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Demand forecasting

This article covers sales and demand forecasting within Qoblex.

Demand forecasting is the art of predicting customer demand to ensure there is enough stock to satisfy any new incoming sales and maintain a high service level for your business. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data and statistical techniques or current data from test markets.

Qoblex uses a quantitative technique to forecast demand for each product and variations. A mathematical procedure is used to look at a predefined number of past sales days and attempts to fit a mathematical model, which is then used to forecast future demand.

Note: The forecasting algorithm is based on a mathematical model to predict future customer demand using past sales data. The algorithm does not account for discounts and high seasonality. While the result give you a hint of how well a product is selling and how much you should plan to replenish, it remains an estimation of the demand and not an exact measure.

Let’s see how the details on how the forecasting works in Qoblex.

Parameters of forecasting algorithm

Qoblex allows users to choose their own parameters like the base time interval, days to forecast, growth factor and etc. Let’s see what parameters you can set to train the forecasting model.

Here is how you can access the parameters:

  1. In your Qoblex account, go to Forecasting
  2. Click on Parameters on the left side of the page

Now let’s see what parameters are available in the Forecasting module.

Sales data time interval 
You can select the time interval that you want your forecast to be based on. The sales from the selected time interval will be used to train the forecasting algorithm.

You can choose a predefined time range that the system offers, or set a custom date range.
Days to forecast
This defines how many days into the future you want the system to forecast.
Growth Factor (%)
You can define your growth from the selected time interval. The forecasting engine will include that in the calculations
Additional parameters
You can use additional parameters like Location, Filter by product tag, Filter by product type, Filter by brand, and Filter by supplier. This will help you narrow down the results to only certain products.
Flattening bundles
The last parameter that comes as a toggle switch is for users to define whether they want to run forecasts for bundles or their components

The forecasting view gives you useful insights into how your variations are selling, the total incoming quantities (these are calculated from your open purchase orders), available stock levels, allocated stock, the demand forecast and the most useful measure of all, the total number of units to purchase to fulfill the predicted demand.

Each row in the table represents the forecast for each variation. Clicking on a chart symbol () will show details about the variation being selected.

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