In a big data enabled environment, manufacturers and distributors may have access to previously unobserved retailer-level demand related information. This additional information can be considered in demand forecasting to produce more accurate forecasts, and thus enable better stock-outs management. In Part II of this two-part paper, we explore the hierarchical nature of fast moving consumer goods (FMCG) demand (represented by sales) time series and produce one week ahead rolling forecasts on universal product code (UPC) level (or distributor level, as per our definition below). We show that the hierarchical forecasting framework has significant accuracy improvement over the conventional univariate forecasting methods. The main reason of the observed improvements is due to the price and promotion information available at the retailer level, which is assumed to be unknown to the distributor. To reconcile forecasts according to the hierarchy, only the forecast values at retailer level are needed, the business strategies of individual retailers remain proprietary. A freely available dataset is considered to encourage further exploration. Data exploratory analysis and visualization tools are discussed in Part I of the paper.