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AI-Powered Demand Forecasting Solution for FMCG Industry

plavno.io

Consumer products & services

Retail

Demand Forecasting Challenges in FMCG

Analysts spent 60-70% of time on manual calculations with standard methods, leading to 30%+ forecast deviations. Variable demand influenced by seasonality, promotions, weather, and regional factors caused frequent overstocking/understocking, increasing storage and transportation costs.

About the Client

Cosmetics and personal care brand with 35,000 employees, operating 92 boutique stores across Spain and France, selling 400,000 products monthly

Project Goals for AI-Driven Forecasting

Increase first-week demand forecasting accuracy by 25%
Improve first-month forecasting accuracy by 20%
Reduce deviation from stable benchmarks to 1%
Decrease analytical specialists' forecasting time by 40%

Core Forecasting System Requirements

Automated demand forecasting using machine learning algorithms
Real-time data processing with statistical/weather/calendar attributes
Custom analytical reporting module
Integration with existing ERP and sales data systems
User-friendly interface for forecast adjustments

Preferred Technology Stack

Artificial Intelligence

Cloud Computing

Big Data

Machine Learning

System Integration Requirements

ERP systems
Sales data platforms
Weather data APIs
Promotional calendars

Non-Functional Requirements

Scalable architecture for 400,000+ monthly transactions
Real-time processing performance
Data security compliance
High-availability cloud infrastructure

Expected Business Impact of AI-Powered Forecasting

Improved inventory management through accurate demand predictions, reduced operational costs from automated analytics, enhanced decision-making capabilities with customizable reports, and optimized resource allocation through strategic 5-year forecasting.