A Decision Support Framework for Business Development Performance Evaluation Using Analytic Hierarchy Process

Business development plays a key role in organizational growth, yet its evaluation remains difficult due to limited data integration and delayed outcomes. This study introduces a structured, computational model that applies the Analytic Hierarchy Process using Python to evaluate business development across six defined process phases. Each phase contains three performance indicators designed to reflect both strategic alignment and operational clarity. Scenario-based simulations represent growth, efficiency, and balanced orientations to examine how strategy influences outcomes. A case drawn from published literature illustrates the model’s structure and application. Evaluation outputs enable consistent scoring and structured comparison while supporting alignment between decision intent and process performance. Weight sensitivity testing confirms internal consistency and adaptability across decision-making contexts. The framework provides a decision support tool that integrates performance metrics with multi-phase evaluation logic to enhance transparency, traceability, and strategic learning.