Organizations operating many comparable sites, including retail chains, bank branch networks, diagnostic laboratory systems, hospital groups, distribution centers, and regulated utility networks, face a recurring problem: separating performance differences that reflect managerial effort from those reflecting operating environment, scale, asset condition, and chance. Regression-based benchmarking is the dominant quantitative response. This review synthesizes methodological developments from the foundational frontier literature of the late 1970s through the state of practice in 2023, and integrates that canon with applied work on performance analytics in distributed operating environments. Seven method families are examined alongside five areas of recent methodological advance, and the synthesis is organized into a framework arranging benchmarking into five sequentially dependent layers with a feedback path from decision back to measurement, from which six propositions are derived. Three findings emerge. Benchmark validity is bounded above by the credibility of the comparison set, so returns to estimator sophistication diminish once common support fails. The two-stage practice of regressing efficiency scores on environmental variables persists despite sustained evidence that it yields invalid inference. And methods designed for measurement are routinely repurposed for target-setting, a role in which they degrade once measured units can anticipate the model. Theoretical, managerial, and policy implications are set out, the limitations of a narrative synthesis are stated, and an agenda is proposed centred on decision-relevant uncertainty, strategic robustness, and empirical testing of the framework.