Table-Driven Inventory System for Multi-Cabin Revenue Optimization
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Solution Overview
Problem
Conventional seat inventory yield management systems in passenger transportation networks assume that passenger demand is limited to a single cabin category and departure time, leading to inefficiencies in pricing and demand forecasting, especially when introducing premium cabin categories, which requires extensive IT rework and increases the likelihood of mistakes.
Innovation Solution
A table-driven data storage system that integrates demand across multiple cabin classifications and departure times, using customer segmentation and utility functions to optimize pricing and revenue maximization, allowing for flexible categorization of demand categories without requiring changes to the underlying logic code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional seat inventory yield management systems assume single cabin category demand, then system complexity is reduced, but pricing accuracy and revenue optimization deteriorate
Solution Approach 1:
The patent segments demand into multiple categories based on cabin class preferences (e.g., first class demand, business class demand, coach demand) and departure time preferences. This segmentation allows the system to track and manage demand for each category separately, enabling accurate pricing for premium cabins without requiring a complete rewrite of the underlying system logic. The demand segmentation is implemented through table-driven data structures that categorize historical booking patterns.
Solution Approach 2:
The patent changes the parameter representation from a single cabin-category assumption to multiple demand categories with distinct parameters. Each demand category has its own parameters for price sensitivity, booking timing, and cabin preference. This parameter change enables the system to model complex passenger behavior while maintaining the existing yield management framework through table-driven configurations rather than code changes.
2Productivity
If premium cabin categories are added to the product mix, then revenue optimization potential is improved, but IT rework and error risk increase
Solution Approach 1:
The patent creates a universal table-driven framework that can handle any number of cabin categories and demand types without requiring specific code implementation for each case. The same data structures and processing logic work whether managing two cabins or ten cabins, making the system universally applicable to different premium product mixes. This multi-functionality is achieved through generic table schemas that accommodate variable numbers of cabin classes and demand categories.
Solution Approach 2:
The patent uses table-driven data structures that copy and replicate demand patterns across different cabin categories and time periods. Historical demand data is copied into standardized table formats that can be reused for forecasting and pricing across multiple premium cabin types. This copying approach eliminates the need to create custom logic for each cabin category, reducing IT rework while enabling comprehensive revenue optimization.
3Measurement precision
If demand is segregated by individual departure time and travel class, then demand forecasting precision is improved, but system adaptability to premium products deteriorates
Solution Approach 1:
The patent implements dynamic demand categories that can be configured through table data rather than fixed code structures. Demand categories can be dynamically added, removed, or modified to reflect new premium products or changing market conditions without requiring system reconfiguration. The table-driven approach allows the system to adapt to different product mixes by simply updating data tables, maintaining both forecasting precision and versatility.
Data Source
AI summary
A system and method that includes receiving for each passenger: a PNR; a passenger identification number (“PIN”); a passenger's historical itinerary; a fare; and an assigned seat. The method also includes assigning each PIN with a customer segment; storing the assignment within a first table; and then associating, using the first table and hierarchical rules stored within a second table, each PIN with a demand category. The method also includes identifying a product market; identifying a demand for the product market by each demand category; representing, using a utility function, the demand for the product market; and using the utility functions in a variable pricing strategy for the product market. The method also includes changing the hierarchical rules to alter the association between customer segments and demand categories without changing logic code, thereby increasing flexibility of the system.