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Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supply chain data analytics, firmsize, age and annual sales to predict supply chain agility based on knowledge-based theory.

Survey data (n = 215) were obtained from firms operating in the United Arab Emirates. Consequently, fuzzy sets qualitative comparative analysis (fsQCA) technique was applied to the data to establish causal recipes that are necessary and sufficient to achieve high scores of supply chain agility.

Results from fsQCA support the major tenets of complexity theory that several configural combinations (i.e. supply chain data analytics, firm size, firm age and annual sales) are sufficient and necessary conditions for achieving higher scores of supply chain agility.

This study is first of its kind in understanding the association between supply chain data analytics and agility with fsQCA technique. This research also offers a headway for supply chain managers in identifying configural combinations of antecedents manifesting high scores for supply chain agility. Implications for theory and practice are illustrated as well as future research course.

The abrupt outbreak of coronavirus disease (COVID-19) hit every nation in 2020–2021, causing a worldwide pandemic. The worldwide COVID-19 epidemic, described as a “black swan”, has severely disrupted manufacturing firms' supply chain. The purpose of this study is to investigate how supply chain data analytics enable the effective deployment of agility, adaptability and alignment (3As) strategies, resulting in improving post-COVID disruption performance. It also analyses the indirect effect of supply chain data analytics on disruption performance through the 3As supply chain strategies.

The hypothesis and theoretical framework were tested using a questionnaire survey. The authors employed structural equation modelling through the SMART PLS version 3.2.7 to analyse data from 163 textile firms located in Pakistan.

The results revealed that the supply chain data analytics contributed positively and significantly to the agility and adaptability, while all 3As supply chain strategies impacted the PPERF substantially. Further, the connection between supply chain data analytics (SCDA) and disruption performance has substantially been influenced through 3As supply chain strategies.

The results imply that in the event of low likelihood, high effect disruptions, managers and decision-makers should focus their efforts on integrating data analytics capabilities with 3As supply chain policies to ensure long-term company success.

This research sheds fresh light on the importance of data analytics in effectively implementing 3As strategies for sustaining company performance amid COVID-19 disruptions.

This study aims to investigate digitalization as a performance driver in supply chains, especially the role of data analytics in the digitalization of procurement. The study investigates how digital procurement capabilities are linked to data analytics capabilities and supply chain operational performance and how this links to business success.

Using operational and dynamic capabilities as foundations for data analytics capabilities, this paper studied the digital procurement capabilities and proposed the conceptual model and hypotheses for empirical testing. The collected industry survey data and structural equation method are then applied to test the hypotheses.

The study confirms positive and significant relationships among digital procurement capabilities, data analytics capabilities and supply chain performance. Digital procurement capabilities mediate the positive relationship between external data analytics capabilities and supply chain performance.

This study has some limitations that should be addressed. The empirical study was based on survey data from a questionnaire that was probably challenging for some respondent companies with low levels of digital procurement and data analytics. Also, it is necessary to adopt secondary data to measure business performance in future studies which reduces the effect of subjective bias.

From the managerial point of view, the findings highlight the importance of gaining knowledge from gathered data and digitalized processes. Managers must focus on data utilization capabilities to improve the operational performance expected from the digitalization of supply chain activities. In addition, managers need to consider exploiting of data through new creative approaches as part of standardized operations.

The present study contributes to existing knowledge by investigating the mediating role of data analytics capabilities between the digitalization of procurement and supply chain performance. The findings support a positive relationship between the data analytics capabilities and supply chain performance in digital upstream supply chain procurement processes. The present study also clarifies the impact and role of data analytics capabilities in digital supply chain development and success.

Despite the escalating significance and intricate nature of supply chains, there has been limited scholarly attention devoted to exploring the cognitive processes that underlie supply chain management. Drawing on cognitive-behavioral theory, the authors propose a moderated-mediation model to investigate how paradoxical leadership impacts manufacturing supply chain resilience.

By conducting a two-wave study encompassing 164 supply chain managers from Chinese manufacturing firms, the authors employ partial least squares structural equation modeling (PLS-SEM) to empirically examine and validate the proposed hypotheses.

The findings indicate that managers' paradoxical cognition significantly affects supply chain resilience, with supply chain ambidexterity acting as a mediating mechanism. Surprisingly, the study findings suggest that big data analytics negatively moderate the effect of paradoxical cognition on supply chain ambidexterity and supply chain resilience, while positively moderating the effect of supply chain ambidexterity on supply chain resilience.

These findings shed light on the importance of considering cognitive factors and the potential role of big data analytics in enhancing manufacturing supply chain resilience, which enriches the study of behavioral operations.

The results offer managerial guidance for leaders to use paradoxical cognition frames and big data analytics properly, offering theoretical insight for future research in manufacturing supply chain resilience.

This is the first empirical research examining the impact of paradoxical leadership on supply chain resilience by considering the role of big data analytics and supply chain ambidexterity.

The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply chain in the “new normal” environment.

A systematic literature review was conducted by extracting research articles related to analytics in the healthcare supply chain from Scopus. The author used a hybrid review approach that combines bibliometric analysis with a theories, contexts, characteristics, and methodology (TCCM) framework-based review to identify various themes of analytics in the healthcare supply chain.

The hybrid review strategy yielded results that focus on prevalent theories, contexts, characteristics, and methodologies in the field of healthcare supply chain analytics. Future research should explore the resulting antecedents, decision-making processes and outcomes (ADO) framework, which integrates technological, economic, and societal concerns and outcomes. Future research agendas could also seek to apply theoretical perspectives in the field of analytics in the healthcare supply chain.

The result of a review of selected studies adds to the current body of work and contributes to the growth of research in the field of analytics in the healthcare supply chain. It also provides new directions to healthcare supply chain managers and academic scholars.

The study objective was to understand what components of organizational culture and capability combined with analytic skillsets are needed to allow organizations to exploit real-time analytic technologies to create supply chain performance improvements.

The authors relied on information processing theory to support a hypothesized model, which is empirically tested using an ordinary least squares equation model, and survey data from a sample of 208 supply chain executives across multiple industries.

The authors found strong support for the concept that real-time analytics will require specialized analytical skills for the managers who use them in their daily work, as well as an analytics-focused organizational culture that promotes data visibility and fact-based decision-making.

Based on the study model, the authors found that a cultural bias to embrace analytics and a strong background in statistical fluency can produce decision-makers who can make sense of a sea of data, and derive significant supply chain performance improvements.

The research was initiated through five workshops and presentations with supply chain executives leading real-time analytics initiatives within their organizations, which were then mapped onto survey items and tested. The authors complement our findings with direct observations from managers that lend unique insights into the field.

Building on the information processing perspective, the authors propose that both internal and external supply chain risk management (SCRM) practices play essential roles in mediating supply chain disruption orientation (SCDO) to exercise an influence on resilience. The authors also put forward analytics capability as an important moderator in the above-mediated relationship.

The authors collected 170 match-paired questionnaires from Chinese firms to test our model. The authors further interviewed some managers to supplement key quantitative results.

First, SCDO positively affects resilience via internal and external SCRM practices. Second, the mediating effects of internal and external SCRM practices are stronger when analytics capability is higher. Third, analytics capability positively moderates the positive effect of SCDO on SCRM practices. Meanwhile, it does not moderate the positive effect of SCRM practices on resilience.

Our study contributes to SCRM-related and IT-related literature by considering the content, mediating mechanisms (i.e. internal and external SCRM practices) and boundary conditions (i.e. data analytic capability) of SCDO in shaping resilience in the digital supply chain.

Our study helps remind managers that firms build disruption orientation, develop different SCRM practices and leverage analytics capability to improve resilience amid unexpected and unplanned disruptions.

Our study sheds light on the roles of both internal and external SCRM practices. Furthermore, this research helps explain how SCDO motivates resilience through SCRM practices, particularly for those firms that have higher analytics capability.

This study investigates the impact of big data analytics capabilities on green supply chain performance. Moreover, it assesses the mediating effect of the green innovation and moderating effect of technological intensity.

This study is based on primary data that were collected from the food and beverages manufacturing sector operating in Jordan. A total of 420 samples were used for the final data analysis. Data analysis was performed via structural equation modeling (SEM) using SmartPLS 3.3.9.

The results of the data analysis supported a positive relationship between big data analytics capabilities and the green supply chain performance as well as a mediating effect of green innovation. It was confirmed that technological intensity moderated the relationship of green innovation on green supply chain performance.

The study faced many limitations such as the method of collecting primary data, which relied on a questionnaire only and the use of cross-sectional data, as well as studying one context and in one country.

The findings can guide managers and policymakers in the Jordanian food and beverage manufacturing sector on how to manage organizational capabilities related to big data analytics to enhance green supply chain performance and improve green innovation in these firms.

This study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, green innovation, technological intensity and green supply chain performance. This study offers new theoretical and managerial contributions that add value to the supply chain management and innovation literature by testing the moderated mediation model of these constructs in the food and beverages manufacturing sector in Jordan.

Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM.

Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique.

Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective.

These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM.

The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

The paper draws on the relational view of resource-based theory to propose a theoretical model. The data were collected through survey of 145 service firms.

The results of this study found that the Big Data analytics has a positive and significant relationship with a firm’s ability to manage data security and a positive impact on service supply chain innovation capabilities and service supply chain performance. This study also found that most service firms participating in this study used Big Data analytics to execute existing algorithms faster with larger data sets.

A main recommendation of this study is that service firms empower a chief data officer to establish the data needed and design the governance of data in the company to eliminate any security issues. Data security was a concern if a firm did not have ample data governance and protection as the information was shared among members of service supply chain networks.

Big Data analytics are a useful technology tool to forecast market preference based on open source, structured and unstructured data.