Machine Learning Integration for Six Sigma and AI with Python

1. Introduction to Machine Learning in Six Sigma

Machine Learning (ML) can significantly enhance Six Sigma methodologies by providing powerful tools for data analysis, prediction, and optimization. Integrating ML into Six Sigma processes can lead to more accurate insights, better decision-making, and improved process control.

2. Importing Required Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.impute import SimpleImputer
    

3. Loading and Preparing Data

# Load sample manufacturing data (you would replace this with your own data)
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00567/qualitative_bankruptcy.csv"
df = pd.read_csv(url)

# Encode categorical variables
df = pd.get_dummies(df, drop_first=True)

# Split features and target
X = df.drop('Bankrupt?_Y', axis=1)
y = df['Bankrupt?_Y']

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

print(df.head())
print(f"\nShape of training data: {X_train.shape}")
print(f"Shape of test data: {X_test.shape}")
    

4. Implementing Logistic Regression for Defect Prediction

def train_and_evaluate_model(model, X_train, X_test, y_train, y_test):
    # Train the model
    model.fit(X_train, y_train)
    
    # Make predictions
    y_pred = model.predict(X_test)
    
    # Evaluate the model
    accuracy = accuracy_score(y_test, y_pred)
    conf_matrix = confusion_matrix(y_test, y_pred)
    class_report = classification_report(y_test, y_pred)
    
    print(f"Accuracy: {accuracy:.2f}")
    print("\nConfusion Matrix:")
    print(conf_matrix)
    print("\nClassification Report:")
    print(class_report)

# Logistic Regression
log_reg = LogisticRegression(random_state=42)
print("Logistic Regression Results:")
train_and_evaluate_model(log_reg, X_train_scaled, X_test_scaled, y_train, y_test)
    

5. Implementing Decision Tree for Root Cause Analysis

# Decision Tree
dt = DecisionTreeClassifier(random_state=42)
print("\nDecision Tree Results:")
train_and_evaluate_model(dt, X_train, X_test, y_train, y_test)

# Visualize feature importance
feature_importance = pd.DataFrame({'feature': X.columns, 'importance': dt.feature_importances_})
feature_importance = feature_importance.sort_values('importance', ascending=False)

plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance in Decision Tree')
plt.tight_layout()
plt.show()
    

6. Implementing Random Forest for Process Optimization

# Random Forest
rf = RandomForestClassifier(random_state=42)
print("\nRandom Forest Results:")
train_and_evaluate_model(rf, X_train, X_test, y_train, y_test)

# Partial dependence plot
from sklearn.inspection import PartialDependenceDisplay

features = [0, 1]  # Adjust based on your most important features
PartialDependenceDisplay.from_estimator(rf, X_test, features)
plt.tight_layout()
plt.show()
    

7. Handling Missing Data in Six Sigma Projects

# Simulate missing data
X_with_missing = X.copy()
X_with_missing.iloc[np.random.choice(X.index, 100), 0] = np.nan

# Impute missing values
imputer = SimpleImputer(strategy='mean')
X_imputed = pd.DataFrame(imputer.fit_transform(X_with_missing), columns=X.columns)

print("Original data:")
print(X.head())
print("\nData with missing values:")
print(X_with_missing.head())
print("\nImputed data:")
print(X_imputed.head())
    

8. Anomaly Detection for Quality Control

from sklearn.ensemble import IsolationForest

# Simulate process data
np.random.seed(42)
process_data = np.random.normal(loc=10, scale=1, size=1000)
process_data[np.random.choice(1000, 20)] += np.random.uniform(5, 10, 20)  # Add some anomalies

# Reshape data for scikit-learn
process_data = process_data.reshape(-1, 1)

# Train Isolation Forest
iso_forest = IsolationForest(contamination=0.01, random_state=42)
predictions = iso_forest.fit_predict(process_data)

# Visualize results
plt.figure(figsize=(12, 6))
plt.scatter(range(len(process_data)), process_data, c=predictions, cmap='viridis')
plt.title('Anomaly Detection in Process Data')
plt.xlabel('Sample')
plt.ylabel('Measurement')
plt.colorbar(label='Prediction (-1: anomaly, 1: normal)')
plt.tight_layout()
plt.show()
    

9. Integration of Machine Learning in DMAIC

Machine Learning can be integrated into each phase of the DMAIC (Define, Measure, Analyze, Improve, Control) process:

• Define: Use NLP techniques to analyze customer feedback and identify critical-to-quality characteristics.
• Measure: Implement automated data collection and cleaning pipelines, use anomaly detection for data validation.
• Analyze: Apply various ML algorithms for root cause analysis, feature importance, and correlation analysis.
• Improve: Use optimization algorithms and simulation models to find optimal process parameters.
• Control: Implement ML-based process monitoring and predictive maintenance systems.

10. Challenges and Considerations

When integrating Machine Learning into Six Sigma projects, consider the following:

• Data Quality: Ensure data is accurate, complete, and representative of the process.
• Model Interpretability: Use techniques like SHAP values to explain model predictions in business terms.
• Continuous Learning: Implement systems for model retraining and performance monitoring.
• Cross-functional Collaboration: Foster collaboration between data scientists and process experts.
• Ethical Considerations: Be aware of potential biases in data and models, and their implications on decision-making.

Conclusion

Integrating Machine Learning into Six Sigma methodologies can significantly enhance the effectiveness of quality improvement initiatives. By leveraging advanced analytics and predictive modeling, organizations can gain deeper insights into their processes, make more accurate predictions, and optimize their operations more effectively. However, it's crucial to approach this integration thoughtfully, ensuring that the fundamentals of Six Sigma are maintained while harnessing the power of AI and Machine Learning.