How Procter & Gamble Uses Machine Learning to Forecast Demand and Optimize Inventory Management

By: GoBeyond Team

July 3, 2025

3 min read

P&G AI-driven demand forecasting dashboard showing inventory optimization metrics

Quick Overview

Procter & Gamble (P&G) uses machine learning and advanced analytics to forecast demand by analyzing sales data, market trends, and external factors. This allows P&G to precisely manage inventory, reduce stockouts and overstock, and optimize supply chain performance, improving operational efficiency and customer satisfaction.

Procter & Gamble

Company Size

Large multinational consumer goods company

Revenue Range

$80B+ annual revenue

Primary Challenge

Improving demand forecasting accuracy and inventory management to reduce costs and meet customer demand

Key Metrics

\- More accurate demand forecasts\- Reduced stockouts and overstock\- Optimized inventory levels\- Enhanced supply chain efficiency\- Increased customer satisfaction

The Problem

Traditional forecasting methods were less accurate, leading to inventory imbalances and lost sales opportunities

The Solution

Machine learning models analyzing historical sales, market trends, and external data to generate precise demand forecasts and inventory recommendations

Results

\- Improved forecast accuracy\- Lower inventory holding costs\- Reduced stockouts and lost sales\- Enhanced supply chain responsiveness\- Better alignment of production with demand