The development of systems with complex interactions has led to limitations which are reflected in performance including reliability and safety of the systems. Concurrent developments of frameworks to represent and analyze complexity have aided the understanding of complexity in human-machine systems. The methodology and framework presented is proposed to aid the design of experiments to establish causative relationships of complexity attributes with performance and further deployment in industry. The framework leverages three independent measurement paradigms, at the worker level, interaction level, and task level to classify twenty measurable complexity attributes. Their deployment in key performance indicator (KPI) frameworks and procedure writer’s guides are discussed.
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