  Operational disruptions can significantly impact companies performance. Ford,
with its 37 plants globally, uses 17 billion parts annually to manufacture six
million cars and trucks. With up to ten tiers of suppliers between the company
and raw materials, any extended disruption in this supply chain can cause
substantial financial losses. Therefore, the ability to forecast and identify
such disruptions early is crucial for maintaining seamless operations. In this
study, we demonstrate how we construct a dataset consisting of many
multivariate time series to forecast first-tier supply chain disruptions,
utilizing features related to capacity, inventory, utilization, and processing,
as outlined in the classical Factory Physics framework. This dataset is
technically challenging due to its vast scale of over five hundred thousand
time series. Furthermore, these time series, while exhibiting certain
similarities, also display heterogeneity within specific subgroups. To address
these challenges, we propose a novel methodology that integrates an enhanced
Attention Sequence to Sequence Deep Learning architecture, using Neural Network
Embeddings to model group effects, with a Survival Analysis model. This model
is designed to learn intricate heterogeneous data patterns related to
operational disruptions. Our model has demonstrated a strong performance,
achieving 0.85 precision and 0.8 recall during the Quality Assurance (QA) phase
across Ford's five North American plants. Additionally, to address the common
criticism of Machine Learning models as black boxes, we show how the SHAP
framework can be used to generate feature importance from the model
predictions. It offers valuable insights that can lead to actionable strategies
and highlights the potential of advanced machine learning for managing and
mitigating supply chain risks in the automotive industry.
