  Time series forecasting is an important task in many fields ranging from
supply chain management to weather forecasting. Recently, Transformer neural
network architectures have shown promising results in forecasting on common
time series benchmark datasets. However, application to supply chain demand
forecasting, which can have challenging characteristics such as sparsity and
cross-series effects, has been limited.
  In this work, we explore the application of Transformer-based models to
supply chain demand forecasting. In particular, we develop a new
Transformer-based forecasting approach using a shared, multi-task per-time
series network with an initial component applying attention across time series,
to capture interactions and help address sparsity. We provide a case study
applying our approach to successfully improve demand prediction for a medical
device manufacturing company. To further validate our approach, we also apply
it to public demand forecasting datasets as well and demonstrate competitive to
superior performance compared to a variety of baseline and state-of-the-art
forecast methods across the private and public datasets.
