Technology or innovation alone is not enough: A SEM-fsQCA based study on intelligent automation driven supply chain resilience

This study utilizes the dynamic capability framework to investigate the relationship among Supply chain (SC) adaptability, SC alignment, SC ambidexterity, SC competence, and SC flexibility fit in blockchain technology (BCT) adoption to understand their interactions and contribution to firm performance (FPER). SCs are considered the primary mechanism by which businesses impact all aspects of our daily needs; however, the challenges caused represent an opportunity to integrate BCT, which may fundamentally alter the future of SCs. Anchored in the Dynamic Capability (DC) framework, this study leverages SmartPLS to examine insights from 283 survey participants across manufacturing firms in the UK and US. SC adaptability and SC alignment influence SC ambidexterity; SC ambidexterity positively influences SC flexibility fit, and SC flexibility fit influences SC competence. SC competence has a full mediation influence between SC flexibility fit and FPER. BCT moderates the relationship between SC ambidexterity and SC flexibility fit but not between SC competence and FPER. This study advances knowledge on how BCT deployment shapes supply chains, underscoring the critical role of a robust digital strategy in driving FPER. By reframing the discourse around competencies and strategic alignment, this study not only enriches the literature on digital transformation and supply chains but also opens new avenues for future research and managerial practice. Organizations need to think holistically rather than focusing on acquiring the best IT systems and bombarding employees with training to develop their competencies. • Blockchain-enabled dynamic capabilities enhance supply chain ambidexterity, flexibility fit, and competence. • SC competence mediates the effect of flexibility fit on firm performance. • Blockchain moderates the ambidexterity–flexibility fit relationship. • Holistic digital strategy and alignment of capabilities are critical for leveraging blockchain in supply chains.

Abstract Metaverse technologies, specifically digital twins (DT) and blockchain, are increasingly influencing the evolution of supply chain management (SCM). This study explores how these technologies support adaptability, flexibility, and resilience in complex environments. Using a mixed-method approach incorporating BERTopic modeling, the research identifies four thematic areas: real-time operational optimization via DTs; immersive workforce training through AR/VR; transparency improvements via blockchain; and Industry 5.0 technological trends. The result indicated the DTs in developing flexible, adaptive supply chains by facilitating predictive analytics, dynamic scenario planning, and stakeholder coordination. Integrated with machine learning and blockchain, DTs help optimize responsiveness, collaboration, and reconfiguration strategies, core principles of flexible systems management. The study further applies the antecedents–decisions–outcomes (ADO) framework to propose an outline for further research avenues and practice, grounded in three key questions focusing on how metaverse technologies enhance flexibility, support collaboration, and advance the sustainability–resilience nexus. The analysis also identifies governance, scalability, and ethical implementation as persistent challenges. Methodologically, the work demonstrates how topic modeling can support theory development in digitally transformed organizational systems. Expected outcomes include greater operational agility, improved sustainability alignment, and enhanced trust across supply chain networks. These insights contribute to ongoing efforts to build digitally enabled, ethically governed, and dynamically flexible supply chains aligned with United Nations Sustainable Development Goals (SDG) such as SDG 8 (work), SDG 9 (innovation), SDG 12 (consumption), SDG 13 (climate), and SDG 16 (governance).

The evolution of logistics strategies in Ukraine’s grocery retail sector reflects deep structural transformations in the economic environment driven by the interaction of structural shifts, crisis-related disruptions, and the rapid digitalization of retail processes. In contemporary conditions, logistics is no longer limited to an operational support function but is increasingly recognized as a strategic mechanism that ensures resilience, adaptability, and long-term competitiveness of retail enterprises. The article explores the key stages of transformation of logistics strategies, emphasizing the shift from cost-oriented optimization toward integrated supply chain management focused on ensuring continuity of supply, flexibility of logistics infrastructure, and the development of collaborative partner networks. The study demonstrates that modern logistics strategies are shaped by a complex set of crisis-related factors, including war-related risks, inflationary pressures, energy constraints, and regional disparities in logistics infrastructure. These factors significantly reshape traditional approaches to logistics management and necessitate the development of adaptive supply chain models capable of maintaining stable food supply under conditions of heightened uncertainty. Particular attention is paid to the growing role of digital transformation in logistics, including the implementation of omnichannel retail models, advanced demand analytics, and integrated information systems that enhance transparency, coordination, and responsiveness of logistics operations. It is argued that logistics strategizing in grocery retail should be understood as a multidimensional process aimed at balancing operational efficiency, system resilience, and adaptive capacity in a rapidly changing economic environment. The findings indicate that the future development of logistics strategies will be closely linked to the integration of innovative technologies, expansion of digital logistics platforms, and the formation of flexible and resilient supply chain structures. These transformations are expected to strengthen the competitive position of retail enterprises while simultaneously enhancing the stability and sustainability of food supply systems at the national level.

ABSTRACT Geopolitical disruptions have become recurrent and materially reshape global supply chains, particularly for innovative new products with limited life cycles. These disruptions not only constrain production capacity and supplier availability but also alter market potential and demand diffusion through consumer interactions. However, existing studies rarely examine how recurrent geopolitical disruptions, occurring at different stages of a product's life cycle, jointly affect new product diffusion and supply chain configuration. This study develops an AI-enabled, Physical Internet–based multi-period supply chain configuration model that integrates production–sales planning, dynamic demand evolution and flexible supplier selection. The proposed framework captures how geopolitical disruptions dynamically influence market potential, instant demand, processing costs, lead times and logistics options across the product life cycle. Our results show that early-stage disruptions substantially reduce profitability and trigger downstream supplier switching due to constrained capacity and impeded inventory buildup, while mid-period disruptions dampen demand volatility and inventory costs. Moreover, adaptive configuration through multiple planning horizons – enabled by AI-driven decision-making under the Physical Internet paradigm – can partially mitigate the adverse impacts of short-term disruptions. The findings offer actionable insights for designing resilient, data-driven global supply chains in an increasingly uncertain geopolitical environment.

The article investigates the specific features and modern approaches of working capital management among the largest international corporations operating in the fast-moving consumer goods market. The global consumer goods sector is characterized by massive production volumes, rapid inventory turnover, and relatively low operating margins, therefore it requires highly efficient financial management approaches focused primarily on cash flow optimization rather than simple markup maximization. Based on a comprehensive empirical analysis of 46 leading public companies selected from the S&P Global 1200 Consumer Staples index, the study explores how market leaders structurally organize their liquidity. The research confirms that for global industry giants that are maintaining negative working capital and a negative cash conversion cycle is not an indicator of financial distress or inadequate liquidity management, but rather an intentional, highly efficient operational strategy. This strategy relies heavily on maximizing trade credit and extending terms of payments for accounts payable. The paper also highlights significant strategic differentiation based on sub-industry specifics and geographical origin. Western food and beverage corporations often pursue aggressive liquidity minimization, companies dealing with premium cosmetics or volatile raw materials (like the confectionery sector) require longer inventory holding periods and, consequently, distinct financing models. Japanese corporations demonstrate a different, conservative corporate finance model, prioritizing high liquidity buffers to ensure absolute financial stability against macroeconomic shocks. A critical aspect of the study is the evaluation of how the digital transformation directly influences corporate financial strategies. The integration of advanced enterprise resource planning systems enables multinational companies to manage inventory and cash flows in real-time, fundamentally altering traditional liquidity management frameworks. The deployment of digital tools such as reverse factoring platforms, automated predictive inventory planning via IoT, and deep data integration with suppliers serves as a primary catalyst for reducing the cash conversion cycle. Consequently, the ability to establish adaptive supply chains and seamlessly embed fintech solutions into daily operations remains the defining factor for sustaining financial competitiveness in the global consumer goods sector.

Purpose This conceptual paper examines how artificial intelligence (AI) can be systematically integrated into supply chain management (SCM) training to support sustainable management objectives. It focuses on how AI-enabled training can enhance strategic adaptability and risk-aware decision-making to increase operational efficiency in increasingly volatile and sustainability-oriented supply chains. Design/methodology/approach This study adopts an integrative literature-based approach, drawing on publications from 2010–2024 across the SCM, AI, and training and development domains. Guided by the resource-based view, transaction cost economics and the dynamic capabilities framework, the paper synthesizes this literature into thematic clusters and develops a conceptual framework for AI-driven supply chain training. The review process follows a PRISMA-inspired structure to transparently report the identification, screening, eligibility and inclusion of relevant studies. Findings The analysis reveals that AI-enabled training, through simulations, real-time decision-making exercises, and predictive analytics, can enhance dynamic capabilities, reduce coordination and information costs, and foster data-driven competencies. These mechanisms collectively enhance supply chain resilience, risk management, and sustainability performance. The paper identifies five strategic insights that connect AI capabilities to training design, demonstrating how AI can move SCM training from static, process-oriented models toward adaptive, learning-oriented architectures. Originality/value This paper contributes by bridging classical SCM theories and AI-enabled training, a relatively underexplored area in supply chain education. By bridging the gap between traditional SCM theories and the practical needs of modern digital operations, the paper offers novel insights into AI’s transformational potential in enhancing supply chain adaptability and managerial decision-making. Additionally, it sets a foundation for future empirical research to validate and expand upon the proposed theoretical constructs.

Abstract The Manufacturing-as-a-Service (MaaS) model has been proposed by the research community and has been given significant attention by the industry as it holds the promise for agility and resilience of production networks. However, there are significant challenges (such as unforeseen events) that should be overcome for successful implementation of the MaaS model. The objective of this work is to enable companies to mitigate both short- and medium-term unforeseen events and to support them to participate in a dynamic MaaS network. To address these needs, five research objectives are suggested and discussed: 1) actionable propositions for adapting supply chains or internal production; 2) dynamic supply chain generation; 3) building trust in MaaS networks; 4) dynamic planning and scheduling of production processes; 5) dynamic assembly and disassembly. Moreover, a novel digital platform for implementing and integrating these objectives is presented. This paper presents the architectural foundations and methodological framework; empirical validation through two industrial pilots—one from the automotive domain (Continental, CZ) and one from the bicycle industry (Aska, BE)—is ongoing, with the validation methodology and target metrics defined herein. The work takes place in the context of the research and innovation action (RIA) project RAASCEMAN (Resilient and Adaptive Supply Chains for Capability-based Manufacturing as a Service Networks), funded by the Horizon Europe program.

A dual-path driving mechanism study of digital transformation on supply chain adaptation from the asymmetric network power perspective

The transition from data-driven to cognitively adaptive supply chains represent a critical step toward Industry 6.0, where learning, coordination, and sustainability must be addressed jointly. Existing supply chain analytics approaches remain limited in capturing adaptive and systemic behaviors under uncertainty, particularly in resource- and energy-intensive industrial contexts. This study proposes a Cognitive and Data-Driven Framework for Supply Chains based on federated learning and synthetic data simulations grounded in aggregated industrial benchmarks. The framework introduces the Adaptivity Coefficient (A c ), a composite metric integrating learning velocity, anticipatory responsiveness, and technological exposure to quantify cognitive readiness at the network level. Results from simulation experiments show that cognitively adaptive supply chains achieve significant performance improvements compared to conventional predictive approaches. Specifically, cognitive coordination reduces cumulative disruption costs by 18–25%, lowers emissions intensity by up to 15%, and shortens recovery time by approximately 27%. The analysis further demonstrates that adaptive learning expands the Pareto-efficient frontier, enabling simultaneous gains in cost efficiency, resilience, and environmental performance under varying levels of uncertainty. These findings suggest that cognitive adaptivity functions as a strategic capability rather than a purely technical feature. The study concludes by highlighting the managerial and policy implications of embedding cognitive learning into supply chain governance and by outlining pathways for future empirical validation in hard-to-abate manufacturing sectors. • A Cognitive and Data-Driven Framework is proposed to model Industry 6.0 supply chains. • Synthetic data and federated learning are used to simulate adaptive and sustainable behaviors. • The Adaptivity Coefficient (Ac) quantifies network readiness and cognitive performance. • Cognitive adaptivity simultaneously improves cost efficiency, resilience, and environmental outcomes. • The study outlines governance and validation pathways for systemic change in hard-to-abate industries.

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.

ABSTRACT Purpose Previous studies have highlighted that B2B supply chain design in the context of new product development (NPD) can contribute to the success of innovative products on the market. However, there is limited empirical literature regarding the role of B2B supply chain characteristics/capabilities in improving the NPD effectiveness. The aim of this article is to investigate the interrelationship between B2B supply chain dynamic capabilities and their influence on NPD effectiveness. Methodology/approach We have adopted the dynamic capability view (DCV) to explain the combination of resources and capabilities (i.e. technology resources, agility, and adaptability) to explain the improvement of NPD effectiveness. Using structural equation modeling, this study conducted empirical tests on 214 Chinese manufacturing companies. Findings Research findings indicate that supply chain agility and supply chain adaptability exert positive effects on NPD effectiveness. AI-driven big data analytics capabilities, as a prerequisite, positively influence both supply chain agility and supply chain adaptability. Furthermore, supply chain agility partially mediates the relationship between supply chain adaptability and NPD effectiveness. Environmental dynamism exerts a negative moderating effect on the relationship between supply chain agility and NPD effectiveness. Originality/value Overall, this study contributes to NPD effectiveness research by integrating the supply chain dynamic capabilities theory with an AI-driven innovation framework. By drawing on the dynamic capabilities lens, our research reveals the mechanism through which supply chain dynamic capabilities influence NPD effectiveness, thereby advancing our understanding of NPD effectiveness. We position AI-driven big data analytics capability, supply chain agility, and supply chain adaptability as a coherent cluster of supply chain dynamic capabilities. This positioning indicates that organizations’ effective management of supply chain processes constitutes a key pathway for significantly enhancing NPD effectiveness. Practical implications We believe that our research findings will be useful for managers who have a positive and optimistic attitude toward using new technologies to influence supply chain characteristics to support NPD.

The COVID-19 pandemic exposed deep-seated structural vulnerabilities within global value chains (GVCs), severely disrupting cross-border production networks, logistics systems, and trade flows. These disruptions have intensified the imperative to reconfigure GVCs to enhance resilience, adaptability, and long-term sustainability. This theoretical study examines the role of digital trade including e-commerce platforms, blockchain technologies, artificial intelligence (AI), and data-driven logistics in transforming GVCs within a post-pandemic, data-intensive global economy. Drawing upon interdisciplinary insights from international trade theory, supply chain management, and digital governance literature, the paper develops a conceptual framework that links digital trade enablers with adaptive supply chain strategies. The study underscores the critical role of digital infrastructure, institutional capacity, and regulatory harmonisation in building resilient and inclusive trade ecosystems. The proposed framework incorporates key constructs such as predictive analytics, smart contracts, real time supply chain visibility, and paperless customs procedures to demonstrate how digital technologies enhance transparency, operational agility, and risk mitigation across GVCs. Furthermore, the paper analyses emerging policy instruments including the Digital Economy Partnership Agreement (DEPA), ongoing WTO e-commerce negotiations, and ESG aligned trade metrics under global reporting frameworks such as GRI and ISSB that increasingly shape the governance of digital trade. Special emphasis is placed on the integration of micro, small, and medium enterprises (MSMEs) and public sector enterprises in developing economies, with particular reference to India’s digital public infrastructure. The study also critically examines persistent challenges, including the digital divide, cybersecurity vulnerabilities, and fragmented data governance regimes, highlighting the necessity for coordinated international policy responses. The paper concludes by outlining future research avenues for empirical testing, sector-specific investigations, and comparative regional analysis, thereby contributing to the theoretical discourse on digital trade and post-pandemic economic resilience.

Purpose This study aims to critically investigate the presumed universal mediating role of operational efficiency between core supply chain capabilities integration, agility, adaptability and overall performance. It questions the established paradigm that positions operational efficiency as an indispensable conduit to competitive advantage, particularly within the volatile context of small and medium-sized enterprises (SMEs) in an emerging economy Pakistan. Design/methodology/approach To empirically test this theoretical contention, a quantitative, positivist approach using a cross-sectional survey of 400 Pakistani SMEs was employed. This design was chosen to objectively measure the complex relationships between latent constructs within a population facing significant volatility and resource constraints, a context that provides a critical test for universal mechanism. Established scales were rigorously adapted and pretested for contextual relevance. Data were analyzed using partial least squares structural equation modeling in R. This method was specifically selected for its ability to model complex mediation pathways without strict distributional assumptions, making it ideal for an exploratory test of the hypothesized contingencies in this emerging economy setting. Findings The results reveal a critical contingency; operational efficiency does not universally mediate the capability performance relationship. While it transmits the effects of supplier and internal integration to performance, it fails to be a significant mediator for supply chain adaptability and agility. Furthermore, customer integration, while improving operational efficiency, does not directly enhance overall performance. This demonstrates that the efficacy of operational efficiency is not automatic but is instead bounded by the type of capability deployed and the firm’s specific contextual constraints. Originality/value This research provides a significant practical recalibration by challenging the universal application of operational efficiency as a core mediator in supply chain models. To the best of the authors’ knowledge, it is the first study to empirically demonstrate that this mechanism can fail in specific, high importance contexts. The findings offer a crucial contingency perspective, emphasizing that the path to supply chain performance in emerging economies is more nuanced than prevailing theories suggest, requiring a move beyond one size fits all, efficiency centric strategies.

Traditional markets remain a vital component of Indonesia’s local economy, particularly in the distribution of ready-to-wear clothing products. However, empirical studies examining supply chain models within traditional market contexts remain limited. This study aims to analyze and model the supply chain structure of ready-to-wear clothing traders in traditional markets in Bandung City. A qualitative descriptive research design was employed, with primary data collected through in-depth interviews, field observations, and supporting literature review. Data were analyzed using descriptive-interpretative techniques, including data reduction, coding, categorization, and triangulation. The findings reveal that the supply chain structure is relatively short and simple, involving producers (garment and small-scale sewing workshops), retailers (kiosk and street vendors), and end consumers. Despite its flexibility, the supply chain relies heavily on informal coordination, social networks, and adaptive procurement strategies, which increases vulnerability to supply fluctuations and price instability. Differences in access to suppliers and inventory strategies between kiosk traders and street vendors further shape supply chain performance. This study concludes that the effectiveness of supply chain management in traditional markets cannot be assessed using modern retail standards alone. Instead, a contextual and adaptive supply chain model is required to strengthen efficiency and sustainability while preserving the inherent flexibility of traditional market systems.

Global supply chains are increasingly characterized by complexity, uncertainty, and vulnerability to disruptions, creating a pressing need for intelligent, adaptive systems that support decentralized decision-making and real-time control.This paper develops a new framework that integrates Multi-Agent Systems (MAS) with Case-Based Reasoning (CBR) to address these challenges.The model leverages autonomous agents representing suppliers, manufacturers, distributors, retailers, and coordinators that negotiate through defined protocols while embedding CBR mechanisms to retrieve and adapt historical supply chain cases for enhanced responsiveness.An optimization layer, guided by both agent heuristics and case-driven initial solutions, targets key objectives such as cost minimization, leadtime reduction, and resilience improvement.Simulation experiments were conducted under both static and dynamid environments with disruptions including supplier failures and demand fluctuations.Results demonstrate that the proposed framework achieves convergence up to 34-41% faster than heuristic-only baselines (p<0.05) and sustains solution quality with supply chain sizes increasing from 50 to 500 agents, indicating near-linear scalability.Comparative analysis further highlights adaptability in dynamic contexts and robustness under uncertainty.A case study illustrates practical deployment and validates its effectiveness.The findings provide evidence of a powerful synergy between MAS and CBR, with implications for next-generation supply chain intelligence.

The analytical examination will concentrate on elucidating transformations within supply chain management systems precipitated by crisis phenomena spanning the period from 2020 through 2024. The principal objective of this investigation is to conduct a comparative assessment of adaptive strategies employed by logistics systems under the conditions of both the COVID-19 pandemic and armed conflict in Ukraine. The methodological framework underpinning this research is grounded in a comprehensive approach that synthesizes quantitative examination of statistical indicators with qualitative evaluation of managerial practices. The results of the study demonstrate fundamental differences in the mechanisms of supply chain adaptation to different types of crises. The COVID-19 pandemic allowed for gradual adaptation with the possibility of recovery planning: companies had time to reorganize routes, search for alternative suppliers, and implement digital solutions. Military operations in Ukraine required an immediate response and a radical restructuring of logistics networks: companies were forced to move warehouses, change transportation corridors and ensure the safety of their staff within days. According to an analysis of sea freight rates, the cost of container transportation increased from $2,100 in July 2020 to $1,200 in November 2021. In the structure of Ukrainian imports in 2022, road transport accounted for 64% of the total value of all imported transportation, while in 2021 this figure did not exceed 35%. According to international technology reports, the introduction of digital twins in supply chain management increased from 5% of companies in 2020 to 34% in 2024, and the use of blockchain technologies increased from 8% to 35%, respectively. A conceptual model of adaptive management has been developed that integrates early warning systems and rapid response mechanisms. The study forms a new paradigm of management in the context of permanent instability. Keywords: supply chain management, crisis management, adaptive strategies, digital transformation, logistics resilience, COVID-19, military operations, transport logistics, crisis management, instability, control and organization in supply chain management, supply chains, optimization

From Resilience to Flexibility: A Hybrid SEM-ANN Model of AI-Enabled Adaptive Supply Chains

This study aims to analyze the influence of Collaborative Supply Chain Management (CSCM) and Supply Chain Adaptability (SCA) on the Operational Performance (OP) of the building materials industry in Indonesia. The research utilized online survey data to analyze 93 companies. The analysis of the data was conducted through PLS-SEM using the SmartPLS3 software. The results show that CSCM shows a positive but negligible impact on OP, while SCA does not have an effect on OP. However, the effect of CSCM and SCA on operational performance is significant and positive through a full mediation by operational capabilities. The conclusion emphasizes that external strategy success depends on strengthening internal resources, highlighting the importance of operational capability development as a core element for performance improvement.

Changes in the global business environment constrain companies to adopt digital technology to enhance their performance and competitiveness. This study aims to analyze the role of managerial capability on operational performance through supply chain digitalization and supply chain adaptability in manufacturing companies in Indonesia. Managerial capability is a key factor that enables organizations to design effective strategies, allocate resources efficiently, and foster internal collaboration and external partnerships. The study has distributed questionnaires to 117 respondents from various middle and top managerial functions within the manufacturing company. The questionnaire was designed using a five-point Likert scale and distributed offline and online using a Google Form link. Data analysis employed Partial Least Squares – Structural Equation Modeling (PLS-SEM) to examine the outer model and inner model. The results indicate that managerial capability has a significant impact on supply chain digitalization and supply chain adaptability, but does not directly affect operational performance. Instead, the effect of managerial capability on operational performance is mediated by supply chain adaptability and the combination of supply chain digitalization and adaptability. Supply chain digitalization plays a crucial role in enhancing supply chain adaptability, ultimately leading to a positive impact on operational performance. These findings confirm that supply chain digitalization not only increases the speed of information flow and transparency but also strengthens the company's ability to adapt to market dynamics. The practical implications of this research are the need for manufacturing companies to continuously develop managerial competencies that support digital technology investments, strengthen an adaptive organizational culture, and foster synergy between internal and external functions. For academics, this research provides a conceptual contribution in explaining the mediating relationship between managerial capabilities, supply chain digitalization, supply chain adaptability, and operational performance in the era of digital transformation.