Manufacturing competitiveness increasingly depends on the ability to translate engineering performance data into operational decisions that directly support throughput, quality, and cost objectives. This study addresses a critical industrial engineering gap: the absence of a structured decision-support framework that connects PID controller auto-tuning strategies to operational Key Performance Indicators. A multi-objective framework integrating relay-feedback simulation and the Analytic Hierarchy Process (AHP) is developed and applied to representative thermal manufacturing processes (air-heater FOPTD and quadruple-tank MIMO systems). Relay-feedback tuning methods (Åström and Schei variants) are evaluated against six manufacturing KPIs—Integrated Absolute Error, settling time, overshoot, total variance, gain margin, and convergence time—and mapped explicitly to throughput, quality cost, OEE, actuator reliability, and process capability outcomes. The Schei ideal relay method ranked first across all three AHP priority scenarios (quality-focused, throughput-focused, and balanced), achieving 76.7% IAE reduction and 43.8% settling time reduction versus manual Ziegler-Nichol’s tuning (Cohen’s d > 3.2 for both). Operational impact quantification yields annual incremental revenue of $204,000 for throughput-sensitive operations, quality cost savings of $348,750/year for pharmaceutical batch processes, and projected maintenance cost reductions of $150,000/year for a 200-loop facility. The proposed IE framework provides practitioners with a transparent, auditable methodology for controller selection aligned with organizational strategic objectives, bridging the traditional divide between control engineering and operations management.