IT Ticket Resolution Optimization via Predictive Service Tiering
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Solution Overview
Problem
Conventional systems face difficulties in accurately predicting incident tickets for large and complex data warehouse and operational systems, especially with the integration of new applications, leading to inefficient resolution times due to system complexities.
Innovation Solution
A computer-implemented method that uses predicted volumes of IT tickets and times to identify optimal service characteristics by analyzing deployment characteristics, implementation data, and relationships within the data to predict new ticket volumes and resolution times, thereby recommending preferred service characteristics to minimize resolution time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems are used to manage IT tickets for large and complex data warehouse and operational systems, then system stability is maintained, but ticket resolution time increases and efficiency decreases
Solution Approach 1:
The patent segments the complex system into multiple service tiers (first service tier, second service tier, third service tier) with different levels of automation. Simple tickets are handled by automated services in higher tiers, while complex tickets are escalated to human agents in lower tiers. This segmentation resolves the contradiction by distributing the workload across multiple levels, maintaining system stability while improving resolution efficiency for different ticket types.
Solution Approach 2:
The system performs preliminary actions by pre-configuring multiple service tiers with different resolution capabilities before tickets arrive. Automated services are pre-established to handle common issues, and escalation paths are pre-defined. This allows the system to immediately process tickets according to their complexity without requiring real-time decision-making about resource allocation, thus improving efficiency while managing complexity.
2Productivity
If more resources are allocated to handle increased ticket volumes, then resolution capacity improves, but operational costs increase
Solution Approach 1:
The patent implements dynamic resource allocation where service tiers are activated or deactivated based on real-time ticket volume and complexity. When ticket volumes are low or issues are simple, higher-tier automated services handle more workload, reducing the need for human resources. When complexity increases or volumes surge, lower-tier human services are dynamically activated. This dynamic adjustment resolves the contradiction by optimizing resource utilization according to actual demand rather than maintaining static resource levels.
Solution Approach 2:
The system changes operational parameters by adjusting the threshold values for ticket escalation between service tiers. These parameters can be modified based on observed ticket patterns, seasonal variations, or system changes. By dynamically adjusting escalation thresholds and service capacity parameters, the system optimizes the balance between automated and human resource usage, improving processing capacity without proportionally increasing resource allocation.
3Loss of time
If automated services are used to reduce resolution time, then efficiency improves, but handling complexity of diverse ticket types decreases
Solution Approach 1:
The patent segments ticket handling into multiple specialized service tiers, where each tier is optimized for specific types or levels of complexity. The first service tier handles simple, routine tickets with fast automated resolution. The second and third tiers handle progressively more complex tickets requiring human expertise. This segmentation resolves the contradiction by allowing automated services to efficiently handle their designated simple ticket types while ensuring complex diverse tickets receive appropriate human attention.
Solution Approach 2:
The patent introduces service tier escalation as an intermediary mechanism between automated services and human agents. Tickets that cannot be resolved by automated services are automatically escalated to human agents through defined escalation paths. This intermediary escalation system resolves the contradiction by allowing automated services to maintain fast resolution for compatible tickets while ensuring complex tickets are transferred to adaptable human services that can handle diverse ticket types.
Data Source
AI summary
A method, system, and computer program product for predicting optimal service characteristics to execute predicted IT tickets. The method may include identifying deployment characteristics for an operational system based on an architecture of the operational system. The method may also include receiving implementation data in response to past incident tickets based on the deployment characteristics. The method may also include identifying relationships within the implementation data. The method may also include predicting a volume of new tickets based on the implementation data and the relationships between the implementation data. The method may also include predicting a resolution time for high severity tickets in the volume of new tickets. The method may also include determining preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets. The method may also include transmitting a recommendation comprising the preferred service characteristics.