Learning Objectives
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# Learning Objectives - Understand big data characteristics (4 Vs) - Apply analytics to manufacturing data - Evaluate data-driven decision making - Statistics fundamentals - Week 2: IoT data sources ## 1. Big Data in Manufacturing **Volume:** Terabytes of sensor data from thousands of devices daily **Velocity:** Rea...
Learning Objectives
- Understand big data characteristics (4 Vs)
- Apply analytics to manufacturing data
- Evaluate data-driven decision making
- Statistics fundamentals
- Week 2: IoT data sources
1. Big Data in Manufacturing
Volume: Terabytes of sensor data from thousands of devices daily Velocity: Real-time streaming data (millisecond intervals) Variety: Structured (sensor readings), semi-structured (logs), unstructured (images, video) Veracity: Data quality, noise, missing values, uncertainty
2. Analytics Maturity Model
Descriptive: What happened? Dashboards, reports, KPIs (OEE, throughput, defect rate) Diagnostic: Why did it happen? Root cause analysis, drill-down, correlation Predictive: What will happen? ML models for forecasting, predictive maintenance Prescriptive: What should we do? Optimization, simulation, recommendation
3. Industrial Analytics Applications
- Predictive Maintenance: Predict equipment failure using vibration, temperature, acoustic data
- Quality Prediction: Predict defect rates from process parameters
- Demand Forecasting: Predict customer demand for production planning
- Energy Optimization: Analyze energy consumption patterns to reduce costs
- Supply Chain Optimization: Optimize inventory levels and logistics
Q1: What are the 4 Vs of Big Data?Volume (amount), Velocity (speed), Variety (types), Veracity (quality). Sometimes Value is a 5th V. Q2: How does predictive maintenance work?Sensors monitor equipment (vibration, temperature). ML models learn normal behavior patterns. Anomalies trigger alerts before failure occurs, enabling proactive maintenance scheduling. Q3: Compare descriptive and prescriptive analytics.Descriptive: reports what happened (e.g., last month's defect rate was 2%). Prescriptive: recommends actions (e.g., adjust temperature to 180C to reduce defects to 1.5%). Q4: What is OEE (Overall Equipment Effectiveness)?OEE = Availability x Performance x Quality. Measures how effectively manufacturing equipment is utilized. World-class OEE is >85%. Q5: What is root cause analysis in manufacturing?Systematic approach to identify fundamental cause of problems. Methods: 5 Whys, fishbone diagram, fault tree analysis, data-driven correlation. Join Discord PreviousIoT & Smart ManufacturingNextAI & Machine Learning in Industry