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Development of a Machine Learning-Driven Forecasting Solution for Enhanced Operational Efficiency in Inflight Retail

supercharge.io

Retail

Food & Beverage

Travel Services

Challenges in Traditional Forecasting Methods

The client lacked a modern data platform to implement machine learning solutions, relying on outdated rule-based forecasting systems. This resulted in suboptimal stock levels, inefficiencies in supply chain operations, and missed opportunities to leverage passenger purchasing behavior patterns for demand prediction.

About the Client

World's leading inflight retailer serving over 20 airlines and 300 million passengers annually, specializing in food & beverage and travel retail offerings.

Key Project Goals

Develop an ML-based forecasting solution to improve accuracy by double digits
Optimize inventory management through predictive analytics
Integrate automated forecasting into daily supply chain operations
Process multi-source data in real-time for dynamic demand prediction
Establish a scalable foundation for future AI-driven initiatives

Core System Capabilities

Exploratory data analysis tools for passenger behavior insights
Automated feature engineering for time-series forecasting
Real-time data pipeline integration with airline sales systems
Model deployment environment with Databricks integration
Interactive dashboards for forecasting visualization and scenario planning

Technology Stack

Databricks

Cloud-based ML platforms

Time-series forecasting algorithms

Data lake architecture

System Integrations

Existing supply chain management systems
Airlines' sales data APIs
Inventory tracking platforms

Operational Requirements

High scalability for global airline operations
Real-time processing capabilities
Data security compliance with aviation regulations
99.9% system availability SLA

Expected Business Outcomes

Implementation of the ML forecasting solution is projected to deliver a 10x return on investment through reduced stockouts, optimized inventory costs, and improved passenger satisfaction. The system will enable data-driven decision-making across 1,000+ aircraft operations while establishing a foundation for future AI applications in retail aviation.