Short Term Sample / Long Term Sample

Long term and Short Term Comparison

SHORT term sample:

1) Free from assignable or special cause

2) Represents random causes only

3) Group of similar things

4) Collected across a narrow inference space

5) Data from one lot of material, on one shift, one part, one machine, one operator

LONG term sample:

1) Consists of random and assignable causes

2) Collected across a broad inference space

3) Data from several lots, many shifts, many machines and operators

Date across many short term (within) samples is shown below in the illustration that when the data is combined it exhibits the long term distribution. The long term distribution includes all the short term distributions.

Short Term and Long Term Sample

Applying Measurements

As a general rule, six sigma performance is a long term process that creates a level of 3.4 defects per million opportunities (DPMO).

If the area under the normal curve represents one million opportunities then approximately 3.4 of them would be outside of the customer specification limit(s) when shifted 1.5 sigma to account for all the short term shifts.

A six sigma process refers to the process short-term performance or how it is performing currently. When referring to DPMO of the process, we are referring to long-term or projected performance behavior. DPMO is a more exact and informative measurement than PPM.

A six sigma level of performance has 3.4 defects per million opportunities (3.4 DPMO). A current six sigma process now will have a estimated shift of 1.5 sigma (lower) in the future and will perform at a 4.5 sigma level, which produces 3.4 DPMO.

A typical process has been proven to have a shift in its average performance of up to +/- 1.5 sigma over the long term. A long term Six Sigma process that is rated at 4.5 sigma is considered to have a short term sigma score of 6 sigma. The combination of all the short term samples that make up the long term performance will create no more than 3.4 defects per million opportunities.

A process, product, or service would need to create conformance

999,996.6 times for every 1,000,000 opportunities

and sustaining a process mean shift of up to 1.5 standard deviations (sigma).

The Six Sigma methodology focuses on variation reduction within a process and designing new processes or products that will perform at a near perfect and consistent level over the long term. The idea is to have the best term performance be the actual long term performance, the long term performance does not have to be -1.5 sigma lower, but studies show this is usually the case

SHORT TERM process capability metrics

  • DPU or DPO
  • Short term Sigma
  • Cpk
  • Cp (the best a process can perform) LONG TERM process capability metrics
  • DPMO or PPM (be consistent, use one to describe short and long term)
  • Long term sigma
  • Ppk
  • Pp 
  • DPMO is NOT the same as PPM since it is possible that each unit (part) being appraised may be found to have multiple defects of the same type or may have multiple types of defects.

    A part is defective if it has one or more defects. Defectives can never exceed defects. IF each part only has one characteristic that can be a defect, then DPMO and PPM will be the same. 

    DPMO will always exceed or equal PPM for a given yield or sigma level of performance.

    DPMO and Sigma score Calculator

    Get the DPMO and Sigma Calculator where you can enter values and scenarios and several metrics are calculated with the formulas shown within the cells. 

    Return to Population and Samples

    Subscribe for access to all pages within this site

    Templates and Calculators

    Return to the Six-Sigma-Material Home Page

    Site Membership
    Click for a Password

    Six Sigma
    Templates & Calculators
    $14.95 USD

    Six Sigma & Lean

    (online, onsite, classroom)

    Six Sigma Courses

    Six Sigma Modules

    The following presentations are available to download

    Click Here

    Green Belt Program 1,000+ Slides
    Basic Statistics
    Process Mapping
    Capability Studies
    Cause & Effect Matrix
    Multivariate Analysis
    Central Limit Theorem
    Confidence Intervals
    Hypothesis Testing
    T Tests
    1-Way Anova Test
    Chi-Square Test
    Correlation and Regression
    Control Plan