Gaialabsconsulting

Case Study

Driving and defining change with expertise, compassion and persistence.

Case Study

Driving and defining change with expertise, compassion and persistence.

Case Study

Driving and defining change with expertise, compassion and persistence.

Demand Forecasting for A Large US Retail Company

Background

More and more merchandise being bought online.

The company seeks to ship to customer as soon as possible while lowering the cost.

Forecasting the demand for the products is the key.

Design

Given the scale of the data, use complex neural networks (similar to those OpenAI used) to forecast demand.

Take special considerations for weekends and holidays

Be careful of overfitting to historical data.

Objective

Given a huge amount of historical sales data, forecast the demand for the next day, week, and month.

Especially important to be accurate around the holidays.

Methodology

Initial Steps: clean and normalize the data.

Baselines: use ARIMA models and other simpler neural networks as baselines to compare against.

Validation: use temporal cross-validation to split training and validation set.

Propose to use Fourier transform to perform longer horizon forecast.

Add special positional embedding for weekends and holidays.

Result & Impact

Better Demand Forecasting

The company now can forecast demand more than 20% more accurate than their previous generation models

Improved Logistics Efficiency and Customer Satisfaction

Better demand forecasting leads to improved logistics efficiency and happier customer

Design AI solutions that create more efficiency and impact in the world

Design AI solutions that create more efficiency and impact in the world

Design AI solutions that create more efficiency and impact in the world