This study explores the dynamic relationship between talent management practices, employee retention, and organizational performance within the Information Technology (IT) sector. In a rapidly evolving and highly competitive environment, retaining top talent has become a critical determinant of sustained business success. The research investigates how strategic talent acquisition, development, and retention initiatives influence key performance indicators such as productivity, innovation, and employee engagement. Utilizing a mixed-methods approach, including surveys and interviews with HR professionals and IT employees, the study identifies effective talent management frameworks that correlate strongly with reduced turnover rates and improved organizational outcomes. The findings emphasize that organizations investing in employee development, recognition, and career progression are more likely to retain skilled personnel and gain a competitive edge in the digital economy.

The modern supply chain environment is defined by complexity, variability, and uncertainty. Traditional optimization techniques, rooted in precise numerical data and deterministic models, often fail to accommodate uncertainty inherent in real-world decision problems such as supplier selection, performance evaluation, and cost optimization. This study integrates interval analysis and grey system theory to develop a comprehensive decision-making framework tailored for supply chain performance management and optimization. Interval analysis provides a mathematical basis for handling uncertain parameters as intervals rather than fixed point values (Moore, 1979), while grey systems theory offers mechanisms for decision-making under partial information (Xie & Liu, 2010). The framework synthesizes these theoretical constructs, contextualizing them within supply chain key performance indicators (KPIs), cost of goods sold (COGS), information technology’s role in performance improvement, and vendor development strategies. The methodology elaborates qualitative and quantitative facets of interval and grey approaches, extending multiobjective programming techniques to supply chain contexts (Ishibuchi & Tanaka, 1990). Detailed theoretical elaboration and descriptive analyses outline how uncertainty and incomplete information can be rigorously incorporated into performance evaluation and strategic decisions. Findings suggest that adopting interval and grey-based models can significantly enhance robustness in supplier selection, performance evaluation, and cost optimization strategies, providing richer insights compared to classical deterministic methods. Limitations and future research directions illustrate pathways for empirical validation and computational implementation, especially in the context of digital supply chains. Implications extend across academic inquiry and managerial practice, emphasizing the need to embrace uncertainty not as a hindrance but as an integral dimension of supply chain modeling.