MRC Cost Optimization for Virtual Network Operators
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Solution Overview
Problem
Virtual network operators incur significant losses due to erroneous wholesale plan configurations, either with lower or higher usage limits than retail plans, leading to overage payments or unnecessary expenses with mobile network operators.
Innovation Solution
A method and system for monthly revenue commit cost optimization that involves selecting an initial wholesale plan, estimating future network usage, determining the least-cost wholesale plan, and automatically switching customers to this plan to minimize costs, using a MRC Cost Optimization algorithm that considers usage variance and demographic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If customers are mapped to wholesale plans with lower usage limits than retail plans, then overage payments to mobile network operators are reduced, but customer service quality and satisfaction deteriorate due to insufficient data allowances
Solution Approach 1:
The system dynamically adjusts wholesale plan allocations based on actual customer usage patterns. Instead of static mappings, the system continuously monitors usage and recalibrates wholesale plan assignments to match actual consumption, allowing flexible optimization without service degradation
Solution Approach 2:
The system implements feedback loops that monitor customer usage data and automatically adjust wholesale plan mappings. By continuously measuring actual usage against allocated resources, the system learns and adapts to prevent both overallocation and underallocation, resolving the contradiction between cost reduction and service quality
2Reliability
If customers are mapped to wholesale plans with higher usage limits than retail plans, then service quality is maintained, but unnecessary costs are incurred due to overallocation
Solution Approach 1:
The system initially allocates wholesale plan resources at higher levels to ensure service quality coverage, then uses usage monitoring to identify and eliminate excess allocations. This approach ensures service quality is never compromised while systematically removing unnecessary cost overhead
Solution Approach 2:
The system changes the parameters of wholesale plan mappings based on empirical usage data. By adjusting allocation parameters from static to dynamic values derived from actual customer behavior, the system optimizes the balance between service quality and cost efficiency
3Ease of manufacture
If manual methods are used to match retail plans with wholesale plans, then implementation simplicity is maintained, but accuracy and efficiency deteriorate due to errors and heavy manual effort
Solution Approach 1:
The system performs automatic self-matching of retail and wholesale plans using algorithmic analysis of usage patterns. The system serves itself by autonomously identifying optimal mappings without human intervention, eliminating manual errors while maintaining implementation simplicity through automated processes
Data Source
AI summary
Disclosed are systems and methods for monthly revenue commit (MRC) cost optimization, for virtual network operators whose customers utilize a network operated by a different network operator. The disclosed generally involves four stages: 1) onboarding and initial wholesale plan selection; 2) estimating what a customer's requirements (e.g., data usage) are likely to be for a month; 3) determining the least-cost wholesale plan the customer should be placed on; and 4) switching the customer's wholesale plan.