supply-chain-data-science

A Simple Batch Processing library in Go

In this study, we aimed to detect fraudulent activities in the supply chain through the use of neural networks. The study focused on building two machine learning models using the MLPClassifier algorithm from the scikit-learn library and a custom neural network using the Keras library in Python.

Documentation and tools for the API of opensupplyhub.org

Enhancing Spatiotemporal Supply Chain Forecasting Using Virtual Node-Augmented Graph Diffusion for Improved Fuel Efficiency

Exploitation of material consolidation trade-offs in multi-tier complex supply networks

A Jupyter Notebook containing data and visualizations for automatic planning in Satisfactory. This is a Work In Progress.

Maximize profits and losses by minimizing risks arising from global supply chain volatility in imported raw materials.

Predicting permanent drug discontinuation risk using public FDA data

SHSID x HKU 2026 AI Competition Second Prize Solution

Synthetic supply-chain shock cascade testbed comparing GNNs to direct-exposure baselines.

The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. As a result of your analysis, you should be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them.

Passionate and well-rounded senior leader working closely with data scientists and forecasters to predict the future of the cloud.

Supplier Assembly Dashboard