Search Results (158)

The integration of Artificial Intelligence (AI) and Big Data is increasingly associated with firms’ resilience in dynamic business environments. This study examines the relationships between AI–Big Data integration, AI capabilities, supply chain agility, and firm resilience, with particular attention paid to the mediating roles of AI capabilities and supply chain agility. Data were collected from 475 experts across firms in Saudi Arabia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI–Big Data integration is positively associated with AI capabilities and supply chain agility, both of which, in turn, significantly contribute to firm resilience. In addition, AI capabilities show a direct positive relationship with supply chain agility. The findings further confirm the mediating roles of AI capabilities and supply chain agility in strengthening organizational resilience. This study contributes to the Dynamic Capabilities View (DCV) and Knowledge-Based View (KBV) by empirically examining how integrated AI–Big Data relates to capability development and firm outcomes. The results also provide implications for managers seeking to align AI and Big Data initiatives with supply chain capabilities to support resilience in dynamic environments. Full article

Background: Research on what promotes agility and resilience in the supply chain from an organizational strategy perspective is limited. This paper profiles the factors that can enable supply chain agility and resilience, with a special emphasis on organizational strategy. Method: Using an exploratory approach, the study first identifies the enablers of supply chain agility and resilience and then applies Fuzzy Total Interpretive Structural Modeling (Fuzzy TISM) to rank them. Data were collected from experts using a literature-derived knowledge base. Results: The findings reveal key resource and knowledge-based enablers (Integration, both internal and external; Knowledge Management; Culture for Flexibility, Risk Management, Innovation, Organizational Ambidexterity, Absorptive Capacity, and Collaborative Communication) that strengthen resilience and agility, offering insights into mitigating disruptions caused by macro- and micro-level factors and global interdependencies. Conclusions: The study contributes by exploring the enablers of supply chain agility and resilience through an organizational strategy lens. By applying a rent-yielding mechanism grounded in resource and dynamic capability theories, the study advances theoretical maturity in this domain from an emerging-country context. Full article

Against the background of climate uncertainty, market volatility, and evolving regulatory environments, firms embedded in agricultural value chains face increasing pressure to maintain sustainable growth. This study examines China’s A-share-listed Traditional Chinese Medicine (TCM) firms to explore how internal organizational factors and external institutional conditions jointly shape firm-level sustainable growth capability. This setting is characterized by strong ecological dependence, strict quality regulation, deep policy embeddedness, and supply-chain sensitivity. Drawing on the resource-based view, dynamic capability theory, contingency theory, and the institutional environment perspective, this study applies fuzzy-set qualitative comparative analysis (fsQCA) to 2023 cross-sectional data from 59 A-share-listed TCM firms. The results show that no single condition constitutes a necessary condition for high sustainable growth capability. Instead, high sustainable growth capability is mainly achieved through three configurational pathways: innovation-driven growth, policy-supported development, and market-responsive strategy. Low sustainable growth capability follows asymmetric pathways, mainly reflected in the mismatch between innovation capability and the institutional environment, and the coexistence of high financing constraints and low agility response. The findings indicate that sustainable growth capability is not the result of isolated factors, but a context-specific configurational outcome shaped by innovation, agility response, internationalization, equity governance, ESG performance, government support, marketization level, and financing conditions. This study provides a configurational explanation for growth research on agriculture-related firms and offers differentiated pathway implications for managers and policymakers. Full article

Real-time coordination across OEM–Tier-1 manufacturing networks remains challenging due to delayed shop-floor data, stochastic machine availability, and the need for schedule stability. This paper presents a protocol-agnostic, two-stage mixed-integer linear programming (MILP) framework for real-time family-level scheduling. The method integrates MTConnect-like data streams without requiring adherence to any single communication standard. In Stage 1, a baseline plan is generated using expected capacity; in Stage 2, a rolling-horizon recourse model adapts the plan to observed (possibly lagged) capacity while incorporating a stability penalty to control resequencing. A synthetic OEM–Tier-1 testbed with three machines (two Tier-1, one OEM) is used to benchmark performance under real-time (L = 0) and delayed (L = 5) data scenarios. Across these scenarios, the real-time rolling scheduler improves strict on-time fulfillment by approximately 70% and eliminates terminal backlog relative to static planning, while MILP solve times remain under 0.1 s per cycle. Sensitivity experiments that vary disruption intensity, replanning interval (Δ), and stability weight (λ) show consistent qualitative trends and illustrate how the framework can be tuned to balance service performance against schedule stability without sacrificing computational tractability. Full article

The escalation of geopolitical tensions has forced global manufacturers to reconfigure their supply chains. While Digital Transformation (DT) is widely touted as a primary driver of resilience, traditional variance-based research often assumes a symmetric, linear relationship that applies universally across firms. This study challenges this assumption through the lens of Complexity Theory. Viewing supply chains as Complex Adaptive Systems (CASs), we employ Fuzzy-Set Qualitative Comparative Analysis (fsQCA) on a stratified sample of 928 manufacturers in a geopolitical high-risk zone (Taiwan). We identify equifinal pathways to Organizational Resilience, revealing a fundamental asymmetry between organizational types. The results suggest that while large enterprises rely on a resource-intensive strategy—which we term the “Digital Fortress” configurational metaphor (combining high digital maturity and agility as a core condition)—SMEs can achieve high resilience through an “Agile Dodger” configuration, leveraging operational agility and niche positioning without necessitating high digital maturity. This study contributes to the systems literature by mapping the “topology of resilience” and offering tailored configurational pathways that complement traditional variance-based perspectives in volatile ecosystems. Full article

The fashion industry’s business is becoming increasingly complicated and active. This industry is expected to be highly competitive, particularly in the retail sector. Therefore, this research aims to examine the impact of supply chain information sharing and quality management on firm performance, with supply chain agility as a mediating variable, in the Asian fashion industry. A total of 169 participants from the fashion sector in a developing country were surveyed. The proposed hypotheses were examined using a quantitative approach, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to assess and validate the measurement model. The results indicate that supply chain information sharing and quality management have a significant impact on a firm’s performance. Similarly, the sharing of supply chain information and quality management has a significant impact on firm performance by mediating supply chain agility. The study offers actionable insights for managers in volatile fashion supply chains. Firms can enhance performance by sharing real-time demand and inventory information, strengthening key quality practices, and adopting flexible, data-driven production processes. Integrating information sharing, quality management, and agility enables faster responses to shifting consumer trends, thereby improving overall competitiveness in fast-fashion environments. This study offers valuable guidance for supply chain professionals seeking to enhance practices within their networks. The results underscore the strategic importance of information sharing and quality management in promoting agility, an essential capability for achieving a competitive advantage. Additionally, the insights generated are relevant to practitioners, policymakers, and industry leaders aiming to strengthen supply chain responsiveness and resilience. Full article

Embedded devices in modern power systems offer increased connectivity and remote reprogrammability/reconfigurability. These features along with interconnections between Information Technology (IT) and Operational Technology (OT) networks enable greater agility, reduced operator workload, and enhanced power system performance and capabilities, as well as expanding the cyber-attack surface. This increased cyber-attack surface, as well as increasingly complex, diverse, and potentially untrustworthy software/hardware supply chains, increases the need for robust real-time monitoring in power systems, and more generally in cyber–physical systems (CPS). We propose a novel framework for real-time monitoring and anomaly detection in CPS, specifically smart grid substations and SCADA systems. The proposed framework enables real-time signal temporal logic condition-based anomaly monitoring by processing raw captured packets from the communication network through a hierarchical semantic extraction and tag processing pipeline into a time series of semantic events and observations, that are then evaluated against expected temporal properties to detect and localize anomalies. We demonstrate the efficacy of our methodology on a hardware in the loop (HITL) testbed under several attack scenarios. The HITL testbed includes multiple physical power system devices (real-time automation controllers and relays) and simulated devices (Phasor Measurement Units—PMUs, relays, Phasor Data Concentrators—PDCs), all interfaced to a dynamic power system simulator. Full article

COVID-19 supply-chain disruptions clearly illustrated deficiencies in central coordination. Meaningful improvement in the central coordination of supply-chains will require transparency into resource stocks and flows. The latest technology, like 5G, blockchain and IoT, are primed to provide this transparency for collaboration during crises. This will improve agility and service, reduce inventory and enable reverse logistics benefits. Furthermore, transparent global networks can allow a more inclusive and equitable distribution of critical supply, yielding quicker resolution during crises. However, many challenges exist that suggest further delay in the adoption of a holistic and transparent digitalized supply chain. This paper explores the most recent pandemic with attention to the limiting factors at all levels of emergent global crisis response. Full article

Background: This study examines the influence of AI-driven supply chains on the adoption of automation and robotics within Jordanian manufacturing firms, emphasizing the role of supply chain adaptability and agility as mediators and market turbulence as a moderator. Methods: Drawing on dynamic capabilities theory and institutional theory, the study develops a conceptual model and tests it using data collected from 337 managers through an online survey. The analysis was carried out through partial least squares structural equation modeling (PLS-SEM). Results: The results show that AI-driven supply chains significantly enhance both adaptability and agility. However, only agility has a direct and significant effect on the adoption of automation and robotics, while market turbulence significantly moderates the connection between supply chain adaptability and the adoption of automation and robotics, but not the relationship between agility and adoption. Conclusions: Theoretically, the study provides insight into the interplay among internal dynamic capabilities in shaping technology adoption under external uncertainty. These results provide actionable implications for managers operating in developing economies like Jordan, highlighting the significance of building agile capabilities and adopting AI technologies to support innovation. The study is limited by its focus on a single country and sector; future research should explore other industries and incorporate additional moderating or mediating variables. Full article

Digital transformation offers significant potential to reshape supply chains; however, implementation efforts remain fragmented, technology-centric, and insufficiently aligned with strategic, organizational, and sustainability goals. Existing frameworks and maturity models tend to emphasize the technological dimension, offering limited guidance on how digital transformation should be integrated with people, processes, culture, and sustainability at the supply network level. Building on evidence synthesized through an umbrella review of the state of the art, this paper proposes the Agile and Sustainable Supply Network Compass, a holistic and actionable framework designed to support organizations in advancing toward agile and sustainable supply networks. The Compass incorporates three structural dimensions—Strategy, Processes, and Capabilities (related to digitalization and sustainability)—as foundational pillars for transformation. We hypothesize that an effective transformation requires the joint alignment of strategy, cross-functional processes, and capabilities, as well as the explicit identification of a reduced supply network, a focal firm, and its critical linkages. The results show that positioning agility and sustainability as shared strategic objectives at the supply network level enables coherent decision-making, targeted capability development and improved coordination across interconnected actors. Rather than prescribing specific technologies, the proposed framework provides a guiding methodological logic that explains how digitalization and sustainability can co-evolve within supply networks. This work contributes to both theory and practice by bridging conceptual gaps in the literature and establishing the groundwork for future maturity models and empirical applications. Full article

Quantum computing threatens the security foundations of global financial systems, exposing long-lived data and signed digital assets to “harvest-now, decrypt-later” attacks. While the timeline for cryptographically relevant quantum computers remains uncertain, regulatory signals from the USA, UK, EU, Canada, and Australia converge: financial institutions and payment infrastructures must begin migrating to post-quantum cryptography (PQC) now to preserve confidentiality, integrity, and systemic stability. This paper maps emerging standards and roadmaps, contrasting binding requirements like the EU’s DORA crypto-agility provisions with non-binding guidance from NIST, ENISA, and ETSI. Despite a shared intent to secure high-risk use cases by 2030–2031 and complete migration by 2035, divergences in enforcement and milestones create uncertainty for cross-border banks and financial market infrastructures. In parallel, technical adoption is advancing: major browsers, cryptographic libraries (OpenSSL/BoringSSL), and CDNs (e.g., AWS CloudFront) have deployed hybrid PQC key exchange in TLS 1.3, proving confidentiality defenses are viable at internet scale. The paper synthesizes historical transition lessons, sector-specific regulatory drivers, and operational constraints in payment infrastructures to derive a new, principle-based migration: crypto-agility, risk-prioritized scoping, hybrid deployment, vendor and supply-chain alignment, independent testing, and proactive supervisory engagement. Acting now reduces long-tail exposure and ensures readiness for imminent compliance and interoperability deadlines. Full article

Acknowledging recent breakthroughs in the context of deep bio-inspired neural networks, several architectural deep network options have been deployed to create intelligent systems. The foundations of convolutional neural networks are influenced by hierarchical processing in the visual cortex. The graph neural networks mimic the communication of biological neurons. Considering these two computation methods, a novel deep ensemble network is used to propose a bio-inspired deep graph network for creating an intelligent supply chain model. An automated smart supply chain helps to create a more agile, resilient and sustainable system. Improving the sustainability of the network plays a key role in the efficiency of the supply chain’s performance. The proposed bio-inspired Chebyshev ensemble graph network (Ch-EGN) is hybrid learning for creating an intelligent supply chain. The functionality of the proposed deep network is assessed on two different databases including SupplyGraph and DataCo for risk administration, enhancing supply chain sustainability, identifying hidden risks and increasing the supply chain’s transparency. An average accuracy of 98.95% is obtained using the proposed network for automatic delivery status prediction. The performance metrics regarding multi-class categorization scenarios of the intelligent supply chain confirm the efficiency of the proposed bio-inspired approach for sustainability and risk management. Full article

Background: Digital transformation has become a vital driver of competitiveness in the hospitality industry. This study investigates the role of digital agility in enhancing competitive advantage in Egypt’s luxury hotel sector, focusing on the parallel mediating effects of supply chain agility and supply chain resilience. Grounded in the Dynamic Capabilities Theory (DCT), the research explores how digital capabilities promote flexibility, responsiveness, and strategic performance. Methods: Data were collected from 325 senior managers in supply chain, procurement, operations, logistics, and digital transformation across luxury hotels in Egypt. The conceptual framework was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via WarpPLS software. Results: Findings reveal that digital agility significantly enhances competitive advantage, as well as supply chain agility and resilience. Both supply chain agility and resilience positively influence competitive advantage and partially mediate the relationship between digital agility and competitiveness. Conclusions: The study highlights the strategic importance of digital agility in fostering agile and resilient supply chains, which serve as key mechanisms for achieving sustained competitive advantage in the luxury hotel industry. Investing in digital technologies and adaptive capabilities is essential for long-term success in a dynamic market environment. Full article

Ongoing global disruptions, including pandemics, geopolitical tensions, and climate-driven events, have exposed vulnerabilities in healthcare supply chains (HSCs). This study examines how artificial intelligence (AI) is reshaping HSCs to improve agility, resilience, and sustainable performance. Using a systematic literature review with PRISMA-style screening across Scopus and Web of Science, the study is complemented by bibliometric analysis and latent Dirichlet allocation topic modeling to analyze peer-reviewed articles. The results indicate an exponential increase in AI-enabled HSC research, concentrated in a small number of journals and spanning a globally diverse author community. Three dominant thematic clusters emerged: (1) sustainability-oriented supply chain design, (2) disruption and resilience management, and (3) healthcare-focused digital transformation. Across these themes, AI, digital twins, Internet of Things, and simulation are evolving from efficiency tools to strategic enablers of decision intelligence, supporting real-time sensing, scenario analysis, and proactive risk mitigation. The study highlights a convergence of “triple transformation” in which digitalization, resilience, and sustainability are increasingly co-dependent capabilities in HSCs. However, persistent barriers exist, including data quality issues, legacy systems, workforce skill gaps, limited model interpretability, and incomplete governance frameworks, which constrain large-scale adoption. The findings indicate a need for longitudinal and multi-method studies on human–AI collaboration, trust calibration, and leadership in AI-enabled HSCs. This study provides practical guidance for healthcare organizations looking to leverage AI in developing agile, resilient, and sustainable supply chain ecosystems. Full article

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. Full article