Authors: Nitish Kumar Bharadwaj
Abstract: Decision-making in contemporary organizations is increasingly complex due to technological advances, volatile markets, and uncertainty in social, political, and economic environments. Relying solely on intuition or experience often leads to inefficient allocation of scarce resources and costly managerial errors. Operations Research (OR) provides a scientific and quantitative framework that supports rational decision-making through modeling, analysis, and optimization. This paper reviews the historical evolution of OR, clarifies conceptual foundations, and explains the processes through which OR transforms real-world problems into structured decision models. Different classes of models, deterministic, probabilistic, static, dynamic, descriptive, and normative are examined with reference to their applicability and limitations. The paper further highlights implementation challenges and discusses how computing advances have broadened the scope of OR in domains such as supply chains, healthcare, energy, and public policy. The study contributes by presenting a synthesized framework that links classical OR principles with contemporary decision environments, emphasizing how quantitative modeling can improve managerial effectiveness and support evidence-based decisions. Recommendations for future research and practice are also discussed.
