2.0 Measure

Lean Six Sigma Refresher: The Measure Phase

The Measure Phase is where clarity meets data. After defining the problem, practitioners must quantify the current state, validate measurement systems, and establish a baseline for improvement. This phase ensures that subsequent analysis and solutions are grounded in reliable, objective information.

2.1 Process Definition

2.1.1 Cause & Effect / Fishbone Diagrams

  • Visual tool to brainstorm potential causes of a problem.

  • Categories often include People, Methods, Machines, Materials, Environment, Measurement.

  • Helps teams avoid tunnel vision by considering multiple root cause pathways.

2.1.2 Process Mapping, SIPOC, Value Stream Map

  • Process Mapping: Documents step-by-step workflow to identify inefficiencies.

  • SIPOC: High-level view of Suppliers, Inputs, Process, Outputs, Customers.

  • Value Stream Map: Captures material and information flow, highlighting waste and bottlenecks.

2.1.3 X-Y Diagram

  • Links inputs (X’s) to outputs (Y’s).

  • Prioritizes critical factors influencing performance.

  • Serves as a bridge to later statistical analysis.

2.1.4 Failure Modes & Effects Analysis (FMEA)

  • Structured approach to identify potential failure points.

  • Evaluates Severity, Occurrence, Detection to calculate Risk Priority Number (RPN).

  • Guides preventive actions before failures impact customers.

2.2 Six Sigma Statistics

2.2.1 Basic Statistics

  • Core measures: mean, median, mode, range, variance, standard deviation.

  • Foundation for understanding variation.

2.2.2 Descriptive Statistics

  • Summarizes data sets with measures of central tendency and dispersion.

  • Provides quick insights into process behavior.

2.2.3 Normal Distributions & Normality

  • Many statistical tests assume normality.

  • Tools: histograms, probability plots, Shapiro-Wilk test.

  • Recognizing deviations from normality is critical for valid analysis.

2.2.4 Graphical Analysis

  • Visual tools like box plots, scatter plots, histograms.

  • Makes variation and trends immediately visible to stakeholders.

2.3 Measurement System Analysis (MSA)

2.3.1 Precision & Accuracy

  • Precision: Consistency of repeated measurements.

  • Accuracy: Closeness to the true value.

  • Both are essential for trustworthy data.

2.3.2 Bias, Linearity & Stability

  • Bias: Systematic error in measurement.

  • Linearity: Consistency across measurement ranges.

  • Stability: Reliability of measurement over time.

2.3.3 Gage Repeatability & Reproducibility (GR&R)

  • Evaluates variation from the measurement device (repeatability) and operators (reproducibility).

  • Ensures measurement system is fit for purpose.

2.3.4 Variable & Attribute MSA

  • Variable MSA: Continuous data (e.g., dimensions, weight).

  • Attribute MSA: Discrete data (e.g., pass/fail, defect categories).

2.4 Process Capability

2.4.1 Capability Analysis

  • Compares process performance to specification limits.

  • Metrics: Cp, Cpk, Pp, Ppk.

2.4.2 Concept of Stability

  • A stable process shows consistent performance over time.

  • Stability is a prerequisite for capability analysis.

2.4.3 Attribute & Discrete Capability

  • Evaluates defect rates for categorical outcomes.

  • Uses binomial or Poisson distributions depending on data type.

2.4.4 Monitoring Techniques

  • Control charts and run charts track ongoing performance.

  • Early detection of shifts prevents costly defects.

Final Thoughts

The Measure Phase is about building confidence in the data. By defining processes, validating measurement systems, and quantifying capability, practitioners create a solid foundation for the Analyze Phase. Without reliable measurement, even the most sophisticated analysis risks being misleading. Certified professionals should treat this phase as a discipline of rigor and precision, ensuring every improvement project rests on trustworthy evidence.