SENTIENT Case Study

Warehouse Retailer Deploys Forecasting App for Inventory Management

Customer

Multinational Warehouse Club Retailer

Industry

Consumer Goods

Function

Demand Forecasting

Problem

Unsold or damaged inventory represent a significant cost to retailers. A company seeks to reduce these costs by accurately predicting the demand forecast for its products, and reducing the human error present in previous estimations.

Solution

An app was developed with the aid of machine learning  to predict sales forecast for each product, and plan accordingly. The model used in the solution was trained on seven years worth of promotional and sales data. The solution could even be extended to fresh foods and baked goods, where, for example, the application could determine how much of an item should be baked, and during what part of the day. 

Result

The solution is being widely deployed across the company, and has been able to accurately predict sales from past historical data, improving annual sales by 5%, recommend appropriate course of action on inventory management, reduce man-hours. 

More Use Cases

For the Retail Industry

Food

Multinational Food Provider Dramatically Increases Sales

Customer Sentiment

NLP Saves Hundreds of Thousands in Customer Sentiment Analysis

New Customer

Floral and gourmet foods gift retailer and distribution company increases revenues by 7% using a ChatBot

2263, KAUST Tech Park Headquarters, 

4700 KAUST, Thuwal, Saudi Arabia 22935

Contact

Call : +966 54 312 3734

Email : info@unitx.tech

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