Partial backward integration in maritime supply chain with duopoly carriers: Incentive analysis and decision-making volatility

Two-stage robust optimization approach for integrated supply chain planning with hybrid-dark stores

Midstream operations in the oil supply chain involve transporting resources from suppliers to retailers, a process that requires continuous monitoring and tracking. The existing systems face significant challenges in integrating trade documentation data, such as taxes, value-added costs, and resource monitoring data, during transportation across port logistics. Managing and processing these integrated data streams remains challenging, leading to operational inefficiencies, high latencies, and increased costs. To address these challenges, a Hyperledger-enabled blockchain framework is proposed, which introduces data segmentation to resolve management issues, utilizing sidechains to store company-specific data. Summarized results from the sidechains are periodically committed to the main blockchain. The framework enhances security through consensus algorithms and ensures data integrity using cryptographic techniques. This framework guarantees that data transferred from sidechains to the main blockchain remains accurate and tamper-proof. This also ensures full back-traceability from drilling sites to end users. Simulation results show that the proposed system reduces the latency by 33%, increases throughput by 49%, and decreases Central Processing Unit (CPU) utilization by 49% compared to the baseline. These results demonstrate that the proposed framework enhances operational transparency, computational efficiency, and secure traceability in midstream oil logistics.

ABSTRACTBrain connectivity, quantified with diffusion MRI (structural connectivity, SC) and resting‐state functional MRI (functional connectivity, FC), can offer crucial insights into glioma‐brain network interactions. Currently, no standardized approach exists to integrate information from FC and SC and to identify potential tumor‐induced abnormalities at the single‐patient level. Variational autoencoders (VAEs) have been shown to be promising for learning the distribution of features representing a healthy brain and deviations thereof and can naturally be applicable to multiple modalities. This study explores the potential of VAE to integrate FC and SC and detect multimodal anomalies in brain connectivity in glioma patients. The VAE is trained on concatenated FC‐SC healthy data to learn how to reconstruct normative connectivity patterns. After ad hoc transfer learning, the model parameters are applied to the oncological dataset, to obtain the healthy version of the pathological matrices. Given the healthy, pathological, and reconstructed matrices, a statistic is developed with the goal of identifying specific alterations in SC, FC, and their FC + SC integration in glioma patients. SC, FC, and FC + SC abnormalities are compared with each other to explore their interplay and their link with tumor and surrounding brain tissues. Results show that FC is more sensitive to alterations distant from the tumor, while SC is more affected in its vicinity. Then, the alterations identified by FC are generally more in agreement with the alterations identified by FC + SC compared with those highlighted by SC. Moreover, SC abnormalities never overlap with FC + SC out of the tumor, and FC and SC single impairments partially overlap within the tumor core and never overlie in other brain tissues. This information could facilitate patient stratification, prognostic modeling, and personalized treatment planning.

A Review of Manufacturing Operations Research Integration in Closed-Loop Supply Chains

This study examines the complexities of producing safe beef in Uganda, focusing on the actors, challenges, and dynamics that influence the production of safe beef. Using qualitative methods like focus group discussions (FGDs) and key informant interviews (KIIs), along with tools like Process Net-Map, the study identifies key actors’ roles and perceived influences in the beef value chain, as well as challenges and opportunities in producing safe beef. Results indicated the presence of 16 actors along the beef value chain in the cattle corridor of Uganda. Based on perceived influence levels, cattle farmers were recognized as the most influential actors, followed by veterinarians and cattle traders. The study also found that collusion among actors, regulatory capture, and lax enforcement hinder the process of the production of safe beef in Uganda, posing market and public health risks. Additionally, variability in actor influence was observed at the subregional and district levels, highlighting different subregional practices, economic conditions, and regulatory environments that affect the production of safe beef. Addressing these subregional variations and customizing interventions accordingly are important for improving food safety standards and maintaining the integrity of the beef supply chain across diverse geographical contexts. The study emphasizes the need for enhanced regulatory oversight, consumer education through labeling, and strict penalties for violations to ensure safe beef production.

Joint economic lot sizing shipment policy in multi-level integrated network supply chains of bottleneck material

Green supply chain integration and performance paradox: a bibliometric and systematic review

Digital green synergies: linking green cyber-physical systems, green supply chains integration, and green business intelligence to boost SMEs sustainability

Supply Chain Management (SCM) has received considerable attention from the industrial community in recent decades. SCM continues to be an interesting and relevant research topic in many business areas such as revealing supply chain integration benefits, uncertainty and risk mitigation methods, decision-making and optimization methodologies, etc. In current supply chain management, huge volumes of data are being developed each second, and emerging technologies such as Radio Frequency Identification (RFID) have amplified the availability of online data. Using Artificial Intelligence (AI) methods that go beyond simply using the huge volume of online data enables Supply Chain (SC) managers to monitor everything in a timely fashion. There are several aspects of an SC that AI—and specifically Artificial Neural Networks (ANNs)—can be applied to better help them manage and optimize. This study aims to review state-of-the-art ANNs and Deep Neural Networks (DNNs) in the field of supply chain management. One hundred high-quality research studies that applied ANNs in supply chain management are reviewed and categorized into four classes: performance optimization, supplier selection, forecasting, and inventory management studies. Our study shows that there is a significant possibility that we could use ANNs and DNNs to better manage supply chains. Across the reviewed studies, neural networks are frequently reported to improve predictive performance and support monitoring/control in complex, nonlinear supply chain settings, often complementing traditional operations research approaches. Finally, the limitations of ANN models and the possibilities for future studies are presented at the end of this study.

ABSTRACT High-quality development of enterprises (HDE) is the inevitable result of the synergistic promotion between the capital market and the real economy. By leveraging digital transformation to connect institutional investors with the real economy, this paper examines how institutional investor digital transformation activism (IIDTA) enhances HDE. We empirically test our hypotheses using the data from Chinese listed companies spanning from 2010 to 2022. Our findings indicate that IIDTA effectively empowers HDE. This facilitating effect is attenuated by analyst following (AF) but enhanced by supply chain integration (SCI). Finally, we elucidate the characteristics of IIDTA on HDE from the perspectives of bankruptcy risk and firm nature. The insights can assist institutional investors in addressing traditional management challenges and strengthening inter-firm collaboration to adapt to HDE in the digital era.

Research methodology The case was compiled using the following information sources, all of which are cited in the “References List” section of the case: Case overview/synopsis This case highlights the success, opportunities and challenges facing Country Delight, a rapidly growing seller of milk and dairy products in India, in 2021. Country Delight had grown its revenues by more than 17 times in the past four years, accompanied by a set of choices that was unusual in the market: deep supply chain integration, digitization, aggressive marketing and a direct-to-consumer delivery model. The rapid rise of Country Delight led to a muted response from established dairy giants such as Nestlé, Amul, Mother Dairy and others. Could cofounder Chakradhar Gade maintain the double-digit growth in its dairy business? Gade had also recently raised funds to expand into nondairy groceries such as spices, flours, fruits and vegetables. The case invites the reader to analyze Country Delight’s business model in the dairy business and to determine whether the drivers of its success in dairy could also fuel growth in nondairy food products. Complexity academic level This case is intended for use in a senior undergraduate or master’s level course in strategy in a module that deals with competitive dynamics. Note that the case assumes an understanding of basic concepts in strategic management, such as competitive advantage and positioning. In the authors’ institute, this case is used in a master’s level elective in strategy and is positioned after a module on game theory and before a module on managing uncertainty.

Purpose This study aims to investigate the impact of artificial intelligence (AI) utilization on firms’ operational performance. Building on this main effect, this study further examines the mediating role of resource reconfiguration and the moderating effect of supply chain integration (SCI), with the aim of uncovering the underlying mechanisms and boundary conditions that govern AI value realization in manufacturing contexts. Design/methodology/approach Grounded in dynamic capabilities theory and complementarity theory, this study develops a theoretical model linking AI utilization, resource reconfiguration, SCI and operational performance. Using survey data from 490 Chinese manufacturing firms, the hypotheses are tested through hierarchical regression and moderated mediation analysis with Hayes’ PROCESS macro. Findings The results reveal that (1) AI utilization enhances operational performance both directly and indirectly through resource reconfiguration; (2) resource reconfiguration partially mediates this relationship via a three-stage process of structuring, recombining and leveraging and (3) SCI significantly attenuates the positive effect of AI utilization on resource reconfiguration, thereby weakening the overall indirect impact on performance. Originality/value This study challenges the prevailing view that AI utilization and SCI are inherently complementary. By theorizing a moderated mediation model, we show that SCI can suppress the positive effect of AI on operational performance by constraining resource reconfiguration. This finding extends the theory of complementarities and highlights the conditional value of SCI in AI-enabled operations. Our study offers new insights for manufacturing firms aiming to balance digital transformation with collaborative routines in complex supply networks.

Purpose This study aims to investigate how firms’ positions within global semiconductor value chains shape their capital-structure decisions under varying institutional environments. It aims to clarify whether leverage is primarily determined by supply chain role, integration scope or institutional quality. Design/methodology/approach Using an unbalanced panel of 496 listed semiconductor firms across 26 countries from 2004 to 2022, we combine fixed-effects, correlated random effects and dynamic panel estimations. The analysis incorporates firm-level financial data and sector classifications. A two-step system generalized method of moments estimator is employed to mitigate endogeneity arising from leverage persistence, reverse causality and omitted variables. Robustness checks include alternative leverage definitions, winsorized samples and variance inflation factor diagnostics. Findings Functional specialization, rather than regional context or integration level, emerges as the primary determinant of leverage. Upstream firms exhibit significantly higher debt ratios than midstream counterparts, consistent with trade-off theory and capital-intensity arguments. The dynamic model confirms that leverage is persistent over time, while East Asian firms display lower leverage – suggesting that institutional support increasingly operates through equity participation and public investment rather than debt financing. Research limitations/implications Future research should explore instrumental-variable or matching designs and extend institutional measures to include policy-level and subregional indicators. Originality/value The study bridges corporate finance and global value chain research by integrating institutional quality into a dynamic, role-based model of capital structure. It demonstrates that supply chain position and institutional conditions jointly explain financial heterogeneity in high-technology industries.

This paper proposes an integrated framework to evaluate Corporate Social Responsibility (CSR) initiatives in peacebuilding settings using the Analytic Hierarchy Process (AHP). The model is structured around six criteria: conflict sensitivity, economic resilience, social inclusion, governance, education for peace, and sustainability, each subdivided into measurable subcriteria. A key methodological innovation is the introduction of objective grouping, which ensures that each alternative project is assessed only against the subcriteria where it generates tangible impact. Unlike the traditional AHP approach, where alternatives are evaluated against all criteria, objective grouping prevents irrelevant comparisons, reduces the cognitive burden on experts, and increases consistency in judgments. The method distinguishes between direct contributions (full weight allocation) and indirect contributions (partial weight allocation), while excluding unrelated dimensions. This refinement yields more transparent and context-sensitive prioritization, particularly relevant for fragile territories where CSR interventions must be both socially legitimate and economically viable. The empirical application shows that objective grouping highlights structural levers, such as grievance redress, local supply chain integration, peace education, and project scalability, as decisive for long-term peacebuilding. The framework thus improves decision-making by combining analytical rigor and stakeholder legitimacy, enhancing both business legitimacy and long-term societal resilience.

Using firm-level data, this study explores how next-generation automotive innovations are reshaping the automotive industry. The sector is now transitioning from a previously defined vertically integrated supply chain into a more horizontal ecosystem in which multiple companies collaborate to build next-generation vehicles. To define and categorize this ecosystem, this study classifies firms into three major groups of electric vehicles (EVs), autonomous vehicles (AVs), and general automotive technologies (GATs) by employing K-means clustering based on patent data. This study then measures firm efficiency in each ecosystem using meta-frontier analysis to compare their technical efficiency over the periods 2017-2019 and 2020-2022. The results show that the EV ecosystem led the initial growth while the GAT ecosystem continued to make steady progress. Later, the AV ecosystem exhibited remarkable technological innovation and efficiency gains. These findings indicate that efficiency dynamics and technology gaps differ systematically across ecosystems, reflecting heterogeneous innovation trajectories within the broader industrial transformation. Overall, our findings clarify the growth potential and trajectories in the automotive industry, thereby providing insights for stakeholders seeking to navigate this rapidly changing landscape and contributing to a clearer understanding of the next-generation automotive ecosystem.

In the era of globalization and rapid digital transformation, e-commerce has emerged as a significant driver of business expansion by enabling producers and sellers to access markets beyond geographical boundaries. Micro, Small and Medium Enterprises (MSMEs), which contribute nearly 40 percent to India’s GDP and rank as the second-largest source of employment after agriculture, play a pivotal role in promoting inclusive economic growth. In Karnataka, particularly in Dakshina Kannada district, MSMEs constitute the backbone of rural industrialization and employment generation. This study examines the role of e-commerce in promoting rural MSMEs in Dakshina Kannada district and further analyzes how digital platforms enhance market access, improve price realization, encourage women-led entrepreneurship, and foster financial inclusion. The major findings indicate that e-commerce has significantly expanded market reach, strengthened rural producer networks, and generated new employment opportunities through digital engagement and supply chain integration. The study concludes that e-commerce represents a transformative instrument for rural economic development in Dakshina Kannada. With coordinated support from government agencies, digital platforms, and local institutions, rural MSMEs can leverage digital markets to achieve sustainable growth, enhance competitiveness, and contribute more effectively to regional and state economic development. Keywords: E-commerce, MSMEs, Dakshina Kannada, and Karnataka

ABSTRACT In the ever-evolving realm of global supply chain management, optimizing corporate supply chain performance is paramount for sustaining competitiveness. Based on data of Chinese A-share listed companies from 2009 to 2022, we harness the Supply Chain Innovation and Application Pilots (SCIAP) policy in China as a quasi-natural experiment to empirically scrutinize the impact of supply chain digitalization on corporate supply chain performance. Our findings suggest that SCIAP policy significantly improves enterprises’ supply chain performance by facilitating corporate digital transformation, promoting the vertical integration of supply chains and alleviating corporate financing constraints. There is significant heterogeneity in the effectiveness of digital transformation policy: compared to large-scale companies, the supply chain performance of small-scale enterprises has significantly improved as a result of digital transformation; the SCIAP policy exerts more pronounced effects on enterprises with high supply chain risks, which indicates that vulnerable groups benefit more from digital transformation. Furthermore, companies located in regions with high municipal government digital attention experience more favourable impacts from pilot policies. These insights contribute to the theoretical and practical understanding of supply chain digitalization, offering a foundation for developing high-quality, modern supply chain systems.

ABSTRACT This study examines the influence of circular economy practices, as a manifestation of corporate social responsibility, on green value co‐creation and its subsequent effects on green collaborative practices and sustainable supply chain integration between providers and customers. Grounded in signaling theory, the proposed model also investigates the moderating role of greenwashing on these relationships. Using structural equation modelling on a sample of 221 supplier–customer dyads in Portugal, results demonstrate that circular economy initiatives significantly enhance green value co‐creation, which in turn fosters green collaborative practices and sustainable supply chain integration. The moderating effect of greenwashing negatively impacts the perceived authenticity of these initiatives, thereby weakening their effectiveness. This research advances theoretical understanding of the mechanisms driving sustainable interorganizational collaboration and provides managerial insights for enhancing green supply chain strategies in the context of increasing environmental scrutiny.

Counterfeit and substandard pharmaceuticals represent a critical global health crisis, with the World Health Organisation (WHO) reporting that falsified medicines comprise 10% of the global pharmaceutical trade, constituting one of the fastest-growing grey economies worldwide. This illicit market affects all regions, including high-income countries, causing devastating health and economic consequences that contribute to increased mortality and morbidity rates. The problem is particularly severe in developing nations with inadequate regulatory systems. Mobile health (mHealth) technologies have emerged as promising solutions for enhancing pharmaceutical supply chain integrity. However, comprehensive frameworks that integrate multiple authentication mechanisms remain limited in addressing the growing counterfeit drug crisis. The primary objective of this study is to design and implement a novel mobile health framework that integrates innovative packaging technology and computer vision to enhance pharmaceutical integrity and patient safety. This innovative approach enables consumers to authenticate genuine drugs and detect counterfeit/spurious products through advanced Quick Response (QR) code verification systems and artificial intelligence- powered tablet recognition capabilities. The proposed system utilises individual strip QR codes that enable real-time scanning at the point of sale to verify drug authenticity, directly addressing critical gaps identified in current pharmaceutical authentication methods. To overcome the practical challenge of expiration date loss when medication strips are cut at pharmacies, we developed a computer vision-based Artificial Intelligence (AI) model that automatically recognises the number of tablets remaining in a strip and correlates this information with the unique Identifier (ID). This approach leverages recent advances in computer vision for pharmaceutical applications and automated packaging inspection technologies. Each medication strip is assigned to an individual customer at the time of purchase, with pharmacists recording detailed customer information to ensure comprehensive tracking and accountability throughout the pharmaceutical supply chain. The integrated mobile application creates a robust anti-counterfeiting ecosystem by combining secure QR code authentication with intelligent visual recognition capabilities. The computer vision model provides accurate tablet counting and strip identification, maintaining continuity of medication tracking even when packaging is modified during dispensing processes, thus significantly enhancing supply chain transparency. This dual-authentication approach builds consumer confidence in pharmaceutical authenticity while directly addressing critical vulnerabilities identified in current regulatory frameworks. This comprehensive mobile health solution provides a scalable, evidence-based approach to pharmaceutical authentication that can be readily implemented across diverse healthcare systems globally, offering substantial potential for reducing the circulation of falsified medicines and improving patient safety outcomes in the ongoing battle against the pandemic of counterfeit pharmaceuticals. To overcome the practical challenge of expiration date loss when medication strips are cut at pharmacies, we developed a computer vision-based Artificial Intelligence (AI) model that automatically recognises the number of tablets remaining in a strip and correlates this information with the unique Identifier (ID). This approach leverages recent advances in computer vision for pharmaceutical applications and automated packaging inspection technologies. Each medication strip is assigned to an individual customer at the time of purchase, with pharmacists recording detailed customer information to ensure comprehensive tracking and accountability throughout the pharmaceutical supply chain. The integrated mobile application creates a robust anti-counterfeiting ecosystem by combining secure QR code authentication with intelligent visual recognition capabilities. The computer vision model provides accurate tablet counting and strip identification, maintaining continuity of medication tracking even when packaging is modified during dispensing processes, thus significantly enhancing supply chain transparency. This dual-authentication approach builds consumer confidence in pharmaceutical authenticity while directly addressing critical vulnerabilities identified in current regulatory frameworks. This comprehensive mobile health solution provides a scalable, evidence-based approach to pharmaceutical authentication that can be readily implemented across diverse healthcare systems globally, offering substantial potential for reducing the circulation of falsified medicines and improving patient safety outcomes in the ongoing battle against the pandemic of counterfeit pharmaceuticals.