Programs

Control Practices

Program Description

The Control Practices program provides students with the prerequisite training needed to develop the knowledge and skills necessary for understanding and applying the essential concepts, tools, procedures, practices needed to use statistical process control (SPC). Upon completion of this training, the participant will be able to implement and sustain several common types of SPC charts for variables and attribute data.

Major emphasis is placed on the ways and means for centering a process and reducing its range of operation; thereby, increasing process capability while concurrently reducing the probability of defects. Of course, this leads to the reduction of overall operating costs. Instruction includes how to plan a control chart study, how to implement successful sampling strategies, how to compute the underpinning statistics, as well as how to uncover nonrandom trends and events commonly associated with underperforming processes. In addition, you will learn a many of the “real world” situations in which SPC charts can be effectively employed to enhance control, reduce variation and generate additional value, to the benefit of the customer and provider.

Throughout this curriculum, particular attention is paid to the planning, organizing, constructing, implementing, interpreting and sustaining statistical process control charts. Key insights are developed for applications, chronic process control problems and low volume environments. In addition, the development of analytical philosophy and language serves to augment the existing skills of participants. Much emphasis is placed on the construction and interpretation of SPC charts in industrial and commercial organizations. Hence, this training can be effectively put to use in small, medium and large organizations.

Reinforcement of major concepts, techniques, and application is realized through exercise, scenarios, case studies, and field studies. Total instructional time for this program is approximately 60 hours.


      Printable Program Outline

Training Orientation

Excel Orientation - Explore the Excel software package

Minitab Orientation - Explore the Minitab software package

Simulator Orientation - Explore the Process Simulator

Breakthrough Vision

Deterministic Reasoning - Describe a basic cause-and-effect relationship in terms of Y=f(X)

Leverage Principle - Relate the principle of leverage to an improvement project

Process Management

Performance Yield - Explain why final yield is often higher than first-time yield

Hidden Processes - Describe the non-value added component of a process

Measurement Power - Describe the role of measurement in an improvement initiative

Establishing Baselines - Explain why performance baselines are essential to realizing improvement

Defect Opportunity - Understand the nature of a defect opportunity and its role in metrics reporting

Process Models - Define the key features of a Six Sigma performance model

Process Capability - Identify the primary indices of process capability

Design Complexity - Describe the impact of complexity on product and service quality

Quality Tools

Variable Classifications - Define the various types of variables commonly encountered during quality improvement

Measurement Scales - Describe each of the four primary scales of measure and their relative power

Problem Definition - Characterize the nature of a sound problem statement

Focused Brainstorming - Explain how focused brainstorming is used to facilitate improvement efforts

Process Mapping - Understand how to define the flow of a process and map its operations

Performance Sampling - Explain how to design and implement a sampling plan

Check Sheets - Understand how check sheets can be used for purposes of data collection

Analytical Charts - Identify the general range of analytical charts that can be used to assess performance

Pareto Charts - Explain how Pareto charts can be used to isolate improvement leverage

Run Charts - Utilize run charts to assess and characterize time-based process data

Correlation Charts - Utilize a correlation chart to illustrate the association between two variables

Frequency Tables - Explain how to construct and interpret a frequency table

Performance Histograms - Construct and interpret a histogram and describe several purposes

Basic Probability - Understand basic probability theory and how it relates to process improvement

Pre-Control Charts - Describe the fundamental rules that guide the operation of a standard pre-control plan

Control Charts - Explain the purpose of statistical process control charts and the logic of their operation

Score Cards - Understand the purpose of Six Sigma score cards and how they are deployed

Basic Statistics

Performance Variables - Identify and describe the types of variables typically encountered in field work

Statistical Notation - Recognize and interpret the conventional forms of statistical notation

Performance Variation - Explain the basic nature of variation and how it can adversely impact quality

Normal Distribution - Describe the features and properties that are characteristic of a normal distribution

Distribution Analysis - Explain how to test the assumption that a set of data is normally distributed

Location Indices - Identify, compute, and interpret the mean, median, and mode

Dispersion Indices - Identify, compute, and interpret the range, variance, and standard deviation

Quadratic Deviations - Understand the nature of a quadratic deviation and its basic purpose

Variation Coefficient - Compute and interpret the coefficient of variation

Deviation Freedom - Explain the concept of degrees-of-freedom and how it is used in statistical work

Standard Transform - Describe how to transform a set of raw data into standard normal deviates

Standard Z-Probability - Describe how to convert a standard normal deviate into its corresponding probability

Central Limit - Understand that the distribution of sampling averages follows a normal distribution

Standard Error - Recognize that the dispersion of sampling averages is described by the standard error

Student's Distribution - Understand that the T distribution applies when sampling is less than infinite

Standard T-Probability - Describe how to convert a T value into its corresponding probability

Statistics Simulation - Employ basic statistics to analyze data generated by the process simulator

Continuous Capability

Performance Specifications - Explain the basic nature and purpose of performance specification limits

Rational Subgrouping - Explain how to form rational subgroups and describe their purpose in Six Sigma work

Capability Study - Understand the concept of process capability and how it applies to products and services

Instantaneous Capability - Understand the concept of instantaneous capability in relation to Six Sigma work

Longitudinal Capability - Understand the concept of longitudinal capability in relation to Six Sigma work

Cp Index - Compute and interpret Cp

Cpk Index - Compute and interpret Cpk

Pp Index - Compute and interpret Pp

Ppk Index - Compute and interpret Ppk

Process Shifting - Understand the impact of process centering error on short-term capability

Process Qualification - Determine the required level of short-term capability necessary to qualify a process

Instruction Videos
1. Nature and Purpose of Process Qualification - Part A -  2m 09s - 2.21 MB
2. Nature and Purpose of Process Qualification - Part B -  1m 45s - 1.79 MB
3. Elements of a Process Qualification Study - Part A -  2m 40s - 2.73 MB
4. Elements of a Process Qualification Study - Part B -  5m 52s - 5.95 MB
5. Elements of a Process Qualification Study - Part C -  3m 54s - 3.99 MB
6. Elements of a Process Qualification Study - Part D -  3m 27s - 3.53 MB
7. Linking Statistics to Process Qualification - Part A -  4m 56s - 5.02 MB
8. Linking Statistics to Process Qualification - Part B -  5m 28s - 5.54 MB
Expansion Videos
9. Six Sigma Models and Process Qualification - Part A -  6m 34s - 3.32 MB
10. Six Sigma Models and Process Qualification - Part B -  8m 09s - 7.96 MB
11. Six Sigma Models and Process Qualification - Part C -  4m 29s - 2.49 MB
12. Six Sigma Models and Process Qualification - Part D -  9m 33s - 8.33 MB
13. Six Sigma Models and Process Qualification - Part E -  11m 04s - 7.16 MB
Application Videos
14. Calculation of Process Qualification Statistics - Part A -  3m 03s - 1.45 MB
15. Calculation of Process Qualification Statistics - Part B -  3m 58s - 2.35 MB
16. Calculation of Process Qualification Statistics - Part C -  4m 11s - 2.50 MB
17. Calculation of Process Qualification Statistics - Part D -  4m 51s - 2.41 MB
18. Simulation of Process Qualification Statistics - Part A -  7m 17s - 8.82 MB
19. Simulation of Process Qualification Statistics - Part B -  6m 00s - 5.09 MB
Supporting Media
Summary Slides: Process Qualification

ConcaP Simulation - Apply continuous indices of capability to the process simulator

Discrete Capability

Defect Metrics - Identify and describe the defect metrics commonly used in Six Sigma work

Defect Opportunities - Understand the nature and purpose of defect opportunities in terms of quality reporting

Binomial Distribution - Describe the features and properties that are characteristic of a binomial distribution

Poisson Distribution - Describe the features and properties that are characteristic of the Poisson distribution

Throughput Yield - Compute and interpret throughput yield in the context of Six Sigma work

Rolled Yield - Compute and interpret rolled-throughput yield in the context of Six Sigma work

Metrics Conversion - Convert yield and defect metrics to the sigma scale of measure

Instruction Videos
1. Practical Need for Converting Performance Metrics -  6m 43s - 6.36 MB
Application Videos
2. Case of Zero Defects and Sigma Values - Part A -  4m 25s - 5.24 MB
3. Case of Zero Defects and Sigma Values - Part B -  6m 27s - 7.07 MB
4. First-Order Conversion of Performance Metrics - Part A -  3m 16s - 3.91 MB
5. First-Order Conversion of Performance Metrics - Part B -  3m 06s - 3.21 MB
6. First-Order Conversion of Performance Metrics - Part C -  3m 44s - 4.85 MB
7. First-Order Conversion of Performance Metrics - Part D -  5m 29s - 6.16 MB
8. First-Order Conversion of Performance Metrics - Part E -  1m 41s - 2.06 MB
9. First-Order Conversion of Performance Metrics - Part F -  3m 52s - 4.23 MB
10. First-Order Conversion of Performance Metrics - Part G -  5m 17s - 5.86 MB
11. First-Order Conversion of Performance Metrics - Part H -  3m 35s - 4.33 MB
12. First-Order Conversion of Performance Metrics - Part I -  2m 28s - 3.43 MB
13. First-Order Conversion of Performance Metrics - Part J -  2m 49s - 3.85 MB
14. First-Order Conversion of Performance Metrics - Part K -  2m 57s - 4.03 MB
15. First-Order Conversion of Performance Metrics - Part L -  5m 44s - 7.71 MB
16. First-Order Conversion of Performance Metrics - Part M -  3m 03s - 4.00 MB
17. First-Order Conversion of Performance Metrics - Part N -  1m 26s - 1.79 MB
18. First-Order Conversion of Performance Metrics - Part O -  2m 20s - 2.78 MB
19. First-Order Conversion of Performance Metrics - Part P -  5m 29s - 7.11 MB
20. First-Order Conversion of Performance Metrics - Part Q -  5m 48s - 7.61 MB
21. First-Order Conversion of Performance Metrics - Part R -  3m 38s - 5.11 MB
22. First-Order Conversion of Performance Metrics - Part S -  4m 12s - 3.88 MB
23. First-Order Conversion of Performance Metrics - Part T -  4m 50s - 5.79 MB
Supporting Media
Summary Slides: Metrics Conversion

DiscaP Simulation - Apply discrete indices of capability to the process simulator

Hypothesis Testing

Statistical Inferences - Explain the concept of a statistical inference and its primary benefits

Statistical Questions - Explain the nature and purpose of a statistical question

Statistical Problems - Understand why practical problems must be translated into statistical problems

Null Hypotheses - Define the nature and role of null hypotheses when making process improvements

Alternate Hypotheses - Define the nature and role of alternate hypotheses when making process improvements

Statistical Significance - Explain the concept of statistical significance versus practical significance

Alpha Risk - Explain the concept of alpha risk in terms of the alternate hypothesis

Beta Risk - Define the meaning of beta risk and how it relates to test sensitivity

Criterion Differences - Explain the role of a criterion difference when testing hypotheses

Decision Scenarios - Develop a scenario that exemplifies the use of hypothesis testing

Sample Size - Define the statistical elements that must be considered when computing sample size

Instruction Videos
1. Nature and Implications of Sample Size - Part A -  3m 50s - 3.89 MB
2. Nature and Implications of Sample Size - Part B -  3m 58s - 4.06 MB
3. Determination of Sample Size for Experiments - Part A -  7m 00s - 7.12 MB
4. Determination of Sample Size for Experiments - Part B -  8m 13s - 8.35 MB
5. Key Considerations for Computing Sample Size -  9m 41s - 9.85 MB
6. Calculation of an Appropriate Sample Size - Part A -  5m 14s - 5.32 MB
7. Calculation of an Appropriate Sample Size - Part B -  3m 15s - 3.32 MB
8. Calculation of an Appropriate Sample Size - Part C -  5m 26s - 5.51 MB
9. Calculation of an Appropriate Sample Size - Part D -  7m 42s - 7.85 MB
10. Calculation of an Appropriate Sample Size - Part E -  6m 12s - 6.29 MB
11. Relationship between Six Sigma and Sample Size -  9m 29s - 9.64 MB
12. Role of Sample Size in Hypothesis Testing - Part A -  5m 46s - 5.85 MB
13. Role of Sample Size in Hypothesis Testing - Part B -  7m 41s - 7.81 MB
Application Videos
14. Example Calculation of Sample Size - Part A -  6m 34s - 3.04 MB
15. Example Calculation of Sample Size - Part B -  3m 38s - 1.84 MB
16. Calculation of Sample Size for Discrete Data - Part A -  3m 57s - 4.10 MB
17. Calculation of Sample Size for Discrete Data - Part B -  2m 49s - 2.81 MB
18. Power of a Test and Random Sampling - Part A -  4m 48s - 4.35 MB
19. Power of a Test and Random Sampling - Part B -  1m 41s - 1.90 MB
20. Power of a Test and Random Sampling - Part C -  3m 03s - 3.59 MB
Supporting Media
Summary Slides: Sample Size

Confidence Intervals

Mean Distribution - Comprehend and characterize the distribution of sampling averages

Mean Interval - Compute and interpret the confidence interval of a mean

Variance Distribution - Comprehend and characterize the distribution of sampling variances

Variance Interval - Compute and interpret the confidence interval of a variance

Proportion Distribution - Comprehend and characterize the distribution of sampling proportions

Proportion Interval - Compute and interpret the confidence interval of a proportion

Frequency Interval - Describe how frequency of defects is related to confidence intervals

Control Methods

Statistical Control - Explain the meaning of statistical control in terms of random variation

Control Logic - Explain the logic that underpins the application of a control chart

Control Limits - Reconcile the difference between specification limits and control limits

Chart Selection - Explain how to rationally select a control chart

Chart Interpretation - Interpret an SPC chart in terms of its control limits

Zone Testing - Explain the concept of zone tests and their application to SPC charts

Variables Chart - Characterize the role and purpose of a variables chart

Attribute Chart - Characterize the role and purpose of an attribute chart

Individuals Chart - Construct and interpret an individuals control chart

IMR Chart - Construct and interpret an individual moving range control chart

Xbar Chart - Construct and interpret a control chart for subgroup averages

Range Chart - Construct and interpret a control chart for subgroup ranges

Proportion Chart - Construct and interpret a control chart for sampling proportions

Defect Chart - Construct and interpret a control chart for defect occurrences

Other Charts - Describe several other types of control charts used in Six Sigma work

Capability Studies - Explain the role of capability studies when making process improvements

Control Simulation - Apply common SPC methods to the process simulator

Measurement Analysis

Measurement Uncertainty - Understand the concept of measurement uncertainty

Measurement Components - Describe the components of measurement error and their consequential impact

Measurement Studies - Explain how a measurement systems analysis is designed and conducted