Smart decision engine for performance optimization in enterprise management

Effective enterprise management is essential for maintaining competitiveness and facilitating long-term growth in the current dynamic business landscape. Decision-making in this area is complex, requiring managers to assess various, frequently conflicting, criteria amid uncertainty and subjectivity. Traditional multi-criteria decision-making (MCDM) methods are valuable but limited by their dependence on crisp data and their inability to effectively handle ambiguity, hesitation, and imprecise expert judgements, often leading to suboptimal or biassed outcomes. This study presents a smart decision engine for enterprise performance optimization, utilising complex picture fuzzy sets (CPSs) to represent uncertainty with improved flexibility and precision effectively. The framework utilizes the integrated determination of objective criteria weights (IDOCRIW) method for establishing objective criteria weights, incorporates the ranking comparison (RANCOM) approach to integrate expert-driven subjective preferences, and employs the measurement alternatives and ranking according to compromise solution (MARCOS) method for a thorough evaluation and ranking of alternative enterprise strategies. The proposed model's robustness and reliability are validated through sensitivity and comparative analyses, demonstrating its superiority over traditional techniques. The findings indicate that the Smart Decision Engine enhances the accuracy and consistency of strategic evaluations while serving as a scalable and adaptable decision-support tool for managers aiming to optimize enterprise performance in uncertain and dynamic environments.