Stock Level Optimizer
Case Details
Client: Taatra, Bangalore
Tag: Business
Project Duration: 12 Months
The Problem
The Company is a seller of Organic Edible Oils and other packed food items. They often ran out of stock and lost potential revenue or had too many items in stock that resulted in wastage.
The Solution
Based on analysis of their existing setup, Spagylo built an AI model that can accurately predict the demand. Data was collected from various sources and a model was built and trained on
1. Order history
2. Seasonal Consumer Trends
3. Holidays and Events
4. Competitor Activity
5. Reviews and Ratings
and a few other parameters. Based on the training results, parameters were tweaked to achieve further improvements. The application also provided a weekly recommendation of the stock that needed to be replenished and the required quantity. An integration with their eCommerce application was also built to track daily Sale and Stock information.
The Impact
Taatra was able to save around 12% of food items from being wasted due to product expiry. Average Out of Stock listings on the website reduced from 20 to 2. Taatra’s sales that year grew by 21%.
