How Industry 4.0 Technologies Enhance Supply Chain Resilience: The Interplay of Agility, Adaptability, and Customer Integration in Manufacturing Firms

PurposeThis paper positions market sensing, supply chain agility and supply chain adaptability as a coherent cluster of dynamic supply chain capabilities. The purpose of this paper is to understand how dynamic supply chain capabilities interrelate and their effect on supply chain ambidexterity.Design/methodology/approachBased on a survey of Pakistani manufacturing firms, a theoretically-derived model was tested in a structural equation model.FindingsThe results of the study show that a market-sensing capability is an antecedent of supply chain agility and supply chain adaptability. Furthermore, supply chain agility, directly, and supply chain adaptability, indirectly, affect supply chain ambidexterity. Supply chain agility, therefore, mediates the relationship between supply chain adaptability and supply chain ambidexterity.Originality/valueThe contribution of this study lies in: first, identifying dynamic capability clusters relevant for achieving supply chain ambidexterity; second, evaluating performance implications of dynamic capabilities in the supply chain, specifically supply chain agility and adaptability; and third, proposing a unique measurement of supply chain ambidexterity in the light supply chain theory, and empirically evaluating the relationship between dynamic capabilities and supply chain ambidexterity.

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.

Building responsive and resilient supply chains: Lessons from the <scp>COVID</scp>‐19 disruption

Supply chain environmental risks are pivotal situational factors that significantly influence the intricate relationship between a business’s supply chain agility, supply chain resilience, and its ultimate supply chain performance. This study aims to explore the interplay between supply chain agility, supply chain resilience, and supply chain performance, while also investigating the moderating effect of supply chain environmental risks. Data analysis was conducted using hierarchical regression based on a questionnaire survey involving 416 companies in Taiwan’s manufacturing supply chain. The findings reveal several key insights. Firstly, supply chain agility has a positive influence on supply chain resilience, highlighting the importance of a flexible and responsive supply chain to handle challenges effectively. Secondly, supply chain resilience plays a vital role in determining supply chain performance, underscoring its significance in maintaining operational efficiency and effectiveness. Furthermore, the study identifies that supply chain environmental risks can act as a positive moderator in the relationship between supply chain agility and supply chain resilience. In other words, when faced with environmental risks, companies with higher supply chain agility can leverage this capability to reinforce their supply chain resilience, leading to improved supply chain performance. Additionally, the results shed light on the mediating role of supply chain resilience between supply chain agility and supply chain performance. This suggests that a resilient supply chain acts as an intermediary mechanism through which the positive effects of supply chain agility translate into enhanced overall performance. Given the uncertain and turbulent market environment today, these findings emphasize the importance of adopting supply chain agility and supply chain resilience as indispensable business strategies. Therefore, enterprise leaders and managers should proactively implement measures to enhance these aspects of their supply chain to effectively navigate and overcome environmental risks, ultimately driving supply chain performance.

The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism

Even though research has suggested that supply chain agility and supply chain adaptability are distinct capabilities, little is known about their performance effects and about the contextual conditions under which they are effective. Based on a sample of 143 German firms, we empirically investigate the effects of supply chain agility and supply chain adaptability on cost performance and operational performance using hierarchical regression analysis. We ground our investigation in the dynamic capabilities view and contingency theory. We find that supply chain agility and supply chain adaptability positively affect both cost performance and operational performance. We further find evidence for a mediating role of supply chain agility in the links between supply chain adaptability and performance. Product complexity positively moderates the links between supply chain adaptability and cost performance, and supply chain adaptability and operational performance. The results contribute to the literature by offering a more nuanced understanding of the performance implications of supply chain agility and supply chain adaptability, thereby addressing the crucial question of why their benefits may or may not materialise under varying levels of product complexity.

This study examines the effects of supply chain agility (SCAG) and supply chain resilience (SCRES) on performance under the moderating effect of organizational culture. We have used the dynamic capability view (DCV) to conceptualize our theoretical models for different phases of humanitarian supply chain (HSC) (pre and post-disaster phases). We used partial least squares (PLS) to examine the proposed research hypotheses using 335 responses gathered from organizations in India using questionnaires designed for a single respondent. The results suggest that SCAG and SCRES are two important dynamic capabilities of supply chain, have significant effects on pre-disaster performance (PRE-DP). The control orientation does not have significant effect on the path joining SCAG and PRE-DP. However, the control orientation has a significant interaction effect on the path joining SCRES and PRE-DP. Similarly, SCRES has significant effect on post-disaster performance (POST-DP) but SCAG has no significant effect on POST-DP. In contrast, the flexible orientation has significant moderation effects on the paths SCAG/SCRES and POST-DP. These findings contribute to our understanding of the differential effect of SCAG/SCRES on supply chain performance in different contexts. The results provide further understanding to develop appropriate strategies for different phases. Finally, limitations of our study and future research are presented.

Background: Despite growing interest in supply chain resilience (SCRes), theoretical overlap between dynamic capabilities (DC) and supply chain agility (SCA) has complicated empirical analysis of their distinct roles. Additionally, the contextual role of information asymmetry in shaping resilience remains underexplored. This study addresses both issues by modeling DC hierarchically and examining IA as a moderator. Methods: Data were collected through a cross-sectional survey of 157 U.S.-based supply chain professionals. Partial least squares structural equation modeling (PLS-SEM) was used to examine the relationships among DC, SCA, IA, and SCRes. Results: SCA was a strong, direct predictor of SCRes. In contrast, DC showed no direct effect in the full model; however, in a hierarchical component model (HCM), DC, a higher-order construct, emerged as significant predictor of SCRes. IA exerted a dual negative influence: it directly weakened SCRes and negatively moderated the relationship between DC and SCRes. Conclusions: This study makes two novel contributions. First, it resolves ambiguity between DC and SCA by empirically modeling DC as a higher-order construct that encompasses but remains distinct from SCA. Second, it introduces IA as a multidimensional barrier to resilience, demonstrating its direct and interactive effects. These findings provide new insight into capability design and contextual adaptation for SCRes in uncertain, information-constrained environments.

This study empirically analyzed the impact of supply chain capabilities—agility, adaptability, and alignment—on supply chain resilience, while also examining whether organizational flexibility plays a moderating role in these relationships. A survey was conducted among domestic manufacturing firms, yielding a total of 176 responses, and the research hypotheses were tested using PLS structural equation modeling(PLS-SEM). The results revealed the following findings. First, supply chain agility had a significant positive (+) impact on supply chain adaptability, alignment, and resilience, and both adaptability and alignment also had a significant positive (+) effect on supply chain resilience. Second, in examining the mediating effect of supply chain adaptability and alignment in the relationship between supply chain agility and resilience, both adaptability and alignment were found to have a significant positive (+) mediating effect. Finally, regarding the moderating effect of organizational flexibility, it was confirmed that organizational flexibility significantly moderated the relationships between supply chain agility and both adaptability and alignment. Additionally, organizational flexibility was found to have a significant moderating effect on the relationships between supply chain agility, adaptability, alignment, and resilience. Through this study, it can be suggested that supply chain resilience is maximized not only through rapid responses to disruptions (agility) but also by incorporating long-term adaptive responses to environmental changes (adaptability) and fostering collaboration among supply chain stakeholders (alignment). Furthermore, strategic management of organizational flexibility is necessary to enhance supply chain resilience.

The study draws on the dynamic capability perspective to explore how turbulent and competitive environments influence big data analytics capabilities which, in turn, impact supply chain (SC) agility. Survey data from 201 UK manufacturers is collected and analysed, and a moderation model is presented. The results show that in turbulent environments, characterized by high degrees of environmental dynamism, firms should leverage the volume, velocity and variety facets of big data which, in turn, enable sensing and creative search (dynamic) capabilities needed to adapt in such environments. In competitive environments however, where first mover advantage is crucial, firms should scale back on time consuming search capabilities (data variety). At the operational level, firms should exclusively leverage the velocity aspects of big data to enhance SC agility. Finally, while, previous studies have focused on analytical maturity as a prerequisite to big data implementation, this study finds that a reconfigured analytical orientation culture specifically on responsiveness, i.e. strategic alignment and predictive forecasting analytics, moderates the relationship between big data velocity and SC agility. The results of this study therefore fill a key gap in the SC management literature as the study demonstrates how environmental factors, both internal and external, influence big data and dynamic capability development in order to enhance SC agility.

Purpose The COVID-19 pandemic, geopolitical conflicts, anti-globalization and the digital economy have led to accelerated changes in the market, forcing companies to use big data to achieve precise and agile product or service delivery, thereby improving performance. Existing research has not yet explored the mechanisms for data-driven supply chain agility and supply chain performance. Based on dynamic capacity theory and organizational information processing theory, this paper constructs a conceptual model to investigate how big data analytics can facilitate the implementation of high-level supply chain agility and performance through customer integration, internal integration and collaborative knowledge creation. Design/methodology/approach We collected a sample of the Chinese food industry and conducted an empirical study using partial least squares structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). Findings The results show that big data analytics has an impact on supply chain agility through three paths. Moreover, big data analytics capability and supply chain agility are considered dynamic capabilities and the effect of configuration under different conditions is empirically tested. Four solutions to improve the performance of the supply chain are obtained. Practical implications This research sheds light on the implementation process of big data-driven supply chain performance, which is of good theoretical and practical value for expanding the theory of organizational information processing and helping enterprises achieve high-level agile supply and performance. Originality/value We provide a new perspective on supply chain agility by exploring the antecedents of supply chain agility and its impact on supply chain performance from the perspective of information processing and dynamic capabilities. Existing studies have not focused on the role of big data analytic capabilities in improving supply chain agility. The purpose of this study is to attempt to establish a clear relationship between the three mediating paths (customer integration, internal integration and collaborative knowledge creation) between big data analytics capabilities and supply chain agility. In addition, we use the empirical methods of PLS-SEM and fsQCA to better substantiate the conclusions.

Background: The primary objective of this study is to investigate the influence of big data analytics (BDA) on supply chain (SC) risk, SC ambidexterity, and SC resilience. It further examines the effects of SC risk and SC ambidexterity on SC resilience and explores their mediating roles in the BDA–SC resilience relationship. Despite growing interest in BDA and resilience, limited empirical research has addressed these linkages in pharmaceutical distribution, particularly in emerging economies such as Jordan. Methods: A quantitative research strategy was adopted, employing a survey-based methodology. Data were obtained from 204 managers in pharmaceutical distribution companies in Jordan. Results: The findings indicate that BDA reduces SC risk and positively influences SC ambidexterity and SC resilience. Furthermore, SC risk and SC ambidexterity positively affect SC resilience. Notably, both variables partially mediate the BDA–SC resilience relationship, with ambidexterity showing a stronger effect. Conclusions: Grounded in the resource-based view and the dynamic capability view, this study provides empirical evidence that BDA enhances SC resilience primarily by fostering ambidexterity and mitigating risks. By clarifying the distinct mediating roles of SC risk and SC ambidexterity, the research extends theory and offers practical insights for managers seeking to build more resilient pharmaceutical SCs.

Based on the current economic environment and from the perspective of dynamic capability, this paper divides big data analysis capability into big data perception capability, big data capture capability and big data transformation capability, and divides supply chain ambidexterity into supply chain agility and supply chain adaptability. A questionnaire survey was used to quantify the impact of big data analysis capability (BDAC) on supply chain elasticity (SC-RE). A total of 300 questionnaires were distributed to managers of supply chain nodes of enterprises, and 217 valid questionnaires were obtained. The regression results of questionnaire data showed that BDAC was positively correlated with SC-RE. Supply chain duality (SC-AM) is positively correlated with SC-RE. SC-AM plays a partial mediating role in the positive effect of BDAC on SC-RE. Based on the regression results of this paper and the research results of existing scholars, the corresponding conclusions are drawn, and some suggestions are put forward for enterprise managers. BDAC is an effective way to improve the performance level and competitive strength of supply chain enterprises. Enterprises should attach importance to the useful information contained in big data, take certain measures to enhance BDAC, improve the information processing capacity and speed of enterprises, reduce the probability of risk occurrence, so as to improve the supply chain resilience of enterprises and improve the level of supply chain risk management.

The article examines the primary meanings of 'supply chain adaptability' and 'supply chain resilience'. The aim is to assess the resilience and adaptability of retail food supply chains during the declaration of martial law in Ukraine. Supply chain adaptation is focused on responding to specific changes or challenges, while supply chain resilience encompasses a broader range of measures and strategies to ensure the continuity and efficiency of the supply chain during stressful or crisis situations, such as times of civil emergency. Supply chain adaptation is the process of modifying supply chains to address specific challenges or conditions, such as changes in market conditions, technological progress, or changes in consumer demand. In situations where martial law is in effect, supply chain adaptation may include changing suppliers, revising logistics processes, seeking alternative sources of supply, and taking other measures to ensure continuity of the supply of goods and services. Supply chain resilience refers to the capacity of a supply chain to endure challenging situations and sustain operations, even in the face of adverse impacts, such as military conflict. It is important to acknowledge that supply chains can face diverse challenges, and it is crucial to maintain efficiency and productivity, irrespective of external circumstances. The main problems of implementing resilience and adaptability of the food supply chain in Ukraine during the declaration of martial law are identified to ensure the reliability and food security of the country in the context of military conflict. It suggests that studying the resilience of the food supply chain is crucial for ensuring its smooth and efficient functioning, particularly during times of crisis and stress, to maintain the country's food security. Under such conditions, there may be a risk of supply disruptions, transportation issues, reduced production, and higher food prices. It is important to consider developing and implementing strategies and measures to ensure stability and food security under martial law. This could include crisis response planning, the exploration of alternative supply routes by retailers, the organization of food reserves, and the coordination of efforts between various sectors and governing bodies. The methodology proposed by the author assesses the factors that affect the resilience of food supply chains during the declaration of martial law in Ukraine.

In the era of digital transformation (DT), achieving sustainable supply chain performance (SSCP) has become a strategic imperative for manufacturing firms. While DT is widely recognized as a key enabler of sustainability, its specific influence on SSCP, particularly through the mediating roles of supply chain agility (SCA), supply chain resilience (SCR), and supply chain collaboration (SCC), remains underexplored. Drawing on the dynamic capabilities view (DCV), this study examines how DT impacts SSCP by conceptualizing SCA, SCR, and SCC as mediating capabilities. A quantitative research approach was employed, utilizing survey data collected from 214 managers in Saudi Arabia's manufacturing sector. Data were analyzed using partial least squares structural equation modeling (PLS‐SEM). The results indicate that DT positively affects SCA, SCC, and SCR, and also exerts a direct positive effect on SSCP. Furthermore, these three capabilities partially mediate the relationship between DT and SSCP, suggesting that digital technologies enhance sustainability outcomes by strengthening internal supply chain capabilities. This study extends the DCV framework by validating the mediating roles of SCA, SCR, and SCC in the relationship between DT and SSCP. From a practical standpoint, the findings offer actionable insights for manufacturing firms aiming to improve sustainability performance by leveraging digital tools, fostering collaboration, and enhancing agility and resilience.