Hybrid Fuzzy MCDM for Process-Aware Optimization of Agile Scaling in Industrial Software Projects

Scaling Agile in industrial software projects is a process control problem that must balance governance, scalability, and adaptability while keeping decisions auditable. We present a hybrid fuzzy multi-criteria decision-making (MCDM) framework that combines Fuzzy Analytic Hierarchy Process (FAHP) for uncertainty-aware weighting with a tunable VIKOR–PROMETHEE ranking stage. Weighting and ranking are kept distinct to support traceability and parameter sensitivity. A three-layer hierarchy organizes twenty-two criteria across organizational, project, group, and framework levels. In a single-enterprise validation with two independent expert panels (n = 10 practitioners), the tuned hybrid achieved lower rank error than single-method baselines (mean absolute error, $\mathrm{MAE}$ = 1.03; Spearman $\rho$ = 0.53) using pre-specified thresholds and a transparent $\alpha +\beta$ = 1 control. The procedure is practical for process governance: elicit priorities, derive fuzzy weights, apply the hybrid ranking, and verify stability with sensitivity analysis. The framework operationalizes modeling, optimization, control, and monitoring of scaling decisions, making trade-offs explicit and reproducible in industrial settings.