Posted on 2021-08-03
Description
Not satisfied with your sales predictions on Excel? Indeed. Predicting sales isn't linear nor a simple moving average. This forecast analysis will provide you with an overview of the predicted sales for every item of your catalog - it does take seasonality into account to measure the potential to be sold in a defined timeline.
We recommend to perform this analyis in a timeline matching the product lifecycle, for example, at the stock arrival date. It can either be an automated analysis sent periodically through mail or a regular process pushed to your favorite supply chain ERP/demand planning tool.
Analytics status
- Available
Business benefit
The major positive impacts to be expected by running this analysis are essentially on :▪ total turnover
▪ sell-through rate
Data inputs (mandatory)
We'll be using historical sales data containing the following information :▪ Date : historical timestep of observation (could be daily, monthly, weekly…)
▪ SKU :SKU ID, item identification number
▪ Revenue : sales in quantity or revenue
Data Output
You get two deliverables :▪ the predicted sales per SKU
▪ a graphical presentation of the predictions
Technical description
We use a regular ARIMA model to deliver this prediction. ARIMA refers to auto-regressive "AR", integrated "I", and moving average"MA". This is a statistical analysis model that uses time series data to either better understand the dataset or to predict future trends.Limitations
This model does not take into account product cannibalization or sales pattern similarity (sold simultaneously, correlated products)
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