Six Sigma DMAIC – Control Stage

DMAIC Control Stage

The Control stage is the final step in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve, Control) process. It is an essential phase that ensures process improvements’ sustainability and long-term success. In this stage, control plans, statistical tools such as Xbar-R Chart and U Chart, cost-benefit analysis, the impact of variation, and sampling frequency play a crucial role in maintaining the gains achieved during the previous stages.

Control Plans:

A control plan outlines the activities and processes needed to maintain and control the improved process. It serves as a guide to ensure that the process meets customer requirements and delivers consistent results. The control plan typically includes information about process monitoring, measurement techniques, frequency of data collection, key performance indicators (KPIs), and actions to be taken in case of process deviations.

Control plans serve as a reference guide for operators, supervisors, and other stakeholders. A well-defined control plan ensures that the process consistently meets customer requirements and minimizes the risk of process deviations.

Here’s an example to illustrate the components of a control plan:

Process: Manufacturing of Widget XYZ

  1. Process Monitoring:
    • Measure the key process parameters, such as temperature, pressure, and time, at specific checkpoints during production.
    • Use calibrated instruments to ensure accurate measurements.
    • Record the measurements on a data collection sheet.
  2. Control Methods:
    • Implement visual controls, such as color-coded labels or checklists, to guide operators through the process steps and ensure adherence to standard procedures.
    • Conduct regular audits to verify compliance with control methods and identify any deviations.
  3. Measurement Techniques:
    • Use a digital thermometer to measure temperature.
    • Employ a pressure gauge to measure pressure.
    • Utilize a stopwatch or timer to measure process time.
    • Record measurements in the appropriate units (e.g., Fahrenheit, PSI, seconds).
  4. Frequency of Data Collection:
    • Collect measurements at each production batch’s start, middle, and end.
    • Perform additional measurements if there are any changes in process conditions, such as shift changes or equipment adjustments.
  5. Key Performance Indicators (KPIs):
    • Monitor the defect rate by counting the number of defective widgets per batch.
    • Track the process yield by calculating the percentage of conforming widgets.
  6. Process Deviation Actions:
    • If a measurement falls outside the defined control limits, immediately stop the process and investigate the root cause of the deviation.
    • Engage a cross-functional team to identify corrective actions, implement them, and document the results.
    • Update the control plan if necessary to prevent similar deviations in the future.
  7. Documentation and Reporting:
    • Maintain a record of all measurement data, process deviations, and corrective actions taken.
    • Generate regular reports to communicate the process performance and any improvements or issues to relevant stakeholders.

Remember, control plans should be customized to the specific process and organization, considering factors such as process complexity, criticality, and customer requirements. By effectively implementing and following control plans, organizations can ensure that process improvements are sustained and consistently meet the desired quality standards.

Xbar-R Chart:

The Xbar-R Chart, also known as the Xbar-Range Chart, is a statistical tool used to monitor a process’s central tendency and variation over time. It consists of two parts: the Xbar chart, which displays the average (mean) of a series of measurements, and the R chart, which shows the range (the difference between the highest and lowest values) of the same measurements. The Xbar-R Chart helps identify any shifts or trends in the process mean or variation, enabling prompt corrective action.

U Chart:

The U Chart is another statistical control chart used in Six Sigma. It is specifically designed for monitoring count data, such as defects per unit or events per unit. The U Chart displays the number of occurrences of a specific event or defect within a fixed sample size. It helps identify whether the process is stable or experiencing variations that may lead to defects. By monitoring the U Chart, organizations can take proactive steps to prevent the occurrence of defects and maintain process control.

Cost-Benefit Analysis:

During the Control stage, conducting a cost-benefit analysis is vital. It involves assessing the financial impact of implementing and maintaining the improved process. The analysis compares the costs of implementing controls and monitoring mechanisms against the potential benefits gained from the improved process. It helps organizations make informed decisions about the feasibility and sustainability of process improvements, ensuring that the benefits outweigh the costs in the long run.

A cost-benefit analysis (CBA) is a systematic approach that allows stakeholders to assess a project’s financial and non-financial implications.

Defining Cost-Benefit Analysis: Cost-benefit analysis is a technique used to compare the expected costs and benefits of a project or decision. It involves quantifying and assigning a monetary value to both the positive and negative impacts, allowing decision-makers to evaluate whether the benefits outweigh the costs. A cost-benefit analysis provides a comprehensive framework for decision-making by examining the financial and non-financial aspects.

Determining Project Costs: One of the primary steps in a cost-benefit analysis is identifying and estimating all relevant costs associated with the project. These costs include direct expenses such as materials, labor, equipment, and any additional indirect costs like overhead, training, and maintenance. It is essential to consider both short-term and long-term costs to gain a holistic understanding of the financial implications of the project.

Quantifying Project Benefits: In addition to costs, a cost-benefit analysis requires a thorough evaluation of the project’s expected benefits. These benefits can vary depending on the nature of the project and may include increased revenue, cost savings, improved efficiency, enhanced customer satisfaction, environmental impact, or societal benefits. Assigning a monetary value to these benefits allows for a meaningful comparison against the project costs.

Time Value of Money and Discounting: A crucial aspect of cost-benefit analysis is recognizing the time value of money. Future costs and benefits are adjusted to their present value to account for the potential earning capacity of funds over time. This process, known as discounting, ensures that the analysis considers the opportunity cost of investing capital in the project.

Risk and Sensitivity Analysis: Projects are inherently subject to risks and uncertainties. A robust cost-benefit analysis incorporates risk assessment and sensitivity analysis to account for potential variations in the estimated costs and benefits. By exploring different scenarios and assessing the project’s sensitivity to changing variables, decision-makers can make more informed choices and understand the project’s resilience against various external factors.

Decision-Making and Project Feasibility: A cost-benefit analysis’s ultimate goal is to determine a project’s feasibility and viability. By comparing the net present value (NPV), internal rate of return (IRR), or other relevant financial metrics, decision-makers can assess whether the benefits outweigh the costs and make informed choices about project implementation. This analysis provides a clear picture of the financial implications, allowing stakeholders to prioritize projects and allocate resources efficiently.

Considering Non-Financial Factors: While financial considerations are central to a cost-benefit analysis, it is crucial not to overlook non-financial factors. A project may have intangible benefits such as improved brand reputation, employee morale, or community goodwill that cannot be easily quantified. Including these non-financial factors in the analysis ensures a more comprehensive assessment and a well-rounded decision-making process.

In the world of project management, making sound decisions is vital to achieving success. A cost-benefit analysis serves as a powerful tool for evaluating project feasibility, allowing stakeholders to assess the potential costs and benefits associated with a project. By incorporating financial and non-financial considerations, considering the time value of money, and accounting for risks and uncertainties, decision-makers can make informed choices that maximize benefits and minimize risks. Conducting a thorough cost-benefit analysis ensures that projects align with organizational goals and contribute to long-term success.

Impact of Variation:

Variation can have a significant impact on process performance and customer satisfaction. The Control stage minimizes and controls variation to ensure consistent and predictable results. By using statistical control charts and other monitoring techniques, organizations can identify sources of variation and take corrective actions to reduce their impact. The goal is to achieve a stable and predictable process that meets customer requirements within defined tolerances.

Frequency of Sampling:

Determining the frequency of sampling is crucial in the Control stage. It refers to how often data is collected and analyzed to monitor process performance. The frequency depends on the process stability, criticality, and the potential risks associated with process variations. Generally, more frequent sampling is recommended in the early stages of process improvement to monitor the process closely. As the process stabilizes and demonstrates consistent performance, the frequency of sampling can be adjusted accordingly.


The Control stage of the DMAIC process is essential for maintaining the improvements achieved during the earlier stages. Control plans, statistical tools like the Xbar-R Chart and U Chart, cost-benefit analysis, impact of variation, and frequency of sampling are key components in this stage. By effectively implementing these elements, organizations can sustain process improvements, ensure customer satisfaction, and drive long-term success in their operations.

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