Day 1: Data Collection and Problem Prioritization

Check Sheet and Data Definition

• Define collection scope, location, timing
• Create non-overlapping, unambiguous categories
• Pilot form before full collection begins
• Design Check Sheet for one case

Pareto and Priority

• Group data into actionable categories
• Rank defects or causes by magnitude
• Interpret cumulative contribution without overclaiming
• Select improvement topic from the chart

Process Maps for Locating Failure

• Map steps decisions and handoffs
• Connect defects with where they appear
• Show exception and rework pathways
• Overlay data on a process map

Day 2: Cause, Variation, and Trend Analysis

Ishikawa and Cause Hypotheses

• Organize possible causes using relevant categories
• Prevent cause diagrams becoming opinion lists
• Select hypotheses requiring additional evidence
• Build a cause-and-effect diagram

Histogram and Distribution

• Interpret center spread and overall shape
• Compare groups beyond simple averages
• Identify extreme values requiring investigation
• Explain distributions from service/process data

Run Chart and Time Trend

• Order measurements according to time
• Identify shifts or sustained trends
• Distinguish patterns from random variation
• Analyze a weekly measurement series

Day 3: Relationships and Combining Tools in Improvement

Scatter Plot and Variable Relationships

• Choose variables with plausible relationships
• Distinguish strong weak or absent association
• Avoid treating correlation as causation proof
• Interpret Scatter Plot for one case

Combining Tools within PDCA

• Use Check Sheets and Pareto appropriately
• Use Ishikawa to guide cause verification
• Track post-change results using Run Chart
• Build logical tool sequence for problem

Presenting the Improvement Story

• Present problem, data, cause, change coherently
• State what evidence does not prove
• Compare result with a before-and-after measure
• Build improvement story board from evidence

• Short data exercises for each core quality tool
• Cases testing tool selection rather than memorized names
• Multiple tools combined within one improvement problem
• A final improvement story presenting evidence change and result