Supply Chain Analytics Learning Repository

An interactive educational repository for learning mathematical concepts through practical supply chain and business problems.

Problem Categories

1. Optimization Problems

• BookWise Distribution: Warehouse location optimization using linear programming

  • Facility location
  • Cost minimization
  • Coverage constraints

• BooBoo Interactive: Bullwhip Effect Analysis and Mitigation

  • Supply chain simulation
  • Demand amplification analysis
  • Mitigation strategies comparison
  • Performance visualization

• BookWise Interactive: Book-to-Bill Ratio Analysis

  • Order pattern analysis
  • Performance metrics
  • Trend visualization
  • Optimization strategies

2. Inventory Management

• Basic Concepts

  • ABC Analysis
  • Economic Order Quantity (EOQ)
  • Safety Stock Calculation
  • Reorder Point Determination

• Advanced Topics

  • Multi-product Systems
  • Service Level Optimization
  • Cost Trade-off Analysis
  • Dynamic Inventory Models

3. Probability and Risk Analysis

• GameStop Pre-order Analysis: Video game pre-order optimization

  • Demand modeling
  • Distribution fitting
  • Inventory optimization
  • Risk assessment
  • Monte Carlo simulation

• Supply Chain Risk Analysis

  • Bullwhip effect quantification
  • Demand uncertainty modeling
  • Service level probability
  • Risk mitigation strategies

4. Statistics and Forecasting

• SunGlass Hut Inventory: Apply normal distribution to retail inventory

  • Service level analysis
  • Z-score calculations
  • Two-stage inventory systems

• QuickPrint Optimization: Process improvement through hypothesis testing

  • T-tests
  • P-value interpretation
  • Performance metrics

Repository Structure

MITx-SC/
├── inventory_management/
│   ├── basic/
│   │   ├── abc_analysis.ipynb
│   │   ├── eoq_analysis.ipynb
│   │   ├── safety_stock_analysis.ipynb
│   │   └── reorder_point_analysis.ipynb
│   └── advanced/
├── optimization/
│   ├── booboo_interactive.ipynb
│   ├── bookwise_interactive.ipynb
│   └── solutions/
├── probability/
│   ├── gamestop_preorder.ipynb
│   └── solutions/
└── statistics/
    ├── sunglass_hut.ipynb
    └── quickprint.ipynb

Learning Path Structure

Each notebook follows a consistent 4-part learning approach:

1. Concept Introduction and Business Context
2. Mathematical Foundation and Theory
3. Implementation and Code Examples
4. Analysis and Interpretation

Features

• Interactive Jupyter notebooks
• Real-world business scenarios
• Step-by-step solution guidance
• Comprehensive visualizations
• Automated solution verification
• Extension exercises

Prerequisites

• Python 3.8+
• Jupyter Notebook environment
• Required libraries:
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • SciPy

Getting Started

1. Clone the repository
2. Install required dependencies
3. Launch Jupyter Notebook
4. Navigate to desired topic
5. Follow guided exercises

Contributing

We welcome contributions! Please see our contributing guidelines for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• MITx Supply Chain Management Program
• Contributors and reviewers
• Open source community

Build Status

Testing

• All problems include automated tests
• Tests run on Python 3.8, 3.9, and 3.10
• Coverage reports available through CodeCov

Testing Framework

The repository includes a comprehensive testing framework in utils/testing/:

Core Test Modules

• test_all.py: Main test runner
• probability_tests.py: Basic probability concept tests
• inventory_tests.py: Inventory management tests
• hypothesis_tests.py: Statistical testing
• regression_tests.py: Regression analysis tests

Optimization Tests

• test_warehouse_location.py: Tests for facility location models
• test_supply_network.py: Tests for network optimization
• test_production_optimization.py: Tests for production planning
• test_beverageco.py: Tests for beverage production
• test_riskshield.py: Tests for risk management
• test_greenchain.py: Tests for sustainable supply chain

Case Study Tests

• test_booboo_interactive.py: Tests for supply chain risk analysis
• test_bookwise_interactive.py: Tests for demand forecasting