Throughput performance in logistics systems is determined not by the average capacity of their operational steps but by the interaction of variability, capacity utilization, and constraint dynamics across the full sequence of operations through which work flows. Traditional logistics improvement programs, focused on the optimization of individual process steps, frequently produce disappointing overall results because they fail to address the specific step whose capacity limits overall throughput and because they do not recognize the system-level consequences of variability at nominally non-bottleneck stations. This review advances a conceptual framework for systems-level throughput optimization in logistics contexts, drawing on the theory of constraints, queueing theory and factory physics, business process reengineering, systems dynamics, and discrete-event simulation to organize the analytical and methodological resources that support effective workflow redesign. The framework identifies four strategic levers through which throughput can be improved at the system level: (1) constraint identification and management, which locates and exploits the current bottleneck; (2) variability reduction, which addresses the capacity erosion that variability produces at every station; (3) flow redesign, which re-engineers the sequence and coupling of operations to reduce inventory, accelerate cycle times, and increase responsiveness; and (4) system-level governance, which establishes the measurement, coordination, and continuous-improvement architecture through which the preceding levers are applied consistently over time. The framework is offered as a conceptual synthesis rather than as empirical validation of any specific operational configuration, and is intended to support logistics leaders, operations managers, and consultants working on throughput improvement in warehouse, distribution, fulfillment, and transportation systems.