Yield Management for Human-Factor Resources
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Solution Overview
Problem
Human-factor resource industries face inflexibility in pricing strategies, leading to costly promotion programs due to fixed prices for composite resources, which do not account for variable demand and supply, unlike yield management systems used in perishable resource industries.
Innovation Solution
A computer-based yield management system that determines marginal values for human-factor resources using a continuous optimization function, allowing for dynamic pricing adjustments based on demand and supply, enabling better matching of resource availability with user demand across different locations and times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If fixed prices are used for composite resources in human-factor resource industries, then pricing simplicity is maintained, but profitability is reduced due to inability to account for variable demand and supply
Solution Approach 1:
The patent implements dynamic pricing by transitioning from fixed prices to variable prices that automatically adjust based on real-time demand and supply conditions. The system continuously monitors resource availability and user demand to determine optimal transaction prices, enabling the pricing mechanism to adapt dynamically rather than remaining static.
Solution Approach 2:
The system changes the pricing parameter from a fixed value to a variable value determined by multiple factors including demand intensity, supply availability, time of day, and resource type. This parameter transformation allows the price to fluctuate within optimal ranges to maximize profitability while responding to changing market conditions.
2Quantity of substance
If promotion programs are expanded to reach more users, then user acquisition increases, but costs increase significantly
Solution Approach 1:
The patent changes the promotional approach by varying transaction prices as a promotional tool rather than relying on uniform discount programs. By adjusting prices dynamically based on demand and supply, the system can attract users during periods of high capacity utilization without incurring additional promotion costs, effectively using price elasticity to drive user acquisition.
Solution Approach 2:
The system incorporates feedback loops that monitor user response to pricing changes and automatically adjust future pricing strategies. This feedback mechanism allows the system to learn from promotional effectiveness and optimize future pricing decisions to achieve user acquisition goals at lower costs.
3Productivity
If uniform pricing is applied across all locations and times, then operational simplicity is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements dynamic pricing that automatically adjusts based on location-specific and time-specific demand and supply conditions. The system monitors capacity utilization at different locations and times, then sets prices to optimize resource utilization, transforming the static uniform pricing model into a dynamic adaptive model.
Solution Approach 2:
The pricing system is segmented into location-specific and time-specific pricing zones, allowing each segment to be optimized independently based on local demand and supply conditions. This segmentation enables tailored pricing strategies for different geographic areas and time periods without requiring complete system redesign.
4Reliability
If capacity is increased to meet peak demand, then service availability improves, but costs increase due to fixed capacity constraints
Solution Approach 1:
The patent implements dynamic capacity management by adjusting service capacity in response to real-time pricing signals and demand conditions. Rather than maintaining fixed capacity levels, the system can scale capacity dynamically based on economically justified demand, allowing service availability to improve during high-demand periods while reducing capacity during low-demand periods to control costs.
Data Source
AI summary
A computer-based system and method loads data for individual resources and composite resources from a resource revenue management system into a yield management system. The individual resources include human-factor resources. Each composite resource includes a collection of individual resources. Internal data structures are constructed for linking each of the resources to their associated composite resources and for linking each of the composite resources to their associated resources. Yield management techniques are used to interpret the data and to provide a set of revenue-maximizing transaction parameters under which a resource network offers its composite resources for transactions with users. The transaction parameters are transferred from the yield management system into the resource revenue management system. The resource revenue management system communicates with users, creating transactions for the composite resources using the values calculated by the yield management system.