Predictive machine learning for supply chain data analytics is reported as a significant area of investigation due to the rising popularity of the AI paradigm in the industry - however, there is a distinct lack of case studies that showcase its application from a practical point of view. In this project, we explore how machine learning can be used in predicting first tier supply chain disruptions using historical performance data. Our methodology involves three steps: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. Second, we develop performance metrics in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our current results indicate that adding engineered features in the data, namely agility outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. We are currently further developing our method to investigate Bayesian approaches for predicting uncertainty associated with predictions.
Funded by: Boeing
Researchers: Stephen Mak, David Ratiney