A leading fashion establishment wanted to maintain cost control on the supply chain and logistics of their retail merchandise. They needed a solution that optimized the monetary cost associated with trucks arriving at the store with less than the actual packaged units that were forecasted.
The goal was to achieve a productivity planning dashboard that presented an overall 360-degree view into their supply chain and logistics management
Develop a Machine learning based model that utilized historical data to more accurately forecast/predict units in order to improve associate planning and save money. The model also used data based on projected demand or marketing event for a given day, week or month.
Technology used: Machine Learning Algorithms
Obtain data from EDW and multiple data sources that are stored as cloud SQL data
Use cloud ML algorithms on the processed data to provide insights that can be used for further analysis
The client was able to optimize their shipping and logistics costs by accurately predicting the number of packages per order that arrived at the stores.