International Journal of Science, Engineering and TechnologyAuthors: Samyo Ranjan Jagdev, Subhashree Sibani Sahu, Achinta Kumar Palit, Sumant Sekhar Mohanty
Abstract: Strategic decision-making has become an increasingly important function in today’s corporate world. The challenge for managers is to evaluate many options where there are unknown factors (e.g., the condition of the market, financial constraints, and competitors). Managers currently rely heavily on experience and qualitative methods for making strategic decisions. Traditional methods do not take full advantage of the vast volume of data generated by the modern enterprise. With the emergence of Artificial Intelligence (AI), organizations can analyze large-scale databases to generate predictive insight for making data-driven decisions. This paper proposes a hybrid AI-based strategic decision support system. The system combines machine learning and Particle Swarm Optimization (PSO) to forecast strategic decisions and optimize strategic decision-making variables. The paper outlines the mathematical modelling; the overall design of the system, algorithms, and experimental evaluation results. Results from the experiments showed that the hybrid AI+PSO framework improved the accuracy of the decision and effectively identified optimal strategies when compared to traditional machine learning approaches
