Unit 983: Supply Chain Analytics

1. Introduction to Supply Chain Analytics

• Definition and scope of supply chain analytics
• Importance in optimizing supply chain operations
• Role in decision-making processes
• Examples of analytics-driven improvements in supply chains

2. Data Collection in Supply Chain Management

• Sources of supply chain data
  • Transactional systems (ERP, WMS, TMS)
  • Supplier and customer data
  • IoT and sensor data
  • External data (market trends, weather, social media)
• Importance of accurate and timely data collection
• Methods for data collection
  • Automated data capture
  • Surveys and manual inputs
  • Integration of heterogeneous data sources
• Challenges in data collection
  • Data quality issues
  • Data silos and fragmentation
  • Privacy and security concerns

3. Data Cleaning and Preprocessing for Supply Chain Analytics

• Overview of data preparation
• Data normalization techniques
• Detection and treatment of outliers
• Handling missing and incomplete data
• Data transformation and feature engineering
• Tools and software for data preprocessing

4. Descriptive Analytics in Supply Chain Management

• Purpose and benefits of descriptive analytics
• Data visualization techniques
  • Charts, graphs, heat maps
• Key Performance Indicators (KPIs) in supply chains
  • Inventory turnover, order accuracy, lead times
• Dashboards for supply chain monitoring
• Case studies demonstrating descriptive analytics

5. Predictive Analytics for Demand Forecasting

• Role of predictive analytics in demand forecasting
• Time series analysis methods
  • Moving averages, exponential smoothing, ARIMA
• Regression modeling approaches
• Machine learning algorithms for forecasting
  • Random forests, neural networks, support vector machines
• Model evaluation and validation techniques
• Applications and challenges in demand forecasting

6. Optimization Techniques in Supply Chain Analytics

• Introduction to optimization in supply chains
• Linear programming fundamentals
• Network optimization models
• Simulation modeling for scenario analysis
• Use cases: cost reduction, route optimization, resource allocation
• Software tools supporting optimization

7. Inventory Management and Supply Chain Analytics

• Analytics-driven inventory optimization
• Reducing stockouts and overstocks
• Improving order fulfillment rates
• Safety stock calculations using analytics
• Demand-driven inventory policies
• Case examples

8. Supplier Relationship Management using Analytics

• Analytics for supplier selection and evaluation
• Performance measurement metrics
• Risk assessment and mitigation using analytics
• Strategic decision-making support
• Collaborative analytics with suppliers
• Real-world applications

9. Real-time Analytics and Supply Chain Visibility

• Importance of real-time data and analytics
• Technologies enabling real-time tracking (RFID, GPS, IoT)
• Monitoring inventory levels and goods in transit
• Responding to supply chain disruptions promptly
• Benefits of enhanced supply chain visibility
• Implementation challenges and best practices

Summary and Integration

• Connecting analytics techniques across the supply chain
• Future trends in supply chain analytics
• Ethical considerations and data governance