Work Assignment Manager Intelligent Routing
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Solution Overview
Problem
Current workforce management systems rely on manual or rudimentary automated methods for assigning and routing work, which are inefficient and fail to optimize employee schedules, skills, and workflows, leading to suboptimal service levels and bottlenecks in back-office operations.
Innovation Solution
The Work Assignment Manager (WAM) system integrates employee schedules, skills, and workflow simulations to intelligently route tasks, using a Just-In-Time (JIT) scoring system and allocation plans to prioritize work based on urgency, skill level, and queue priorities, ensuring tasks are processed efficiently and meeting service goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual or rudimentary automated work routing methods are used, then system complexity is reduced, but productivity and service levels deteriorate due to inefficiency and bottlenecks
Solution Approach 1:
The system performs preliminary actions by simulating workflows in advance using historical data and employee schedules to predict future work volumes and identify potential bottlenecks before they occur, enabling proactive optimization of work assignments
Solution Approach 2:
The system implements dynamic work routing that continuously adapts to changing conditions by recalculating optimal assignments based on real-time employee availability, skill matches, and predicted work volumes, transitioning from static to dynamic optimization
2Measurement precision
If sophisticated workflow simulations and forecasting are implemented, then service level prediction accuracy is improved, but computational time and processing resources increase
Solution Approach 1:
The system applies partial simulation by focusing computational resources on simulating only the most critical workflows and high-volume queues rather than attempting to simulate every possible scenario, achieving sufficient prediction accuracy with reduced computational overhead
Solution Approach 2:
The system performs preliminary computations by pre-calculating employee skill matrices, historical performance metrics, and workflow patterns during off-peak periods, storing these results for rapid retrieval during active work assignment operations
3Productivity
If work assignments are optimized based on employee skills and schedules, then productivity increases, but the complexity of managing employee data and assignments increases
Solution Approach 1:
The system implements a universal data structure that serves multiple functions: it stores employee skill profiles for matching, tracks schedule availability for assignment, and records performance metrics for optimization, eliminating the need for separate management systems for each function
Solution Approach 2:
The system merges previously separate functions of work routing, schedule management, and skill tracking into a unified optimization engine that simultaneously considers all these factors when making work assignments, reducing overall system complexity
Data Source
AI summary
Methods of intelligent routing of work assignment includes indexing plurality of pending tasks and indexing a plurality of available employees. A first employee is retrieved from an index of available employees. A next available task assignable to the first employee is determined. A work item from the next available task is assigned to the first employee from the available employee list. The assigned work item is removed from pending task list. The first employee is removed from the available employee list. The next employee is retrieved from the index of available employees.