  Demand forecasting of hierarchical components is essential in manufacturing.
However, its discussion in the machine-learning literature has been limited,
and judgemental forecasts remain pervasive in the industry. Demand planners
require easy-to-understand tools capable of delivering state-of-the-art
results. This work presents an industry case of demand forecasting at one of
the largest manufacturers of electronics in the world. It seeks to support
practitioners with five contributions: (1) A benchmark of fourteen demand
forecast methods applied to a relevant data set, (2) A data transformation
technique yielding comparable results with state of the art, (3) An alternative
to ARIMA based on matrix factorization, (4) A model selection technique based
on topological data analysis for time series and (5) A novel data set.
Organizations seeking to up-skill existing personnel and increase forecast
accuracy will find value in this work.
