Big Data Analytics

Understanding big data in manufacturing context:

The Four Vs:

Volume:
Manufacturing generates massive data:
- Sensors sampling 1000s times/second
- Quality measurements every part
- Machine logs continuous
- Video/image data

Velocity:
Data arrives rapidly:
- Real-time streaming
- High-frequency updates
- Immediate processing needs
- Low-latency requirements

Variety:
Multiple data types:
- Structured (databases, transactions)
- Semi-structured (logs, XML/JSON)
- Unstructured (images, video, documents)
- Time-series (sensor data)

Veracity:
Data quality challenges:
- Sensor noise and errors
- Missing data
- Inconsistent formats
- Accuracy validation

Manufacturing Data Sources:

Machine Data:
- PLCs and controllers
- Sensors and IoT devices
- Equipment historians
- CNC programs

Process Data:
- Production parameters
- Recipe/batch records
- Quality measurements
- Environmental conditions

Business Data:
- ERP transactions
- Work orders
- Inventory records
- Supply chain data

External Data:
- Supplier quality data
- Customer feedback
- Market/demand signals
- Weather (if relevant)

Value Proposition:

Descriptive Analytics:
What happened?
- OEE dashboards
- Quality reports
- Downtime analysis
- Production history

Diagnostic Analytics:
Why did it happen?
- Root cause analysis
- Correlation discovery
- Trend analysis
- Comparative analysis

Predictive Analytics:
What will happen?
- Equipment failure prediction
- Quality prediction
- Demand forecasting
- Yield prediction

Prescriptive Analytics:
What should we do?
- Optimization recommendations
- Automated decisions
- Process adjustments
- Resource allocation

Technology platforms for manufacturing analytics:

Data Storage:

Data Lakes:
Store raw data at scale:
- Amazon S3, Azure Data Lake
- Hadoop HDFS
- Cost-effective storage
- Flexible schema

Data Warehouses:
Structured analytical storage:
- Snowflake, Databricks
- Amazon Redshift, Azure Synapse
- Optimized for queries
- Cloud-native options

Time-Series Databases:
Sensor and operational data:
- InfluxDB, TimescaleDB
- OSIsoft PI (AVEVA)
- High-throughput ingestion
- Time-based queries

Data Processing:

Batch Processing:
Process accumulated data:
- Apache Spark
- Scheduled jobs
- Historical analysis
- Large-scale transformations

Stream Processing:
Process data in motion:
- Apache Kafka
- Apache Flink
- Real-time analytics
- Event-driven actions

Cloud Platforms:
Managed services:
- AWS Analytics services
- Azure Synapse/Databricks
- Google BigQuery
- Reduced infrastructure burden

Analytics Tools:

Business Intelligence:
- Tableau, Power BI
- Qlik
- Dashboard and reporting
- Self-service analytics

Advanced Analytics:
- Python (pandas, scikit-learn)
- R for statistical analysis
- Jupyter notebooks
- Data science platforms

Machine Learning:
- TensorFlow, PyTorch
- Azure ML, AWS SageMaker
- AutoML tools
- Model deployment

Integration:

Data Ingestion:
- Apache Kafka
- Azure Event Hubs
- AWS Kinesis
- Edge gateways

Orchestration:
- Apache Airflow
- Azure Data Factory
- Prefect
- Workflow management

Practical applications of big data:

Predictive Maintenance:

Approach:
- Collect equipment sensor data
- Build failure prediction models
- Alert before failure occurs
- Schedule proactive maintenance

Data Sources:
- Vibration sensors
- Temperature readings
- Current/power consumption
- Operating parameters

Benefits:
- Reduce unplanned downtime
- Optimize maintenance schedules
- Extend equipment life
- Lower maintenance costs

Quality Analytics:

Process Quality:
- Predict defects from process parameters
- Identify optimal parameter windows
- Reduce scrap and rework
- First-pass yield improvement

Inspection Analytics:
- Image/vision data analysis
- Automated defect detection
- Pattern recognition
- Classification models

Root Cause:
- Correlate defects to conditions
- Identify hidden relationships
- Accelerate problem-solving
- Prevent recurrence

Production Optimization:

Yield Optimization:
- Model yield vs. parameters
- Identify improvement opportunities
- Golden batch analysis
- Parameter optimization

Throughput:
- Bottleneck identification
- Scheduling optimization
- Resource utilization
- Cycle time reduction

Energy Efficiency:
- Energy consumption patterns
- Optimization opportunities
- Demand response
- Sustainability goals

Supply Chain Analytics:

Demand Forecasting:
- Machine learning predictions
- Seasonal patterns
- External factor integration
- Inventory optimization

Supplier Analysis:
- Quality performance
- Delivery reliability
- Risk assessment
- Relationship optimization

Building a career in manufacturing analytics:

Data Analyst:
Analyze manufacturing data:
- SQL and BI tools
- Reporting development
- Pattern identification
- $55,000-$85,000

Data Scientist:
Build predictive models:
- Machine learning
- Statistical analysis
- Model development
- $90,000-$140,000

Data Engineer:
Build data infrastructure:
- Pipeline development
- Platform engineering
- Data architecture
- $85,000-$130,000

Analytics Manager:
Lead analytics initiatives:
- Team leadership
- Project management
- Strategy development
- $110,000-$160,000

Manufacturing-Specific:

Manufacturing Data Scientist:
ML for manufacturing problems:
- Predictive maintenance
- Quality prediction
- Process optimization
- $95,000-$145,000

Digital Manufacturing Specialist:
Bridge IT and operations:
- Analytics implementation
- Use case development
- Change management
- $80,000-$120,000

Skills Development:

Technical:
- Python programming
- SQL proficiency
- Statistics and ML fundamentals
- Cloud platforms

Domain:
- Manufacturing processes
- Quality principles
- Operations management
- Industry-specific knowledge

Tools:
- Power BI/Tableau
- Jupyter/Python
- Cloud analytics services
- Machine learning platforms

Learning Path:
1. SQL and basic analytics
2. Python programming
3. Statistics and ML fundamentals
4. Domain-specific applications
5. Advanced ML/AI

Certifications:
- Cloud certifications (AWS, Azure, GCP)
- Microsoft Power BI
- Data science certificates
- Domain certifications

Analytics skills combined with manufacturing knowledge create unique career opportunities.