Service Request Allocation Engine Using Dynamic Skill Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current service request management systems in industries like IT and others face inefficiencies due to manual allocation of tasks, lack of consideration for technician skills and availability, leading to unresolved issues, customer dissatisfaction, and increased costs.
Innovation Solution
A system with a plan maintenance component, expert guidance maintenance component, and an expert guidance system that dynamically activates/deactivates request resolution data structure components based on customer-specific plans, prioritizing actions and resources, and allocating service tickets to appropriate resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual allocation of service requests is used, then service technicians can select tickets based on preference, but service efficiency deteriorates and difficult incidents remain unresolved
Solution Approach 1:
The system performs automatic allocation of service requests without requiring manual intervention from service technicians. The allocation engine automatically matches service requests with appropriate technicians based on skills, availability, and incident characteristics, eliminating the need for technicians to manually select tickets while improving service efficiency through objective criteria
Solution Approach 2:
The manual mechanical process of technicians reviewing and selecting tickets from lists is replaced with an automated computer-based allocation system that uses algorithms to match requests with technicians, substituting human judgment with systematic automated decision-making
2Device complexity
If service requests are allocated without considering technician skills, then allocation process is simplified, but incidents remain unresolved due to lack of necessary attributes
Solution Approach 1:
The system pre-establishes service technician profiles containing skills, qualifications, and availability information before allocation occurs. This preliminary preparation enables the allocation engine to quickly match service requests with appropriate technicians based on required attributes, ensuring reliable incident resolution without complex real-time assessments
Solution Approach 2:
The system incorporates feedback loops where allocation outcomes are monitored and used to refine matching algorithms. The system learns from successful and unsuccessful allocations, continuously improving its ability to match technician skills with incident requirements, thereby enhancing resolution success rates
3Reliability
If highly skilled technicians are assigned to simple tasks, then service requests are resolved, but resource waste and increased costs occur
Solution Approach 1:
The system applies the principle of local quality by matching specific technician skills with specific incident requirements rather than using a uniform allocation approach. Each service request is evaluated against the particular skills needed for that incident, ensuring that technicians are assigned to tasks that match their competencies and that simple tasks are handled by appropriately skilled technicians
Solution Approach 2:
The system dynamically adjusts allocation parameters based on incident characteristics, technician availability, and skill matching scores. By changing allocation parameters in real-time based on current conditions, the system optimizes resource utilization and prevents assignment of highly skilled technicians to simple tasks
4Quantity of substance
If large numbers of simultaneous service requests are received, then more work is available for technicians, but manual management becomes infeasible
Solution Approach 1:
The allocation system operates autonomously to manage large volumes of service requests without requiring manual intervention. The automated engine continuously processes incoming requests, matches them with available technicians based on current system state, and assigns tasks, enabling the organization to handle high request volumes that would be impossible to manage manually
Solution Approach 2:
The system dynamically adapts to changing conditions in real-time, adjusting allocations as technicians complete tasks, new requests arrive, or availability changes. This dynamic behavior enables the system to efficiently manage fluctuating workloads and large numbers of simultaneous requests by continuously optimizing the allocation state
Data Source
AI summary
A service request resolution system for resolving requests from customers, the system including a plan maintenance component, including one or more processors, to maintain plans that include an inventory data structure detailing inventory of a plurality of customers, an expert guidance maintenance component including a request resolution data structure including possible actions to resolve requests in respect of items of inventory detailed in the inventory data structure, and an expert guidance system, including one or more processors, operable to dynamically activate and/or deactivate components of the request resolution data structure, the activation or deactivation of components effected according to the plan of a specific customer.