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The Role of Artificial Intelligence in Supply Chain Agility: A Perspective of Humanitarian Supply Chain
Abstract
The present study aims to establish a link between digital and humanitarian supply chain management. This study has focused on using artificial intelligence-big data analytical capabilities and information alignment to develop and maintain supply chain collaboration to achieve supply chain agility in a dynamic environment like a disaster. The targeted population is humanitarian organizations in Pakistan. Simple random sampling method, data was collected from 242 respondents using an online questionnaire. The Partial Least Square – Structural Equation Modelling technique has been used for analysis. Resource Based Theory and Contingency Theory in this study have provided foundations to develop and test the relationships among information alignment, supply chain agility, artificial intelligence – big data analytical capabilities, and supply chain collaboration in disaster management. Findings showed the use of artificial intelligence – big data analytical capabilities are beneficial for information alignment and supply chain agility.
... AI significantly improves supply chain performance across key processes such as demand forecasting, inventory management, logistics planning, and automation [5,10,12,20,22,23,28,[33][34][35][36]41,42,[44][45][46][47][48][49][50][51][52][53]57,58,[67][68][69]. ...
... The critical role of artificial intelligence-particularly through big data analytics capabilities-in enhancing supply chain agility by improving information alignment and collaboration has been highlighted, emphasizing its importance in achieving responsiveness and adaptability in dynamic and uncertain environments [33]. ...
... AI enables sustainability in supply chains by reducing waste, controlling emissions, and supporting ethical sourcing across environmental, social, and governance (ESG) dimensions [14,26,[32][33][34]37,38,47,54,55,[58][59][60][61][62][63][64][65]70,71]. ...
This systematic literature review investigates the recent applications of artificial intelligence (AI) in supply chain management (SCM), particularly in the domains of resilience, process optimization, sustainability, and implementation challenges. The study is motivated by gaps identified in previous reviews, which often exclude literature published after 2020 and lack an integrated analysis of AI’s contributions across multiple supply chain phases. The review aims to provide an updated synthesis of AI technologies—such as machine learning, deep learning, and generative AI—and their practical implementation between 2021 and 2024. Following the PRISMA framework, a rigorous methodology was applied using the Scopus database, complemented by bibliometric and content analyses. A total of 66 studies were selected based on predefined inclusion criteria and evaluated for methodological quality and thematic relevance. The findings reveal a diverse classification of AI applications across strategic and operational SCM phases and highlight emerging techniques like explainable AI, neurosymbolic systems, and federated learning. The review also identifies persistent barriers such as data governance, ethical concerns, and scalability. Future research should focus on hybrid AI–human collaboration, transparency through explainable models, and integration with technologies such as IoT and blockchain. This review contributes to the literature by offering a structured synthesis of AI’s transformative impact on SCM and by outlining key research directions to guide future investigations and managerial practice.
... The study concludes that blockchain-based food supply chains, value stream mapping, sustainable supply chain domain, and online ordering systems via mobile applications served as information and communication technology platforms during covid 19. Pereira and Shafique (2024) established a link between digital and humanitarian supply chain management in Pakistan. The population of the study comprised of humanitarian organizations. ...
... It also presented an analytical framework that shows the cause-and-effect relationships of all active actors in this system. Pereira and Shafique (2024) examined the role of artificial intelligence in supply chain agility: a perspective of the humanitarian supply chain in Pakistan. The targeted population is humanitarian organizations in Pakistan. ...
... The findings agree with the extant literature on the previous work of Elkady and Sedky (2023) which emphasized the potential of artificial intelligence and machine learning to improve decision-making processes, optimize resource allocation, increase supply chain visibility, and promote sustainable practices. The result is also supported by the work of Pereira and Shafique (2024) which explained the potential of artificial intelligence and big data analytical capabilities to enhance supply chain information and supply chain agility. ...
The study focuses on evaluating the influence of the adoption of technological advancement on supply chain management in Selected Nigerian Brewery Plc, SouthEast Zone Nigeria. The research employed a survey design and questionnaire as instruments for data collection. The total population of the study comprised 2,100 staff of the organization. Taro Yamane method was used to estimate the sample size which produced a result of 336. A proportionate allocation formula was applied in the distribution of the survey in the following states: River State, Bayelsa, Akwa Ibom, Delta, and Edo State, Nigeria. Out of 336 copies of a questionnaire sent to the participants, only 321 were returned and utilized for the study while the remaining 15 copies were not utilized for this study. The study hypotheses were statistically tested and analyzed using Pearson correlation methods at a 5% significance level. The findings of research hypothesis one indicates that artificial intelligence (AI) adoption positively contributes to supply chain visibility (SCV) when the p-value (.000) is less than a 5 % level of significance (p < 0.05). The researcher recommends that businesses should create a demand forecast based on using historic sales to meet customer demands. Management of the Nigerian Brewery Plc should continue to invest in advanced technologies such as AI and IoT to further enhance supply chain operations. This will help maintain a competitive edge in the market by improving efficiency and reducing costs. This study adds to the body of knowledge by presenting data on the beneficial effects of technology improvements on supply chain management in the Nigerian brewery industry. The study would guide the Nigerian government to implement e-government, and e-health services to citizens.
... Key technologies for logistics 4.0 and digital supply chain include the Internet of Things, Blockchain, digitalization, artificial intelligence, machine learning, cyberphysical, augmented reality, virtual reality, robotics, additive manufacturing, and cloud computing [8][9][10][11][12]. However, studies on logistics 4.0 and digital supply chains have often focused on individual technologies such as artificial intelligence [13][14][15], blockchain [16], Internet of Things [17][18][19], big data [15,[20][21][22], supply chain analytics [23], and wearable technologies [24]. The literature found inconsistencies in the use of information technology in digital supply chain and logistics 4.0. ...
... The study explored the relationship among logistics 4.0, digital supply chain, artificial intelligence, Internet of Things, digitalization, big data, circular economy, green logistics, and green supply chain management. The focus was only on four key technologies: Artificial intelligence [13][14][15], Internet of Things [17][18][19], big data [15,[20][21][22], and digitalization [51] These technologies are common in logistics 4.0 and digital supply chains, and most literature focuses on their economic, environmental, and social benefits. ...
... Novel studies aim to examine the concept of agility in the humanitarian sector, whereas former studies were focused on understanding agility in corporate settings. Pereira and Shafique (2024), for example, illustrate how emerging technologies like AI and real-time data analytics are transforming the agility of the humanitarian supply chain. Similarly, Abou-AL-Ross and Shatali (2022) discuss the potential of workforce agility when operating in unpredictable circumstances. ...
Organizations are gradually relying on Information Technology (IT) to assist them in addressing unexpected challenges they face in their daily operations. The reliance is particularly essential in humanitarian settings where agility and adaptability are crucial. To examine the factors influencing IT agility in the humanitarian context, Interpretive Structural Modeling (ISM) along with MICAMAC analysis were applied. After identifying interconnections among the factors, they were classified according to their driving and dependence power. The findings reveal a considerable degree of interdependence and the impact that one factor may have on all the others. This study emphasizes the importance of continuous monitoring and flexible strategies to maintain operational efficacy. While the insights gained from the research are valuable, particularly due to the range of perspectives, this diversity along with a small sample size of 16 IT professionals, could also be considered a limitation. Future studies can explore the dynamics across different sectors or with a larger sample size to validate the results in a broader context. This study contributes to the understanding of how IT agility can be managed and optimized in humanitarian contexts, providing valuable insights for practitioners and researchers.
... The integration of Artificial Intelligence with strategic human resource management enhances agility and resilience, demonstrating a direct correlation between Artificial Intelligence adoption and agility measures (Yamin, 2024). (Pereira 2024) underscores the significance of Artificial Intelligence in humanitarian supply chains, enhancing agility via improved demand forecasting and resource allocation. Artificial Intelligence driven forecasting models enhance inventory management and refine logistics, crucial for a responsive supply chain. ...
Background: Artificial Intelligence is an implementation aimed at improving efficiency from various angles, particularly in terms of business competitiveness. Artificial Intelligence not only enhances operational efficiency and enables demand forecasting, but from other perspectives, it also has a positive impact on communication, organizational readiness, sustainability, collaboration, finance, transparency, and product quality.
Methodology: This scoping review evaluates the effects of implementing Artificial Intelligence in supply chain management, providing insights into its viability and advantages. This study utilises two databases, Web of Science (WoS) and Scopus, to ascertain the characteristics of the published scientific literature on this subject and the growing topics associated with Artificial Intelligence in supply chain management.
Result: Utilising Arksey and O'Malley's technique, the findings underscore the impact of Artificial Intelligence on improving organisational efficacy. This emphasises impact as a dominant subject. The study suggests that most of the studies aim to comprehend the impact of Artificial Intelligence on supply chain management.
Conclusion: This study provides a comprehensive overview of how Artificial Intelligence is shaping the present and future of supply chain management. It offers essential insights for researchers, practitioners, and decision-makers engaged in this dynamic and rapidly evolving field.
... AI can enhance supply chain transparency and efficiency, ensuring that resources are used optimally from production to delivery. This includes demand forecasting, inventory management, and logistics optimization (Jackson et al., 2024;Nozari, 2024;Pereira & Shafique, 2024;Sadeghi-R et al., 2024). The role of AI goes beyond the allocation of resources (Target 12.2) since it allows the analysis of consumption patterns and calibrates production processes in realtime, serving as a bulwark against overproduction and the uncontrolled generation of waste, which would make it possible to adapt production to actual demand and move toward a circular production model (Bag et al., 2021;Tsui et al., 2022), The predictive abilities of machine learning algorithms enable industries to predict resource requirements more precisely and ensure the efficient utilization of materials (Akkem et al., 2023;Jhajharia et al., 2023). ...
Aim/purpose-This study aims to identify the role of Artificial Intelligence (AI) in achieving the Sustainable Development Goals (SDGs), with specific reference to their targets, and to present good practices in this regard. Design/methodology/approach-This study adopts qualitative research based on an integrative literature review encompassing five stages: problem identification, literature search, data evaluation, data analysis, and presentation of findings. Findings-This study presents a framework for leveraging AI to achieve SDGs. It details the role of AI in achieving each SDG, identifies the best practices for using AI to achieve these goals, and recommends the main steps for systematically deploying AI to achieve SDGs. Research implications/limitations-The presented findings reflect the authors' perspective on the role of AI in achieving SDGs based on an integrative literature review, which may have overlooked some literature on AI's impact on individual SDGs or lacked published evidence on such interlinkages. Originality/value/contribution-This study contributes to the existing body of knowledge by providing a comprehensive framework for leveraging AI to achieve the SDGs. It systematically identifies and details the role of AI in advancing each SDG, highlights best practices for deploying AI effectively, and recommends steps for integrating AI into SDG initiatives. The study's value lies in its ability to guide policymakers, researchers, and practitioners in harnessing AI's potential to address critical global challenges while highlighting the need for careful consideration of potential limitations and gaps in the existing literature.
... In the contemporary era, Internet of Things capabilities (IoTCs) enhance the green supply chain performance [8]. The literature has focused on artificial intelligence-big data analytical capabilities (AI-BDACs) from the perspective of supply chain agility [9]. Additionally, big data analytics (BDA) has sharpened the supply chain performance and overall organizational performance [10,11]. ...
The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.
... Much research (Yamin & Murwaningsari, 2023;Ponomarenko et al., 2024;Wang et al., 2024;Ivashchenko & Polischuk, 2018;Chytilová et al., 2024;Anton, 2024;Pereira & Shafique, 2024;Musyaffi, 2024;Kliestik et al., 2023;Aliane et al., 2023;Krajčík et al., 2023;Chang & Ku, 2023;Červinka, 2023;Agboola et al., 2023;Odei Addo & Keelson, 2023;Lytvyn et al., 2024) was provided on the level of enterprises and organisations. For example, the article (Yarovenko et al., 2021) aims to develop a rapid methodology for assessing the risk of information and knowledge loss management. ...
The rapid development of digital technologies and their active implementation in all spheres of the economy, on the one hand, contribute to increased control over the activities of economic entities, and on the other hand, create new challenges associated with the dynamic development of cybercrime. The purpose of the article is to analyze the global trends in cybercrime in 2016-2023 (by calculating national levels of cybercrime) and to model the mechanisms of combating it in order to form a global, open and safe cyberspace, protect the population from cyber threats and cyber fraud, increase the effectiveness of financial monitoring procedures and control over transactions in cyberspace. The article presents the dominant directions, evolutionary, temporal and spatial patterns of the development of crime in cyberspace, clustering of the countries, and identification of leaders and outsiders in the field of cybercrime (through visualization density maps based on the construction of self-organized Kohonen maps). The results of the calculations confirm a significant increase in the level of cybercrime in the world since 2016 (in particular, due to the COVID-19 pandemic and active digital transformation). A comparative analysis of the indicator across countries made it possible to distinguish those with the highest rates of cybercrime (Slovenia, Iceland, Moldova, Georgia) and those with a significantly higher level of cyber security (Greece, Belgium, France, Germany).
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