The ability of NetRush to predict sales plays a pivotal role in determining optimal inventory levels. The sales data furnished by NetRush encompasses weekly sales quantities of $50$ products retailed on Amazon. This dataset spans from the first week of $2012$ to Week $39$ of $2017.$ The primary aim of this assignment is to construct forecasting models for each product, predicting sales quantities for a span of $4$ weeks beyond Week $39$ of $2017.$ This involves generating forecasts for Weeks $40,41,42,$ and $43$ of $2017.$ Additionally, the secondary objective involves crafting a procedural framework for forecasting. This empowers brand managers to methodically discern an appropriate forecasting model and generate forecasts for new products.

Required Files

Data File: Data Analytics Forecasting.xlsx Download Data Analytics Forecasting.xlsx$-$ The WeeklySales worksheet comprises three date$-$related columns $($Columns A$,$ B$,$ and C$)$ along with weekly sales quantities data for $50$ products $($Columns D to BA$)$:

Column A date The last day $($Saturday$)$ of the week when weekly data was compiled.

Columns B and C year, weekNum Year and the corresponding week of the year for the weekly data compilation.

Columns D to BA S$37322,$ S$37326,...,$ S$1$A$174$

Sales quantity sold during the respective week of the year. Each column header represents a product's stock$-$keeping unit $($SKU$).$ Missing values are indicated as #N$/$A$.$

Please note that the specific items are not disclosed, but these products fall within the food supplement category.

Forecast Table Template: Data Analytics Forecasting Summary Table.xlsx Download Data Analytics Forecasting Summary Table.xlsx This file provides a template for your team to present final point and interval forecasts for each SKU.