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Solaris Fares Management

Case 1 - Automate Airlines’ fares management

Deliver an intelligent fares management system for the airline

  • Manual to automatic: efficiency improvement in fare monitoring/building/publishing
  • Biweekly to hourly: efficiency improvement in fare/rule monitoring
  • 80% fare publishing labor cost saving
  • 300,000+ data processing quantity/day
  • 1,000+ publishing fare quantity/day


Business Challenges

  • Manual processes are costly, time-consuming, and inaccurate
  • The single fare structure needs to be expanded to support business growth
  • The business process and organization lack the coordination needed to support growth
  • Improve fare management efficiency to manage and publish multiple fares, including public and private fares


Project Complexity 

  • 100+ types of ATPCo data analysis, including fare, rule, fare by rule, footnote, fare class, RBD, routing, YQYR
  • 10+ types of ATPCo data publishing, including fare、addon、rec1、recG、P02、FN、CATs、routing...
  • Integration of multiple data sources from 6 departments 
  • 30,000+ OD’s fares, footnote, rules, data monitoring and real-time data query
  • 10,000+ versions of historical fare database and query


System Covering

  • ATPCo Data Integration
  • Strategy Mgmt. 
  • Fare Building
  • Verify & Approve
  • Publish
  • Analyze & Optimize

Learn More

Case 2 - Replacing the existing legacy fares management system

An intelligent fares management system based on business strategies and AI algorithms to help users create competitive fares


Problem

  • Replacement of the existing legacy fares management system function
  • Data accuracy and completeness can’t meet the data analysis requirements
  • Unable to quickly integrate fare and rule data for downstream systems
  • Unable to monitor and query over 1,000 pieces of data in real-time
  • Price changes of competitor airlines, unable to respond quickly, intelligently and automatically to market changes


Solution

  • Phased introduction of a new technology-based system to replace the existing system, with both systems running in parallel
  • Rebuild ATPCo data processing capabilities to improve data accuracy and completeness
  • Process and integrate millions of data points daily
  • Monitor and query over 200,000 OD’s fare, footnote, and rule data in real-time
  • Utilize AI algorithms and machine learning to optimize pricing strategies


4 Phases

  • Replace basic functions
  • Function enhancements
  • Rebuild local fare database
  • Intelligent fare management

Learn More

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