Skip to content

PDSA Cycle

Definition

The PDSA model stands for Plan-Do-Study-Act, a foundational improvement cycle originating from the Institute for Healthcare Improvement. It guides rapid improvement by testing small changes through empowered employees. The cycle begins by answering three critical questions: What are we trying to accomplish? How will we know that a change is an improvement? What changes can we make that will result in improvement? Each iteration moves through Plan (identify opportunities and design improvements), Do (carry out the plan), Study (compare actual results to predictions and summarize learning), and Act (identify required changes or plan to spread improvements).

In the Book

Slides 40-41 define PDSA as the "Model for Improvement" and the foundation of A3 Thinking and continuous improvement. The workbook outlines the four steps:

  • PLAN: Identify the opportunity and plan the improvements, starting from the three guiding questions.
  • DO: Carry out the plan through small-scale testing.
  • STUDY: Compare actual results to predicted results and summarize what has been learned, distinguishing success from failure.
  • ACT: Identify any changes required and develop a plan to spread successful improvements across the organization.

The workbook emphasizes that PDSA uses "rapid improvement cycles using small tests of change driven by empowered employees" and that the model is iterative—learning from each cycle informs the next. Kaizen events (slides 73-74) apply PDSA across multiple days, testing proposed solutions before full implementation.

Why It Matters

PDSA decouples improvement from big-bang implementation. By treating each change as a hypothesis to test at small scale, it makes failure cheap and learning fast. The cycle embeds humility: prediction must face reality, assumptions get tested, and surprises trigger adaptation instead of sunk-cost defensiveness. This is exportable to any domain where uncertainty is high and the cost of large, wrong decisions is steep—software development, product design, organizational change, or experimental science all benefit from a structured prediction-vs-reality feedback loop.