Lean Six Sigma Notes

Lean vs Six Sigma

  • Lean Principles:
    • Focus on eliminating waste, inspired by Toyota's practices.
    • Critical Questions:
    • Does this step need to exist?
  • Six Sigma:
    • Aims to improve process performance by eliminating the causes of defects and errors, as demonstrated by Motorola.
    • Critical Question:
    • How can this process be improved?

Understanding Waste

  • Consequences of Waste:
    • Increased costs
    • Delays in processes
    • Limited efficiency
  • Lean Six Sigma Strategy:
    • Employs a problem-cause-solution approach to improve processes.
    • Methodology: DMAIC (Define, Measure, Analyze, Improve, Control)

Sigma Metrics

  • Sigma Definition:
    • Quantitative measurement of error or variation in a system.
    • Process sigma indicates the capability of a process to meet or exceed defined acceptance criteria.
    • Key Metrics:
    • Number of defects per million opportunities
    • Number of Standard Deviations (SD) from mean a process can shift before exceeding acceptable limits.
  • Example:
    • If a sodium test has a six sigma quality, the mean could shift by 6 SD and still meet accuracy and precision requirements.

Sigma and Errors

  • 6 Sigma Quality:
    • Very precise; produces 3 errors for every million tests reported.
  • 3 Sigma Quality:
    • Produces 26,674 errors per million tests.

Importance of Sigma Beyond the Analytical Stage

  • 12% of lab errors impact patient health.
  • Only 0.0375% of errors occur in the analytical stage.

Sigma and Quality Control (QC)

  • Excellent precision and accuracy lead to fewer errors.
  • A significant mean shift is necessary to fail quality requirements.
  • Fewer QC rules may be needed to identify errors.
    • False Rejection: Maximizing the chance of detecting a problem while reducing the risk of incorrectly discarding a correct result.
  • Sigma Representation: \text{Sigma} = \mu - \text{(mean)}

Goal of Six Sigma for World-Class Quality

  • Tolerance Specification:
    • Aiming for 6 SDs within specification limits.
  • Target Specification:
    • Ensures +6 SDs fit into specification.
    • Visual representation of distribution enables understanding acceptable limits.

Minimum Acceptable Performance Levels

  • 3 Sigma:
    • Represents minimum performance standards with an increase in the number of allowable defects.

Accuracy and Precision Metrics

  • Assess the observed %Bias and %Coefficient of Variation (CV)
  • Create a decision chart to classify performance levels based on Sigma levels (2, 3, 4, 5, 6).

Example Sigma-Metric Calculation

  • Cholesterol Study Example (Clin Chem July 2014):
    • Acceptability criterion: 10% (CLIA PT criterion).
    • Calculating Total Precision (CV):
    • Bias values: 1.0%, 0.9%, 1.0%
  • Sigma Calculation:
    • For 1.0% Bias: \text{Sigma} = \frac{(10 - 3)}{1.0} = \frac{7.0}{1.0} = 7.0
    • For 0.9% Bias: \text{Sigma} = \frac{(10 - 2.5)}{0.9} = \frac{7.5}{0.9} = 8.3
    • For 1.0% Bias: \text{Sigma} = \frac{(10 - 2.3)}{1.0} = \frac{7.7}{1.0} = 7.7
  • Average Sigma:
    • \text{Average Sigma} = \frac{(7.0 + 8.3 + 7.7)}{3} = 7.67

Westgard Sigma Rules

  • Data Quality Control (QC) guidelines including:
    • Conditions for reporting results
    • Recommendations for corrective actions based on Sigma metrics.
  • Sigma Scale Formula: \text{Sigma Scale} = \frac{(%TEa - %Bias)}{%CV}
    • Utilize the Sigma Scale to locate and understand the performance standards.