Durban plays a pivotal role in South Africa’s maritime trade. This article addresses a critical challenge faced by the Durban dry-bulk and break-bulk terminal, which is the excessive waiting time experienced by vessels at anchorage. Lengthy anchorage waiting time results in economic losses for shipping companies and affects the overall efficiency of the port. Objectives: The study aimed to identify the factors that affect the anchorage of dry-bulk and break-bulk vessels while waiting for the berth (WFB) in order to recommend actions that should be taken to reduce vessel anchorage waiting time while WFB at the Port of Durban’s bulk terminal. Method: Qualitative data were obtained through semi-structured interviews, which lends itself particularly well to thematic analysis. Results: The research findings reveal that multiple factors contribute to the extended waiting times experienced by dry-bulk and break-bulk vessels at the Port of Durban. These factors include inefficient cargo handling processes, inadequate infrastructure, congestion, adverse weather conditions, and port capacity constraints. In addition, vessels arriving at the same time while the berth is still occupied further intensify delays, resulting in vessels WFB at anchorage. Conclusion: The study identified and prioritised these factors, which enabled the development of targeted solutions to mitigate the waiting time issue. The proposed solutions encompass both short-term and long-term measures. Contribution: This research emphasises the importance of supply chain collaboration among key port stakeholders.

Purpose Manufacturing firms are increasingly under pressure to manage products responsibly at their end-of-life stage, which requires stronger digital-, intelligence- and sustainability-oriented capabilities. However, limited empirical research clarifies which of these capabilities are essential (must-have) and which enhance performance only at higher levels (nice-to-have). Drawing on dynamic capability theory, knowledge-based view and stimulus–organism–response theory, this study examined how digital transformation (DT) and supply chain intelligence (SCI) enhance sustainable end-of-life management (SELM). The study also examined the mediating role of SCI and the moderating role of sustainable product design and development (SPDD). Design/methodology/approach The proposed model was validated using survey data from 309 managers purposively selected from manufacturing firms in Ghana. Structural equation modelling (SEM) using partial least squares software and necessary condition analysis (NCA) were employed to analyze the data. Findings The findings demonstrate that DT directly enhances SELM and indirectly influences it through the integration of SCI. It was further found that the effectiveness of intelligence integration is strengthened by SPDD, particularly for customer and supplier intelligence. NCA results further reveal that DT and SPD represent foundational necessary conditions for SELM, whereas SCI integration remains desirable until higher SELM levels are achieved. Originality/value This study is among the first to offer empirical evidence (via SEM and NCA) of the nice-to-have and must-have antecedents of SELM among manufacturing firms in Ghana. The findings offer practical guidance by highlighting that firms seeking high levels of SELM must invest deeply in DT, SCI capabilities and SPD, particularly DT and SPD at advanced performance levels.

This study examines the impact of digital transformation on supply chain management efficiency among Indonesian listed firms, using a panel dataset of 381 companies listed on the Indonesia Stock Exchange (IDX) from 2015 to 2024. The empirical results show that digital transformation significantly enhances supply chain efficiency, as evidenced by improved inventory turnover and higher order fulfillment rates. This finding remains robust after addressing endogeneity concerns and conducting a series of robustness checks. Further analysis reveals that information transparency serves as a partial mediator in this relationship, suggesting that digital transformation boosts supply chain performance largely by improving the visibility and sharing of real-time operational data across the value chain. Heterogeneity test reveals the positive impact of digital transformation is notably stronger in large firms, firms located in regions with high internet coverage, and manufacturing enterprises. Based on these findings, the study offers practical implications across three dimensions: corporate strategy, policy support, and technology implementation. It also identifying for future research, including broadening the data scope beyond listed firms, refining digital transformation metrics with more direct measures, and conducting cross-national comparisons to explore how institutional contexts shape digitization outcomes.

Abstract In the short-food supply chain, planning the orders that the retailer places with the farmer is essential to ensure the satisfaction of the clients and the minimization of the costs for the retailer. Some of these retailers face a distinct set of challenges that, to the best of our knowledge, we are the first to tailor a model for. We integrate a mathematical model within a rolling horizon framework to address the supplier selection and order allocation problem, accounting for dynamic demand, production, and inventory capacities, as well as unit purchasing, ordering, and holding costs. Additionally, we perform a sensitivity analysis to understand the behavior of the model. The model is able to generate an ordering schedule given the demand of the clients and different parameters of suppliers. When integrated into the rolling horizon framework, the model could adapt to new information and modify previously planned, ordered, stored, and delivered quantities to meet the demand of the clients with minimal costs. When tested on instances of thirty suppliers with planning windows of length fifty time periods, the model had an average execution time of 3.5 seconds which we deemed acceptable.

In response to the recurring violations of labor rights within global value chains, the international community has witnessed the emergence of a new legal governance mechanism exemplified by “mandatory human-rights due-diligence law”. The hallmark of such legislation is the creation of a statutory “due-diligence” obligation for companies, seeking to recast multinational enterprises as pivotal nodes in the governance of labor standards across their supply chains and to leverage their commercial influence to transmit compliance pressure throughout the entire value chain. Focusing on mandatory human-rights due-diligence law, this article analyzes how they achieve extensive extraterritorial reach over global value chains through distinctive ways of delineating the duty-bearing subjects and through the dual pathways of public regulation and private governance. By innovatively expanding the circle of legal duty bearers and by simultaneously deploying public-regulation channel and private governance channel, mandatory human-rights due-diligence law is transforming labor-rights protection from a traditional jurisdictional framework into a new model of global value-chain governance in which multinational corporations act as the primary enforcement intermediaries.

In Bangladesh, pineapple variety identification is still largely dependent on experience and eye-tracking. As a result, there is a possibility of misclassification in marketing, grading, pricing, and agricultural supply chain management. This study proposes a feature-based machine learning approach to automatically identify varieties from pineapple images. In the proposed approach, noise reduction, contrast adjustment, and fruit region (ROI) separation will be performed through image preprocessing. Then, three classes of features—color, texture, and shape—will be extracted to create a composite feature vector, where RGB/HSV-based statistical features will be used for color features, LBP/GLCM-based descriptors for texture features, and contour-based metrics (such as area, perimeter, circularity, and aspect ratio) will be used for shape features. The collected dataset will be divided into training and testing parts, and the varieties will be classified using Decision Tree and k-Nearest Neighbors (KNN) algorithms. Accuracy, precision, recall, and F1-score will be used to measure the effectiveness of the models. The research presents a solution that can be implemented at low computational cost, which can provide effective support for rapid variety identification, quality control, and agricultural decision-making at the field level. The dataset consists of three pineapple varieties with a total of 1410 images after augmentation, divided into 80% training and 20% testing sets. Decision Tree and K-Nearest Neighbor classifiers were employed for classification, where texture-based feature combinations showed the best performance. The proposed low-cost and interpretable framework can support farmers, traders, and agricultural stakeholders in variety identification, quality control, and supply chain decision-making.

In the modern era, demand forecasting enhances the decision-making tasks of industries for controlling production planning and reducing inventory costs. However, the dynamic nature of the fashion and apparel retail industry necessitates precise demand forecasting to optimize supply chain operations and meet customer expectations. Consequently, this research proposes the Formicary Zebra Optimization-Based Distributed Attention-Guided Convolutional Recurrent Neural Network (FZ-DACR) model for improving the demand forecasting. In the proposed approach, the combination of the Formicary Zebra Optimization and Distributed Attention mechanism enabled deep learning architectures to assist in capturing the complex patterns of the retail sales data. Specifically, the neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), facilitate extracting the local features and temporal dependencies to analyze the volatile demand patterns. Furthermore, the proposed model integrates visual and textual data to enhance forecasting accuracy. By leveraging the adaptive optimization capabilities of the Formicary Zebra Algorithm, the proposed model effectively extracts features from product images and historical sales data while addressing the complexities of volatile demand patterns. Based on extensive experimental analysis of the proposed model using diverse datasets, the FZ-DACR model achieves superior performance, with minimum error values including MAE of 1.34, MSE of 4.7, RMS of 2.17, and R2 of 93.3% using the DRESS dataset. Moreover, the findings highlight the ability of the proposed model in managing the fluctuating trends and supporting inventory and pricing strategies effectively. This innovative approach has significant implications for retailers, enabling more agile supply chains and improved decision making in a highly competitive market.

This study analyzes the impact of geopolitical risk and trade balance on the performance of Vietnamese listed firms over the period 2016–2024, using an unbalanced panel of 588 firms. The study employs a dynamic panel System Generalized Method of Moments (System GMM) estimator to address endogeneity, unobserved firm-specific effects, heteroskedasticity, and serial correlation. The empirical findings indicate that geopolitical risk exerts a positive and statistically significant effect on both return on assets (ROA) and return on equity (ROE). This suggests that, in the context of global supply chain realignments and capital reallocation, Vietnamese listed firms have been able to leverage new opportunities created by geopolitical tensions. In contrast, the trade balance shows a negative and significant relationship with firm performance, implying that improvements in the aggregate trade balance are associated with lower efficiency at the firm level. This result reflects the structural dependence of Vietnamese firms on imported raw materials, machinery, and intermediate inputs. Control variables such as firm size, tangible assets, liquidity, Tobin’s Q, and net working capital improve performance, while high financial leverage and the COVID-19 shock reduce it. Moderate inflation contributes positively to firm performance. The study offers new empirical evidence on the role of geopolitical risk and trade balance in shaping corporate performance in an emerging, export-oriented economy and provides policy implications for regulators, investors, and corporate managers.

Purpose: Accounting efficiency is a critical aspect of organizational performance, reflecting the effectiveness of accounting systems, processes, and resources in generating accurate, timely, and relevant financial information. Despite growing scholarly attention, research on factors affecting accounting efficiency remains fragmented across subfields such as cost accounting, management accounting, financial performance, and accounting information systems. A systematic assessment is necessary to synthesize knowledge, identify trends, and guide future research. Methodology: This study adopts a bibliometric research design, analyzing 127 peer-reviewed articles retrieved from the Scopus database (2006–2025). Using R Studio with the Bibliometrix package and Biblioshiny. Results &amp; Analysis: Findings reveal a clear growth trajectory in accounting efficiency research, with publications increasing from 6 in 2006 to 28 in 2025, and citations declining for recent studies due to limited exposure. A concentrated set of core journals and highly productive authors drive the field, while China, the United States, and selected European and Asian countries lead scientific output. Thematic analysis identifies cost accounting, efficiency, and cost-benefit analysis as central topics, with emerging trends in sustainability, supply chain performance, and quantitative efficiency measurement methods such as data envelopment analysis. Originality / Value: The study highlights the evolution of accounting efficiency research from foundational cost and productivity studies to empirical, interdisciplinary, and technology-driven investigations. By mapping publication trends, influential contributors, and emerging themes, this bibliometric analysis provides a comprehensive framework for guiding future research, policy formulation, and practice-oriented strategies to enhance accounting efficiency globally. Type of Paper: Review-based Analysis.