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Decision Making using Statistical Process Control SPC 

Dates Venues Register
03/08/2025 - 07/08/2025 LONDON

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."

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