  One key requirement for effective supply chain management is the quality of
its inventory management. Various inventory management methods are typically
employed for different types of products based on their demand patterns,
product attributes, and supply network. In this paper, our goal is to develop
robust demand prediction methods for weather sensitive products at retail
stores. We employ historical datasets from Walmart, whose customers and markets
are often exposed to extreme weather events which can have a huge impact on
sales regarding the affected stores and products. We want to accurately predict
the sales of 111 potentially weather-sensitive products around the time of
major weather events at 45 of Walmart retails locations in the U.S.
Intuitively, we may expect an uptick in the sales of umbrellas before a big
thunderstorm, but it is difficult for replenishment managers to predict the
level of inventory needed to avoid being out-of-stock or overstock during and
after that storm. While they rely on a variety of vendor tools to predict sales
around extreme weather events, they mostly employ a time-consuming process that
lacks a systematic measure of effectiveness. We employ all the methods critical
to any analytics project and start with data exploration. Critical features are
extracted from the raw historical dataset for demand forecasting accuracy and
robustness. In particular, we employ Artificial Neural Network for forecasting
demand for each product sold around the time of major weather events. Finally,
we evaluate our model to evaluate their accuracy and robustness.
