Decision Making with Statistical Process Control (SPC)

Introduction:

In the global economy, quality and productivity are essential for success, as customers increasingly demand assurance that products and services meet the highest standards.

Decision Making with Statistical Process Control (SPC) is a key component of quality management, providing objective data to monitor and control manufacturing processes and ensure final product quality.

Managers involved in product or service development need a solid understanding of statistical tools used in SPC analysis to effectively oversee process outputs that impact quality.

Additionally, strong quantitative reasoning skills are crucial for correctly interpreting SPC findings and evaluating interpretations provided by others.

Objectives:

This Decision Making with Statistical Process Control (SPC) Course aims to equip participants with the following competencies:

• Understand the concept of variation in work processes, including sources and methods of measurement.
• Explain the importance of data quality in the successful application of SPC tools and their analysis.
• Reaffirm the significance of normal distribution in relation to SPC approaches.
• Differentiate between various control charts used in different SPC applications and processes.
• Effectively use statistical tools for analyzing quality control data.
• Translate statistical results into actionable management strategies.
• Comprehend the concept, objectives, and measurement techniques of process capability.

Training Methodology:

• Interactive lectures
• Case studies
• Data analysis exercises
• Group discussions
• Scenario-based learning
• Simulated control charts
• Real-life problem-solving activities
• Peer feedback sessions
• Group and individual presentations

Course Outline:

Unit 1: Setting the Scene for Statistical Process Control (SPC)

• Overview of SPC and its role in quality control
• Fundamentals of process analysis and the relationship between quality and variation
• SPC in the Six Sigma framework
• The role of statistics and data analysis in quality management
• Data categorization (Variable/Attribute) and the importance of data quality
• Introduction to basic statistical concepts and SPC tools
• Summary tables, graphs, and data distribution analysis
• Frequency distributions, histograms, and Pareto charts
• Descriptive statistical measures (central location, quartiles, percentiles, dispersion, skewness)
• Hands-on Excel analysis using essential statistical tools on quality control datasets

Unit 2: Review of SPC Tools

• Frameworks and terminologies for SPC tools
• Sub-grouping and control charts for continuous data measures
• Types of control charts (X-bar, R chart, Sigma chart, CUSUM chart, EWMA chart)
• Excel analysis of sample datasets for each control chart type

Unit 3: Managing and Communicating Requirements

• Handling conflicts with key stakeholders and resolving them effectively
• Maintaining project viability and stakeholder approval during requirement changes
• Ensuring the solution scope aligns with project goals and stakeholder expectations
• Communicating requirements clearly to ensure proper implementation
• Continuously improving business analysis techniques based on project learnings

Unit 4: Validity Tests and Process Capability

• Validity tests and conditions for SPC analysis
• Control chart assumptions (regular pdf; independence)
• Curve fitting and hypothesis testing for normal distribution
• Process capability analysis, including process capability index (Cp) and process performance index (Cpk)
• Microsoft Excel’s application in analyzing sample datasets for validity tests and process capability measurements

Unit 5: Advanced Statistical Tools in SPC

• Statistical methods for inferring process behavior
• Sampling, sampling distributions, and confidence limits
• Hypothesis testing and analysis of variance (ANOVA)
• Regression analysis, scatter plots, and correlations
• In-depth SPC tools case studies with Excel analysis of sample datasets
• Integration of SPC into work domains