Discrete Capability, also known as Attribute Capability Analysis, is a critical concept in the Measure Phase of Lean Six Sigma that evaluates how well a process performs when dealing with discrete or attribute data. Unlike continuous data that can take any value within a range, discrete data involv…Discrete Capability, also known as Attribute Capability Analysis, is a critical concept in the Measure Phase of Lean Six Sigma that evaluates how well a process performs when dealing with discrete or attribute data. Unlike continuous data that can take any value within a range, discrete data involves countable, categorical outcomes such as pass/fail, good/bad, or defect counts.
In discrete capability analysis, practitioners assess the proportion of defective units or defects per unit produced by a process. The primary metrics used include Defects Per Unit (DPU), Defects Per Million Opportunities (DPMO), and the corresponding Sigma Level. These measurements help organizations understand their current process performance and identify improvement opportunities.
To calculate discrete capability, you first define what constitutes a defect and identify the total number of opportunities for defects to occur in each unit. Then, by collecting sample data and counting actual defects, you can determine the defect rate. The DPMO calculation involves dividing total defects by total opportunities and multiplying by one million, providing a standardized metric for comparison across different processes.
The Sigma Level derived from DPMO indicates process capability on the Six Sigma scale. A higher sigma level represents fewer defects and better process performance. For instance, a Three Sigma process produces approximately 66,807 DPMO, while a Six Sigma process achieves only 3.4 DPMO.
Practitioners use tools like Pareto charts, control charts for attributes (p-charts, np-charts, c-charts, u-charts), and capability analysis software to visualize and analyze discrete data. This analysis reveals patterns, trends, and areas requiring attention.
Understanding discrete capability enables teams to establish baseline performance, set realistic improvement targets, and track progress throughout the DMAIC methodology. It provides a foundation for data-driven decision making and helps prioritize resources toward the most impactful improvement initiatives.
Discrete Capability: A Complete Guide for Six Sigma Green Belt
What is Discrete Capability?
Discrete capability, also known as attribute capability or defect-based capability, is a process capability analysis method used when dealing with discrete (attribute) data rather than continuous data. Discrete data involves counts or classifications, such as pass/fail, defective/non-defective, or number of defects per unit.
Why is Discrete Capability Important?
Understanding discrete capability is essential because:
• Many real-world quality measurements are attribute-based (go/no-go gauges, visual inspections, presence/absence of features) • It allows comparison of process performance against customer requirements • It helps identify improvement opportunities in processes that produce countable outcomes • It provides a standardized way to communicate process performance using metrics like DPMO, DPU, and Yield
How Discrete Capability Works
Key Metrics:
1. Defects Per Unit (DPU) DPU = Total Number of Defects / Total Number of Units
2. Defects Per Opportunity (DPO) DPO = Total Defects / (Total Units × Opportunities per Unit)
3. Defects Per Million Opportunities (DPMO) DPMO = DPO × 1,000,000
4. Yield Calculations: • First Time Yield (FTY) = Units passing first time / Total units • Rolled Throughput Yield (RTY) = FTY₁ × FTY₂ × FTY₃... (product of all process step yields) • Final Yield = e^(-DPU)
5. Sigma Level The sigma level can be determined from DPMO using conversion tables or the inverse normal distribution function.
Converting Between Metrics:
• From DPMO to Sigma: Use Z-tables or the formula involving the inverse standard normal distribution • A 6 Sigma process has 3.4 DPMO • A 3 Sigma process has approximately 66,807 DPMO
Exam Tips: Answering Questions on Discrete Capability
1. Know Your Formulas Memorize the key formulas for DPU, DPO, DPMO, and yield calculations. Exam questions frequently require calculations.
2. Identify the Data Type Before selecting an analysis method, confirm whether the question involves discrete (attribute) or continuous data. Discrete capability applies to countable, categorical data.
3. Watch for Opportunities Pay close attention to the number of defect opportunities per unit. This is crucial for calculating DPO and DPMO correctly.
4. Understand Yield Types Distinguish between FTY, RTY, and Final Yield. RTY multiplies individual yields across process steps, while FTY focuses on a single step.
5. Use the Poisson Assumption Remember that discrete capability often assumes defects follow a Poisson distribution, which is valid when defect occurrence is rare and random.
6. Check Units Carefully Ensure you are working with consistent units. DPMO is per million opportunities, so verify your multiplication factor.
8. Read Questions Thoroughly Determine if the question asks for defects, defectives, or opportunities. These terms have specific meanings in capability analysis.
Common Exam Question Types:
• Calculating DPMO from given defect data • Converting DPMO to Sigma level • Computing RTY across multiple process steps • Identifying when to use discrete vs. continuous capability • Interpreting capability results for process improvement decisions