Statistical Process Control and Six Sigma Flashcards
PDCA
Statistical Process Control
- Understanding the process
- Understanding the causes of variation
- Eliminiation of the sources of special cause variation
Usage Control Charts
1 Select process
2 Identify product or process characteristics that describe process performance
3 Select the appropriate type of control chart
4 Measure process performance over a period of time
5 Use appropriate calculations based on measurement data to determine center lines and control limits for performance characteristics
6 Plot measurement data on control charts
7 Are all measured values within limits and distributed randomly around centerlines?

8 Process is stable; continue measuring
9 Process is not stable
10 Identify and remove assignable causes (Back to 4)
Common questions for investigating an out-of-control process
- Are there differences in the measurement accuracy of instruments/medhotds used?
- Are there differences in the methods used by different personnel?
- Is the process affected by the environment?
- Has there been a significant change in the environment?
- Is the process affected by predictable conditions?
- Were any untrained personnel involved in the process at the time?
- Has there been a change in the soruce for input to the process?
- Is the process affected by employee fatigue?
- Has there been a change in policies or procedures?
- Is the process adjusted frequently?
- Did the samples come from different part of the process? Shifts? Individuals?
- Are employees afraid to report ‘bad news’?
Each team should address each ‘Yes’ answer as a potential source of a special cause.
Fishbone Chart
Six Sigma
- A business philosophy focusing on continuous improvement
- Methodology for improving key processes
- 2 key methodologies
- DMAIC
- DMADV
- DMAIC is used to improve an existing business process
- DMADV is used to create new product or process designs for predictable, defect-free performance
- Is based on the following key underling principles of statistical thinking:
- Everything is a process
- All processes have inherent variability
- Data is used to understand variation and to drive decisions to improve the process
Defects per Million Opportunities
A six sigma process is one in which 99.99966% of the products manufactured are statistically expected to be free of defects (3.4 defects per million)
DMAIC
A general-purose problem solving methodology
Problem or goal statement (Y)
Define
- Refine problem and goal statements
- Define project scope and boundaries
Measure - Analyze - Improve - Control
- An improvement journey to achieve goals and resolve problems by discovering and understanding relationships between process inputs and outputs, such as.
Y = Effectiveness of inspections
= f(size, complexity, duration, reading technique)
DMAIC Roadmap
Define:
- Define Project Scope
- Establish formal project
Measure:
- Identify needed data
- Obtain data set
- Evaluate data quality
- Summarize & baseline data
Analyze:
- Exploure data
- Characterize process & problem
- Update improvement project scope & scale
Improve:
- Identify possible solutions
- Select solution
- Implement (pilot as needed)
- Evaluate
Control:
- Define control method
- Implement
- Document
Revising and Updating Control Limits
- Revising and updating control charts both involve recalculating the control limits but for different reasons and somtimes in different ways.
Revising Control Limits
- You use the initial or trial limits for an onoing chart to omit the following from calculations:
- Unrepresentative data
- Special causes
Updating Control Limits
- You use additional, more recently collected data to re-compute the limits based on the following changes:
- More data is available
- The process has shifted
- A deliberate change ahs been made to the process
Quartiles
A quartile is any of the three values which divide the sorted data set into 4 equal parts, so that each part represents 1/4th of the sample or population.
i.e. Sorted data can be scatter plot, bell curve, box plot etc
Box Plot
Lower tail, Upper tail
Median
Lower quartile, Upper quartile
Scale
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