Identifying & Prioritizing Supply Chain Risks to Improve Readiness
Customer Challenge
The Air Force was seeking new methods for optimizing the supply chain—ensuring the right parts, at the right time, while keeping inventory costs down.
Innovative Solution
Illumination Works data scientists implemented the Markov Chain Monte Carlo method to develop an innovative model to predict outages with 2.5 times greater accuracy and in far less time—predictions in hours versus days.
Benefits/Outcomes
- Predictive model to rank parts based on supply chain risk
- Probable risk severity and actionable information
- Outage prediction with 2.5 times greater accuracy in less time—hours versus days
- Sorted and visualized predictions to optimize work loads
- Automated prediction process
Toolbox
- Python platform
- Markov Chain Monte Carlo models
- Bayesian methods yielded predictions
