Deming’s Red Bead Experiment

1. Slides by Spiros Velianitis CSUS Deming’s Red Bead Experiment “….. Stupidly simple….. The messages learned are not”

2. Summary Slide • Red Bead Experiment Exercise • Use of STATGRAPHICS to analyze the results • Variation • Common vs. Specific (Assignable) Causes • Stable and Unstable Processes • Lessons Learned from the Red Bead Experiment

3. Red Bead Experiment Exercise (step 1) Background • I need 4 students to represent factory workers • One is “middle management” • Three are newly hired “Factory Workers” • Teacher is the CEO • Factory workers will be employed for 10 days on probation • In order for the factory to be cost effective, workers must produce no more than 8 defective (red bead) items out of 50 items produced per day Here is our work day • Factory Workers – Produce daily products • Middle management – Records daily production defects (red beads) After the 10 day production • Top management, middle management, and consultants (class) will construct a time series plot of the number of defects (red beads). • We will look for trends

4. Red Bead Experiment Exercise (step 2) Management Decisions • We will allow our factory workers to work for one more week (5 days of production) • Necessary management decisions are made (class does not know what we decided) Here is our work day for week 3 • Factory Workers – Produce daily products • Middle management – Records daily production defects (red beads) After another 5 day production • Extend the time series plot of the number of defects (red beads) to include the last 5 production defects • We will look for trends • We will examine the time series plot

5. Red Bead Experiment Exercise (step 3) Statistical analysis we will perform (identification tools) • Stationary Constant Mean Constant Variance Time Series Plot • Independence (Random) Runs Up and Down Test • Normality Shapiro-Wilks Test In STATGRAPHICS we wil • Test stationary: DTD: Describe -> Time-Series -> Descriptive Methods • Test Independence: Runs Up and Down Test Tables Options (3 T’s): select “Tests for Randomness” • Test Normality: DDF: Describe -> Distribution Fitting -> Fitting Uncensored Data Tables Options: select “Tests for Normality”

6. Lessons Learned(slides that follow

7. COMMON Numerous small causes of variability that are inherent to any system, operate randomly or by chance SPECIFIC Assignable, have relatively large effects on process, occur occasionally or sporadic VARIATION

8. Common vs. Specific (Assignable) Causes • The variation in the quality of the production output is due to either common causes or specific (assignable) causes. • Common causes: normal or natural variations in process outputs that are due purely to chance. No corrective action is necessary when output variations are due to common causes. • Specific (Assignable) causes: Variation in process outputs that are due to special circumstances or factors (machine tools wearing out, incorrect machine settings, poor-quality raw materials, operator error, etc.)

9. Stable and Unstable Processes • A process is Stable when variation made up of only common causes • In an Unstable process (process is not in statistical control), variation is made up of both common causes and specific causes • Identification Tools are used to find if specific causes of variation exist within a process

10. Lessons Learned from the Red Bead Experiment • Variation is an inherent part of any process • Only management can change the process • Identification tools (time series plots, non parametric runs up and down test, and the Shapiro-Wilk test) can help us identify specific variation • Workers work within a system over which they have little control. The system determines their performance. • The work standard set by management may have no relationship to the capability of the system.

11. Summary Slide • Red Bead Experiment Exercise • Use of STATGRAPHICS to analyze the results • Variation • Common vs. Specific (Assignable) Causes • Stable and Unstable Processes • Lessons Learned from the Red Bead Experiment