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Retail Sales forecasting

Three-quarters of retailers who have grown up their business have adopted sales forecasting for their success. 

This is the process of estimating what the business’s sales are going to be in the next month, quarter, half-annually, or annually. It helps retailers to have the right amount of stocks at any given time, therefore it enables them to become more cost-efficient and improve customers' experience. In fact, the less stock on hand they have, the lower our holding costs. Besides, avoiding out-of-stocks means avoiding customers' disappointment.

This AI showcase  SmartPredict is of interest to all those who work in retail and e-commerce. 

Why SmartPredict?

AutoML in this platform allows anyone, regardless of skill level, to forecast sales effortlessly with robust Machine Learning and Deep Learning models that are automatically trained and auto-select the best parameters to provide them the best accuracy. Two editable flowcharts that represent the workflow behind the model training and deployment, are then generated automatically. Then, those with advanced ML/DL knowledge can fine-tune models on their own.

All you have to do is present historical data to the platform and predicted sales will be provided for whatever period of time you wish.

The project and dataset description

In this showcase, we forecast sales of a product over a given period, using SmartPredict’s autoflow. We have at our disposal a set of actual real datasets extracted from a Brazilian top retailer. Some transformations are made to protect some confidentiality. Finally, we have a time series dataset with 937 daily sales, prices, and stocks of a product from 2014-01-01 to 2016-07-31.

We simply follow the 4 steps in the autoflow interface in SmartPredict ( figured below) and 2 flowcharts are provided, with which the best AI model is trained and deployed. We can handle them.

The result 

SmartPredict provides a line chart to visualize the forecasted sales in Predict space. To evaluate the result we can see the provided metric.

As the pipeline to deploy the model has generated automatically, we just click on the rocket icon, and an API is presented in the Monitor Tab. Thus, it is with ease that we integrate the AI system of this showcase into any type of system. Here is an example of an interface that we can manage.

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