Dr. Tesfay Alemayeh Dagnew

https://doi.org/10.64823/ijcsa.2601006

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.

Artificial intelligence (AI) has emerged as a transformative technology for improving supply chain performance; however, empirical evidence on its adoption and impact in public health supply chains within developing countries remains limited. This study examined the determinants of AI adoption and its influence on public health supply chain performance at the Ethiopian Pharmaceutical Supply Service. A quantitative cross-sectional research design was employed using structured questionnaire data collected from 354 supply chain, logistics, and information systems personnel. Data were analyzed using descriptive statistics, reliability analysis, Pearson correlation, and hierarchical multiple regression. The measurement instrument demonstrated high internal consistency (Cronbach's α = 0.899). The findings revealed that AI adoption was positively associated with enhanced demand forecasting accuracy, inventory management, distribution efficiency, responsiveness to demand fluctuations, and reductions in stock-outs and wastage. Although respondents generally recognized the strategic and operational value of AI, knowledge and competency levels varied considerably across staff. Regression analysis showed that general AI competency (β = 0.502, p < 0.001) and AI-assisted decision-making capability (β = 0.396, p < 0.001) were the strongest predictors of AI adoption, jointly explaining 69.8% of the variance (R² = 0.698). Ethical considerations were not statistically significant after knowledge-based variables were included, indicating that organizational capability and technical competence exerted a greater influence on AI adoption than governance factors. The study concludes that successful AI adoption in resource-constrained public health supply chains depends primarily on strengthening organizational capabilities and workforce competencies, providing practical evidence to support digital transformation initiatives in developing-country healthcare systems.