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.

Interval analysis, grey decision-making, supply chain performance, cost optimization

Moore, R.E. Method and Application of Interval Analysis; Society for Industrial and Applied Math: Philadelphia, PA, USA, 1979; pp. 74–79.

Ishibuchi, H.; Tanaka, H. Multiobjective programming in optimization of the interval objective function. Eur. J. Oper. Res. 1990, 48, 219–225.

Li, G.D.; Yamaguchi, D.; Nagai, M. A grey-based decision-making approach to the supplier selection problem. Math. Comput. Model. 2007, 46, 573–581.

Hu, B.Q.; Wang, S. A novel approach in uncertain programming part I: New arithmetic and order relation for interval numbers. J. Ind. Manag. Optim. 2006, 2, 351.

Xie, N.M.; Liu, S.F. Novel methods on comparing grey numbers. Appl. Math. Model. 2010, 34, 415–423.

Estampe, D. Supply Chain Performance and evaluation models. London: John Wiley & Sons, Inc.

Fernando, J. Cost of Goods Sold (COGS). Investopedia. Available online.

Foster, S. T. Managing Quality: Integrating the Supply Chain. New Jersey: Pearson.

Gilaninia, S. et al. The impact of information technology application on supply chain performance. Interdisciplinary Journal of Contemporary Research in Business.

GEODIS. Key performance indicators used to monitor supply chain in 2017.

Naveen Salunke. Cost Optimization in Supply Chain Management Leveraging Vendor Development and Sourcing Strategies. Journal of Business and Management Studies, 6(5), 225-237.

Goldsby, T. J., Iyengar, D. and Rao, R. Definitive Guide to Transportation: Principles, Strategies and Decisions for the Effective Flow of Goods and Services. New Jersey: Pearson.

Gustafsson, E., Jonsson, P. and Holmström, J. Digital product fitting in retail supply chains: maturity levels and potential outcomes. Supply Chain Management.