Looking for a model to run your continuous improvement experiments?

The PDSA cycle is one of the most well known ones that has stood the test of time for decades now. It’s short for Plan-Do-Study-Act, and originates from the quality management and statistical process control (SPC) philosophies of W. Edwards Deming (check out, for example, “The essential Deming“. Please note: As an Amazon Associate, I earn from qualifying purchases.)

Deming proposed a “System of Profound Knowledge,” which consists of four interrelated components:

  • Appreciation for a System: it’s critical to understand the interdependence of components within an organization so that you can understand how changing it will affect its behavior.
  • Knowledge of Variation: it’s important to recognise the natural variation in processes and distinguish between common and special causes, the exceptions.
  • Theory of Knowledge: it’s important to understand how knowledge is created, validated, and utilized so that learning can happen.
  • Psychology: it’s important to understand the impact of human behavior and motivation on organizational performance.

The PDSA method takes these into account in its structured approach to iterative improvement. 

The PDSA method goes as follows:


The PDSA cycle begins with the planning phase, where organizations identify a problem, set specific objectives, and outline the actions necessary to achieve them. This stage involves:

  •    – Clearly defining the problem or goal.
  •    – Establishing measurable objectives.
  •    – Designing a detailed plan outlining the steps to be taken.
  •    – Identifying the data collection methods to measure success.

For example, imagine a manufacturing company aiming to reduce defects in a specific product. The planning phase would involve setting a clear target for defect reduction, designing a plan to address identified issues, and determining key performance indicators for measurement.


   The second stage involves the execution of the plan. During this phase:

  •    – The identified changes are implemented on a small scale or in a controlled environment.
  •    – Data is collected to monitor the effects of the changes.
  •    – Observations and insights from the implementation are documented.

Using the manufacturing example, this would entail applying the changes to the production process on a limited scale, closely monitoring the results, and gathering data on defect rates.


With data in hand, the study phase allows organizations to analyze the outcomes of the implemented changes. Key activities during this stage include:

  •    – Comparing the actual results to the expected outcomes outlined in the planning phase.
  •    – Identifying trends, patterns, and insights from the collected data.
  •    – Determining whether the changes had the desired impact on the problem.

In our manufacturing scenario, the study phase would involve analyzing whether the implemented changes led to a significant reduction in defects, identifying any unexpected issues, and understanding the factors influencing the outcomes.


Based on the analysis conducted during the study phase, organizations move to the action phase, where decisions are made regarding the next steps. This involves:

  •    – Deciding whether to standardize and implement the changes on a larger scale.
  •    – Adjusting the plan based on insights gained from the study phase.
  •    – Identifying and implementing additional improvements.

In the manufacturing example, if the changes led to a notable reduction in defects, the organization might decide to implement the revised process across the entire production line, standardizing the improvements.

Benefits of the PDSA Method:

Iterative Learning

The PDSA method promotes a continuous learning cycle. By breaking down improvement initiatives into smaller, manageable steps, organizations can learn from each cycle and apply those lessons to subsequent iterations. This iterative approach fosters a culture of continuous improvement and adaptability.

Data-Driven Decision Making

The emphasis on data collection and analysis in the PDSA method ensures that decisions are grounded in evidence. Organizations can rely on empirical data to evaluate the effectiveness of changes, identify trends, and make informed decisions about whether to continue, modify, or abandon specific initiatives.

Risk Mitigation

The incremental nature of the PDSA cycle reduces the risk associated with large-scale changes. By initially implementing improvements on a small scale, organizations can assess the impact and address any unforeseen challenges before scaling up. This approach minimizes the potential negative consequences of a poorly executed change.

Enhanced Team Collaboration

The PDSA method encourages cross-functional collaboration. Teams work together to plan, execute, study, and act on improvements, fostering a sense of shared responsibility for organizational success. This collaborative approach also enhances communication and knowledge-sharing among team members.

Application of the PDSA Method in Different Industries:


In the healthcare sector, the PDSA method is frequently used to enhance patient care, streamline processes, and improve overall operational efficiency. For instance, a hospital might implement small changes in patient admission procedures, collect data on waiting times, and adjust the process based on the findings to optimize patient flow.


Educational institutions leverage the PDSA cycle to refine teaching methods, curriculum design, and student learning experiences. A school might experiment with a new teaching strategy, collect student performance data, and use the insights to refine instructional approaches.

Software Development

In the realm of software development, the PDSA method is instrumental in refining software products and development processes. A software development team might implement a new coding practice, measure its impact on code quality and development speed, and iteratively improve their processes based on the observed results.


In manufacturing, the PDSA method is employed to optimize production processes, reduce defects, and enhance product quality. A manufacturing plant might introduce changes to a specific production line, monitor defect rates, and refine the process based on the data collected to ensure continuous improvement.

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