DMAIC Copilot - Advanced Statistical Analysis for Lean Six Sigma

How DMAIC Copilot Works

🎯 In Simple Terms:

Upload your data - Any spreadsheet with numbers (delivery times, defects, costs, etc.)
Describe your problem - What's going wrong that you want to fix?
Get instant analysis - Charts, statistics, and step-by-step recommendations
Learn as you go - Explanations tailored to your experience level

📈 Perfect For:

Quality Issues

Delivery Problems

Cost Reduction

Customer Complaints

Process Improvement

Defect Analysis

CSV Setup Guide

📋 Example: "Customer delivery times are too long"

Your Data:
delivery_times.csv with columns:
• delivery_time (hours)
• department
• problem_type

What You Get:
• Charts showing which problems occur most
• Department performance comparison
• Statistical analysis of patterns

Recommendations:
• "Focus on shipping delays (40% of issues)"
• "Warehouse team performs best - study their methods"
• Step-by-step improvement plan

How the DMAIC Analysis Works Behind the Scenes

🔍 The DMAIC Methodology

DMAIC is a proven 5-phase problem-solving framework used by Fortune 500 companies

Define

Clarify your problem statement and project scope

Measure

Validate your data quality and establish baseline performance

Analyze

Find root causes using statistical analysis and visual charts

Improve

Generate prioritized action plans based on data insights

Control

Create monitoring systems to sustain improvements

📊 Statistical Analysis Tools Used

Pareto Analysis What it does: Identifies which problems cause the most trouble by counting how often each one occurs. Based on the famous 80/20 rule - typically 20% of problem types cause 80% of all issues.

How it works: Counts each problem type, sorts them from most to least frequent, draws a chart showing the "vital few" that deserve your attention first.

Example result: "Shipping delays happen 45 times (38% of issues), Missing parts happen 32 times (27% of issues) - fix these two and you solve 65% of your problems"

Control Charts (I-MR Charts) What it does: Tells you if your process is behaving predictably (stable) or if something unusual is happening (out of control). Think of it as a "health check" for your process.

How it works: Plots your measurements over time with "control limits" - like speed limits for your process. Points outside these limits signal special problems.

Example result: "Your delivery times are stable with normal variation" OR "Alert: 3 deliveries this week were unusually slow - investigate what happened"

Group Comparison (t-Test) What it does: Compares different groups (teams, shifts, locations) to find which ones perform better and by how much. Not just guessing - uses statistics to prove the difference is real.

How it works: Takes the average performance of each group, calculates if the difference is big enough to be meaningful (not just random luck).

Example result: "Morning shift averages 22 minutes per order, Evening shift averages 31 minutes. This 9-minute difference is statistically significant - study morning shift methods"

🎯 How Recommendations Are Tailored to Your Problem

Data-Driven Prioritization:

If Pareto shows "Shipping delays = 40% of issues" → Recommendation: "Focus on shipping process first"
If t-test shows "Team A performs 20% better" → Recommendation: "Study Team A's methods"
If control chart shows instability → Recommendation: "Fix special causes before optimization"

Belt-Level Coaching:

Yellow Belt: Simple explanations, basic tools, step-by-step guidance
Green Belt: Statistical concepts, hypothesis testing, project planning
Master Black Belt: Advanced insights, coaching strategies, statistical rigor assessment

🔬 Statistical Foundation & Quality Assurance

Confidence Level: 95% (α = 0.05)
Industry standard for business decisions

Effect Size: Cohen's d calculation
Measures practical significance, not just statistical

Data Quality: Completeness & sample size checks
Warns you if data is insufficient for reliable conclusions

Methodology Transparency: Full documentation
Every recommendation shows the statistical evidence behind it

CSV File Setup Guide for Best Results

📋 Required CSV Structure

Basic Requirements:

First row must contain column headers (no spaces in column names work better)
At least 10 rows of data for meaningful analysis (30+ rows preferred)
Save as .csv format (not Excel .xlsx)
One row per incident/measurement/observation

📊 Column Types You Can Include

Metric Column What: Numbers you want to improve
Examples: delivery_time, cost, defect_count, response_time, satisfaction_score
Format: Just numbers (no $ signs or units in the data)
Used for: Control charts, group comparisons, trends

Category Column What: Types of problems or defects
Examples: problem_type, defect_category, complaint_reason, delay_cause
Format: Text labels like "shipping_delay", "wrong_color", "damaged"
Used for: Pareto analysis to find most common problems

Group Column What: Different teams, locations, shifts to compare
Examples: department, team, shift, location, supplier
Format: Text labels like "warehouse", "customer_service", "morning_shift"
Used for: Compare performance between groups

🏭 Real-World CSV Examples

Customer Service Example

DATE	RESPONSE_TIME	COMPLAINT_TYPE	AGENT
2024-01-01	25	billing_error	team_A
2024-01-01	18	product_defect	team_B
2024-01-02	32	billing_error	team_A
...	...	...	...

Analysis: Response time by team, most common complaint types

Manufacturing Example

BATCH_ID	PRODUCTION_TIME	DEFECT_TYPE	SHIFT
B001	45	surface_scratch	day
B002	52	wrong_size	night
B003	38	surface_scratch	day
...	...	...	...

Analysis: Production time by shift, most common defects

✅ Best Practices for Better Results

Data Quality Tips:

More data = better insights: 30+ rows preferred, 100+ rows ideal
Recent data works best: Last 3-6 months of data
Complete data: Avoid too many blank cells
Consistent naming: Use same labels throughout (e.g., always "team_A", not mixing "Team A" and "team_a")

Common Mistakes to Avoid:

Mixed data types: Don't put text and numbers in same column
Summary rows: Don't include totals or averages in your data
Special characters: Avoid #, %, $ symbols in the data cells
Too few categories: Need at least 3-4 different problem types for good Pareto analysis

📝 Quick Checklist Before Upload

File Format:
☐ Saved as .csv file
☐ Headers in first row
☐ One measurement per row

Data Quality:
☐ At least 10 rows of data
☐ No blank column headers
☐ Consistent category names

Column Setup:
☐ Metric column with numbers
☐ Category column for problems
☐ Group column for comparisons

Problem Statement:
☐ Clear description prepared
☐ Target value identified
☐ Right columns selected

Start Your Analysis

3 Simple Steps

Step 1: What problem are you trying to solve?

Describe Your Problem *

Examples:
• "Customers are complaining about long delivery times"
• "Too many defective products are being returned"
• "Our manufacturing process is too slow"
• "Customer service response times are inconsistent"

Step 2: Upload your data

Your Data File (CSV format) *

Upload your CSV file (max 16MB). Don't have CSV data? Download sample file to try

💡 Tip: Your CSV should have columns with numbers you want to analyze (like delivery_time, defect_count, cost) and categories (like department, product_type, problem_category).

Step 3: Tell us about your data columns (optional - we'll auto-detect if blank)

👍 Good news: You can leave these blank and we'll try to figure them out automatically! Only fill them in if you want to be specific.

Main Number Column

The column with numbers you want to measure (delivery time, cost, defect count, etc.)

Goal/Target (Optional)

What's your ideal target for this number?

Problem Types Column

Column with different problem types or categories to analyze

Groups to Compare

Column with groups to compare (departments, teams, locations, etc.)

Analysis usually takes 10-30 seconds

What You'll Get in Your Analysis Report

Every report includes all of these insights automatically:

Data Summary

Quick overview of your data - what's in it, what's missing, basic stats

Problem Charts

Visual charts showing which problems happen most often (80/20 rule)

Stability Check

Is your process predictable or are there special problems?

Action Plan

Step-by-step recommendations for solving your problem

How to Export CSV from Common Tools

Excel

File → Save As
Choose "CSV (Comma delimited)" format
Click Save

ERP/Database

Look for "Export" or "Download" option
Select CSV format
Include column headers

Google Sheets

File → Download
Select "Comma-separated values (.csv)"
Download will start automatically

Transform Your Data Into Action Plans

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