Summary of What If? Causal Machine Learning in Supply Chain Risk Management, by Mateusz Wyrembek et al.
What if? Causal Machine Learning in Supply Chain Risk Management
by Mateusz Wyrembek, George Baryannis, Alexandra Brintrup
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to develop machine learning models that can make optimal interventions in supply chain management by identifying causality rather than just correlations. They propose the use of causal machine learning to build risk intervention models and demonstrate its effectiveness with a case study in maritime engineering. The findings show that causal machine learning improves decision-making by allowing for “what-if” scenario planning, enabling supply chain professionals to predict risks and plan interventions to minimize them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special kinds of computer programs called machine learning models to help make good decisions in supply chains. Supply chains are like a big team effort between many different companies that need to work together to get things made and delivered. The problem is that most of these models just look at what happened, not why it happened. This paper shows how we can use special kinds of machine learning models called causal machine learning to figure out why things happen and make better decisions. They even show how this works in a real-life example with shipping companies. By using these new kinds of computer programs, we can plan for different scenarios and make better choices to avoid problems. |
Keywords
» Artificial intelligence » Machine learning
