2025-04-23 –, Dynamicum
This talk introduces supplyseer, an open-source Python library that brings advanced analytics to Supply Chain and Logistics. By combining time series embedding techniques, stochastic process modeling, and geopolitical risk analysis, supplyseer helps organizations make data-driven decisions in an increasingly complex global supply chain landscape. The library implements novel approaches like Takens embedding for demand forecasting, Hawkes processes for modeling supply chain events, and Bayesian methods for inventory optimization. Through practical examples and real-world use cases, we'll explore how these mathematical concepts translate into actionable insights for supply chain practitioners.
Supplyseer bridges the gap between theoretical supply chain analytics and practical implementation by providing a pythonic interface to advanced mathematical concepts. This talk will walk through the library's core components and demonstrate how they solve real-world supply chain challenges.
Outline:
-
Introduction to Modern Supply Chain Analytics
- The need for sophisticated analytics in today's complex supply chains
- Why traditional methods fall short
- The role of probabilistic modeling and topological analysis -
Core Mathematical Foundations
- Time series embedding techniques using Takens' theorem
- Stochastic process modeling for demand forecasting
- Bayesian approaches to Economic Order Quantity (EOQ)
- Point process modeling with Hawkes processes
- Network analysis for supply chain risk assessment -
Library Architecture and Design Philosophy
- Object-oriented design for supply chain analytics
- Integration of multiple analytical approaches
- Extensible architecture for custom analytics
- Performance considerations and optimizations -
Key Features Deep Dive
a) Demand Forecasting Module
- Stochastic demand process simulation
- Time-delay embedding for pattern recognition
- Mixture density networks for uncertainty quantification
b) Risk Analysis Tools
- Geopolitical risk assessment
- Supply chain network visualization
- Real-time monitoring and alerting
- Trade restriction impact analysis
c) Inventory Optimization
- Bayesian EOQ implementation
- Multi-echelon inventory optimization
- Stockout probability calculation
- Vector field analysis for inventory dynamics
-
Practical Applications
- Route optimization with geopolitical risk consideration
- You and your suppliers play cooperative games: game-theoretic Supply Chain
- Supply Chain Digital Twins
- Real-time risk monitoring and mitigation -
Integration with Data Science Ecosystem
- Compatibility with pandas and polars
- Integration with scikit-learn pipeline
- Visualization with matplotlib and seaborn
- Performance optimization with numpy -
Future Directions
- Planned features and enhancements
- Community contribution opportunities
- Integration with other supply chain tools
- Research directions in supply chain analytics -
Interactive Demonstrations
- Live coding examples
- Real-world data analysis
- Visualization of supply chain dynamics
- Risk assessment workflows
The talk will include code examples and practical demonstrations, showing how to:
- Implement stochastic demand forecasting
- Analyze supply chain risks using network analysis
- Optimize inventory levels using Bayesian methods
- Visualize supply chain dynamics using vector fields
- Monitor and assess geopolitical risks
Target Audience:
This talk is aimed at data scientists, supply chain analysts, and Python developers interested in applying advanced analytics to supply chain problems. Attendees should have intermediate Python knowledge and basic familiarity with data science libraries like pandas and numpy.
Prerequisites:
- Python programming experience
- Basic understanding of supply chain concepts
- Familiarity with pandas and numpy
- Basic knowledge of probability and statistics
Takeaways:
Attendees will learn:
- How to implement advanced supply chain analytics in Python
- Practical applications of mathematical concepts in supply chain
- Best practices for supply chain data analysis
- Techniques for visualizing and monitoring supply chain dynamics
- Methods for quantifying and managing supply chain risks
All code examples and demonstrations will be available in a GitHub repository, allowing attendees to experiment with the concepts presented and apply them to their own supply chain challenges.
Intermediate
Expected audience expertise: Python:Intermediate
Public link to supporting material, e.g. videos, Github, etc.:
I am a Machine Learning Engineer at H&M Group, former Data Scientist at Lidl Sweden, as a professional I am designing Machine Learning services, extracting insights and arranging meaningful stories for my clients by conducting high-quality modeling, engineering, data mining and analytics.
I have a Bachelor degree in Statistics and Probability theory from Uppsala University of Sweden. Because I am a Statistician at core I have good experience with Data Sciencr, Python, R, time series modeling, simulations, machine learning algorithms, SQL, Excel, Spark and database technologies, as well as good communication skills.
You’ll find two comprehensive Python libraries I have open-sourced. One is based on an emerging modern statistical hypothesis testing framework using e-values and martingales based on game-theoretic statistics. The other is for computational Supply Chain and Logistics. The first one is called ’expectation’ and the second one is called ’supplyseer’ and you can find both on my GitHub.