This chapter covers several common methods for demand prediction. We start by presenting
the basic linear regression method applied to one SKU. We then explain how to properly …

This chapter covers several important pre-processing steps. Before implementing a demand
prediction method, it is crucial to process the raw data in order to extract as much predictive …

This chapter discusses how to leverage clustering techniques in the context of demand
prediction for retail applications. Specifically, our goal is to aggregate the data across …

In the last decade, the curriculum of both business schools and engineering schools has
been significantly revamped. This is partly due to the proliferation of data-rich environments …

This chapter explores tree-based methods for demand prediction. These methods are widely
used given their strong predictive power. We consider three types of methods: Decision …

From data collection to evaluation and visualization of prediction results, this book provides
a comprehensive overview of the process of predicting demand for retailers. Each step is …

Forecasting demand and understanding sales drivers are one of the most important tasks in
retail analytics. However, traditionally, linear models and/or models with a small number of …

Retailers supply a wide range of stock keeping units (SKUs), which may differ for example in
terms of demand quantity, demand frequency, demand regularity, and demand variation …

Sales and demand forecasting is one the most critical tasks of enterprises. It lays the
foundation for many other essential business assumptions, such as cash flows, profit …

Forecasting demand periodically is one of the critical tasks of retail operations. We evaluate
methods that differ in pooling scope, prediction technique and input variables on actual …