Aug 2026· International journal of engineering, management and sciences· 0 citations· 39 references
TL;DR
The results indicate that the technology deployment strategy, governance, compliance, and financial considerations are the most influential independent factors, serving as foundational drivers of AI adoption.
Abstract
Artificial Intelligence (AI) has emerged as a transformative enabler for supply chain optimization, offering advanced capabilities in predictive analytics, real-time decision-making, and operational efficiency. However, successful AI integration in supply chain management (SCM) is influenced by multiple interdependent technological, organizational, and governance-related factors. This study aims to identify and structure the key factors affecting AI-driven supply chain optimization in manufacturing industries. A mixed-methods approach was employed. In the qualitative phase, semi-structured interviews with domain experts were conducted to identify critical factors in AI adoption. These factors were then analyzed using Interpretive Structural Modeling (ISM) to develop a hierarchical model of relationships. Subsequently, MICMAC analysis was applied to classify factors based on their driving and dependence power. The results indicate that the technology deployment strategy, governance, compliance, and financial considerations are the most influential independent factors, serving as foundational drivers of AI adoption. Data and system requirements emerged as linkage factors with high driving and dependence power, while operational applications such as demand forecasting and automation were identified as dependent factors. The findings highlight the importance of structured AI strategies and robust data governance frameworks in achieving effective AI-driven supply chain transformation, providing valuable insights for practitioners and policymakers.
This study examines the role of Artificial Intelligence (AI) in enhancing supply chain project management and operational performance in a dynamic business environment. As supply chains become increasingly complex, data-intensive, and disruption-prone, organizations are adopting AI-driven tools to improve forecasting accuracy, optimize inventory, streamline logistics, and strengthen decision-making. The purpose of this research is to assess the level of AI adoption, identify key application areas, and examine the relationship between AI familiarity and AI adoption while considering the broader roles of organizational readiness and governance mechanisms. A quantitative research design was employed using a structured questionnaire administered to 42 respondents, including supply chain professionals, project managers, data/AI analysts, students, and other business or technology-related participants. Data were analyzed using descriptive statistics, correlation analysis, and regression techniques. The findings indicate that approximately 57% of respondents reported current AI adoption within their organizations, while the mean AI familiarity score was 3.6 on a five-point scale, reflecting moderate awareness. Correlation analysis revealed a positive relationship between AI familiarity and AI adoption (r = 0.61), suggesting that increased knowledge supports adoption behavior. The results also highlight the perceived importance of AI training, organizational preparedness, and governance frameworks in maximizing implementation benefits. This study contributes to business analytics, operations management, and decision sciences by providing empirical insight into AI-enabled supply chain transformation. The findings offer practical implications for managers, policymakers, and industry stakeholders seeking to strengthen AI readiness, improve operational efficiency, and promote responsible AI adoption for sustainable supply chain excellence.
Denise Nalini, Dr. S.Barathi, Dr. Rubidhadevi· The Journal of Theoretical A...· 0 citations
Despite growing investments in artificial intelligence (AI), limited understanding exists regarding the organizational capabilities required to integrate AI into sustainable supply chain quality management. This study develops and validates a novel construct, AI-enabled Sustainable Supply Chain Quality Capability (AISSQC), and examines its role in enhancing supply chain resilience (SCR) and sustainable performance (SP). A sequential mixed-method research design was employed. The construct was developed through an integrative literature review, semi-structured interviews with industry experts, a modified Delphi study, and cognitive pretesting. Quantitative validation was subsequently conducted using two independent samples from Canadian manufacturing firms. Exploratory factor analysis established the dimensional structure of the scale, while Partial Least Squares Structural Equation Modeling (PLS-SEM) using a separate sample validated the reflective–formative higher-order construct and tested the proposed structural relationships. The results confirm that AISSQC is a multidimensional organizational capability comprising five complementary dimensions: AI-enabled Quality Intelligence, Sustainable Quality Integration, Collaborative Quality Orchestration, Adaptive Quality Improvement, and Responsible Quality Governance. AI Capability is positively associated with AISSQC, which in turn is associated with higher SCR and SP. SCR further mediates the relationship between AISSQC and SP, demonstrating that AI creates organizational value primarily through the development of complementary organizational capabilities rather than through technology adoption alone. This study introduces and empirically validates AISSQC as a novel higher-order organizational capability that integrates AI, quality management, sustainability, and supply chain management within a unified capability framework. By extending the Resource-Based View and Dynamic Capabilities Theory, the study explains how organizations transform AI resources into resilient and sustainable operational capabilities. The validated measurement scale provides researchers with a robust instrument for future empirical studies while offering managers a practical framework for guiding AI-enabled quality transformation in sustainable supply chains.
Keywords: Artificial Intelligence, AI-Enabled Sustainable Supply Chain Quality Capability, Scale Development, Dynamic Capabilities, Supply Chain Resilience, Sustainable Performance.
Shima Yaghoubi, Shiva Yaghoubi· Radiant Journal of Business...· 0 citations
Construction supply chains (CSCs) are increasingly exposed to material shortages, demand fluctuations, logistics disruptions, and inter-organizational coordination failures. Artificial intelligence (AI) offers new opportunities to improve construction supply chain resilience (CSCR) by strengthening prediction, information processing, and collaborative decision-making. However, the mechanisms through which AI capabilities enhance CSCR remain insufficiently understood. Drawing on organizational information processing theory (OIPT) and dynamic capabilities theory (DCT), this study examines whether AI capabilities affect proactive and reactive CSCR directly or indirectly through three dimensions of supply chain integration (SCI): operational, information, and relational integration. It further compares the relative strengths of these pathways. This research adopts an explanatory sequential mixed-methods design. In the quantitative phase, 353 valid questionnaires from construction professionals in China were analyzed using partial least squares structural equation modeling (PLS-SEM). In the qualitative phase, semi-structured interviews with 15 experts, alongside three real-world cases, were utilized to interpret the quantitative findings and identify contextual boundary conditions. The results demonstrate that AI capabilities have significant positive effects on both proactive CSCR (β = 0.140, p < 0.01) and reactive CSCR (β = 0.116, p < 0.05). Furthermore, AI capabilities significantly promote operational integration (β = 0.299, p < 0.001), information integration (β = 0.361, p < 0.001), and relational integration (β = 0.227, p < 0.001), which in turn enhance both resilience dimensions. Notably, information integration is an important aspect of proactive resilience (β = 0.290, p < 0.001), while operational integration is crucial for reactive resilience (β = 0.274, p < 0.001). The qualitative findings further indicate that environmental uncertainty, technical readiness, and top management support condition the effectiveness of AI-enabled SCI. Theoretically, grounded in OIPT and DCT, this study clarifies the pathways through which AI affects CSCR and the contextual conditions shaping these effects, thereby advancing the analytical framework for AI-driven resilience. Practically, it delivers tiered implementation guidance for construction stakeholders to deploy AI tools for layered integration, thereby specifically enhancing both pre-disruption proactive risk prevention and post-shock reactive recovery capacities.
Qiang Xu, Haitao Chen, Xinyu Yang et al.· Buildings· 0 citations
The growing demand for sustainability in manufacturing and the inefficiencies of traditional methods in the supply chain highlight the importance of finding smarter solutions. Even with the growing availability of digital tools and AI technologies, organizations are yet to fully utilize them to aid sustainability efforts, causing inefficient use of resources, waste, and environmental damage. The chapter examined AI supply chains optimizing for sustainability: evidence from Nigeria manufacturing industry. The chapter utilized a survey research design with a population of 950 supply chain employees in Nigerian Breweries, Lagos State, with an estimated sample size of 281 using the Yamane (1967) formula. Data was collected using a structured questionnaire with a five-point Likert scale. Descriptive statistics and multiple regression analysis were used to analyze the data in SPSS version 27. The findings showed that AI-driven demand forecasting (coefficient = 4.287) and AI-based inventory optimization (coefficient = 3.549) have a positive significant on sustainability, with 56.7% of the variance in sustainability outcomes. The chapter concluded that AI-driven supply chain optimization plays a significant role in minimizing waste, optimizing resources, and meeting market demand. The study recommended that Nigerian Breweries should utilize AI-powered demand forecasting tools for production planning and should implement AI-based inventory optimization systems to ensure that inventory levels are optimized and reducing excess stock.
Dolapo Stephen Akinwumi, A. Salau· Al-Zaytoonah University Jour...· 0 citations
In an economic environment characterized by increasing demand volatility, conventional forecasting methods are revealing their limitations, prompting growing interest in demand sensing. While artificial intelligence (AI) is widely regarded as a catalyst for this transition, the precise mechanisms through which it transforms practices and impacts supply chain performance remain inadequately elucidated in the academic literature. This study aims to address this gap by introducing the "AI-Demand Sensing-Performance" (ADPsc) conceptual model, which delineates the relationships between AI capabilities, the implementation of demand sensing, and the enhancement of supply chain performance. Grounded in a deductive methodology rooted in contingency theory, this research constructs a theoretical framework articulated around three interdependent pillars: (1) real-time, multi-source data integration, (2) processing through AI algorithms, and (3) operational activation, all driven by a continuous learning loop. The ADPsc model highlights the moderating role of AI in the impact of demand sensing on performance. It demonstrates that AI serves not merely as an optimization tool but as the foundation for a continuous adaptive capability, thereby enabling unprecedented responsiveness to demand fluctuations.
Artificial Intelligence (AI) is revolutionizing procurement and supply chain management through the use of predictive analytics, automation, and informed decision making. This research examines the impact of AI-based procurement optimization on strategic performance at Hydrogen Pharmaceutical PLC. A mixed methodological approach was employed, involving questionnaires to staff members in procurement, supply chain, finance, information technology, and management departments.
Out of 60 questionnaires issued, 45 valid responses were analyzed (75% response rate). Data analysis involved descriptive statistics, reliability test, correlation, and multiple regression analyses using SPSS. The findings reveal that AI implementation positively influences procurement efficiency, supplier performance, inventory management, and cost saving. Regression analysis suggests that AI accounts for 63% of the variance in strategic performance (R² = 0.63, p < 0.001).
The research suggests that AI is a strategic competency that positively influences procurement efficiency.
Keywords: Artificial Intelligence Procurement Optimization Strategic Performance Supply Chain Regression Analysis.
Dr. Tesfay Alemayeh Dagnew· International Journal of Com...· 0 citations