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Decision Making using Statistical Process Control SPC 

Introduction

Decision Making using Statistical Process Control SPC 

Course Objectives

Upon completing this statistical process control certification course, participants will be able to:

• Grasp the concept, sources, and methods of measuring variation in work processes.
• Recognize the significance of data quality in SPC and SPC data analysis.
• Reassert the relevance of a normal distribution in applying SPC statistical process control techniques.
• Explore various control charts tailored for SPC programs and understand their use in different SPC processes.
• Utilize statistical tools to analyze quality control data effectively.
• Translate statistical outcomes into substantial management initiatives.
• Comprehend the concept, objective, and measurement of process capability.

Targeted Competencies

• Leveraging data analytics in management.
• Importance and use of data in SPC data analytics.
• Implementation of data analytical methodologies through practical examples.
• Emphasis on management's interpretation of statistical evidence.
• Assimilation of statistical thought into day-to-day operations.

Course Content

Unit 1: Setting the Statistical Scene for SPC

• Overview of SPC and its significance in quality control.
• Fundamentals of Process Analysis (Quality-Variation Relationship).
• SPC in the Six Sigma framework.
• Roles of statistics and data analysis in quality management.
• Data categorization (Variable/Attribute) and the necessity of Data Quality.
• Introduction of basic statistical concepts and tools of relevance to SPC.
• Summary tables and graphs.
• Examine the distribution of data using summary tables and graphs.
• Frequency distributions and histograms.
• One-way and two-way pivot tables; breakdown tables.
• Simple, multiple, and stacked bar charts.
• "Pareto" charts.
• Descriptive statistical measures.
• Central location, quartiles, percentiles, dispersion, and skewness.
• Box plots and categorized box plots.
• The average probability distribution (z statistics).
• Hands-on Excel analysis using essential statistical tools on QC datasets.

Unit 2: Review of SPC Tools

• Framework of SPC tools (terms and definitions).
• Sub-group formation.
• Control charts (types, data requirement, importance, methodology, benefits/advantages, interpretation, uses, and applications).
• Each control chart will be examined under the following headings: purpose/uses/data/methodology/computation/interpretation/application.
• Variable control charts for continuous data measures.
• Subgroups (samples of data) (review purposes).
• X(bar) chart (Shewhart sample mean) (process location).
• R chart (Shewhart sample range) (process variability/stability).
• Sigma chart (standard deviation plot) (process variability/stability).
• CUSUM chart (cumulative sum) (location trend tracking).
• EW moving average charts (location trend tracking).
• Excel analysis of sample datasets for each Control Chart type.

Unit 3: Review of SPC Tools (continued)

• Control Charts for individual data.
• X chart (Shewhart individual "x's").
• IX/MR charts (individual "x's" and moving range) (variability tracking).
• Attribute control charts for discrete/countable data measures.
• P chart (sample proportion defective) (based on a Bernoulli process).
• NP chart (sample number of defectives) (i.e., Bernoulli process).
• C chart (sample number of defectives per sub-group) (Poisson process).
• U chart (orc (bar) chart) (sample number of defects per unit)
• Excel analysis of sample datasets for each control chart type.

Unit 4: Validity Tests and Process Capability

• Tests and conditions of valid SPC analysis.
• Control chart assumptions (regular pdf; independence).
• Curve fitting (normal distribution) (K-S hypothesis test for Normality).
• Run chart and test rules.
• Process capability analysis.
• Overview of process capability analysis (Evans/Olson p155/156).
• Process capability index (Cp).
• Process performance index (Cpk).
• Using Excel to analyze sample datasets for validity tests and process capability.

Unit 5: More Advanced Statistical Tools in SPC

• Statistical methods to make inferences about process behavior
• Sampling and sampling distributions.
• Confidence limits – use and interpretation.
• Hypothesis tests (t-test: two-sample test of means) – use and interpretation.
• Analysis of variance (ANOVA) – use and interpretation.
• Regression analysis (scatter plots; correlations).
• Excel analysis of sample datasets to illustrate each of the Statistical Tools in SPC.
• "How to integrate SPC into the work domain."