Multi-Session Meeting Optimization for Contact Center Staffing
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Solution Overview
Problem
Current contact center systems face challenges in optimizing the number of sessions for multi-session meetings based on agents' skill requirements, leading to potential understaffing or overstaffing during scheduled work-shifts.
Innovation Solution
A computer-implemented method and system that optimize the number of sessions for multi-session meetings by receiving input parameters such as time-range of scheduled work-shifts, maximum agents per session, skill-types, and buffer levels, and using a schedule manager Microservice to determine the optimal number of sessions based on agents' skill requirements, ensuring adequate staffing while minimizing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of sessions is increased to ensure adequate staffing, then understaffing risk is reduced, but host time waste increases due to sessions with few agents
Solution Approach 1:
The system dynamically changes the number of sessions parameter based on skill requirements and staffing needs. By calculating optimal session counts using algorithms that consider skill buffers and availability, the system adjusts session parameters to balance staffing adequacy with efficient host time utilization.
Solution Approach 2:
The system implements feedback mechanisms by monitoring skill requirements, agent availability, and session outcomes. This feedback loop enables continuous optimization of session numbers, ensuring staffing needs are met while minimizing waste of host time through data-driven adjustments.
2Loss of energy
If the number of sessions is decreased to reduce host time waste, then host efficiency improves, but understaffing risk increases
Solution Approach 1:
The system dynamically adjusts session parameters based on real-time skill requirements and staffing data. By optimizing the number of sessions using calculated buffers and availability metrics, the system reduces host time waste while maintaining adequate staffing levels through intelligent parameter management.
Solution Approach 2:
The system performs preliminary calculations of skill requirements and session optimization before scheduling meetings. By pre-calculating optimal session numbers based on forecasted skill needs and agent availability, the system prevents both understaffing and host time waste before sessions are scheduled.
3Reliability
If manual adjustment of sessions is performed to avoid understaffing, then staffing adequacy is maintained, but operational complexity increases
Solution Approach 1:
The system performs self-service by automatically calculating and adjusting session numbers based on skill requirements and staffing data. The automated algorithms eliminate the need for manual supervisor intervention, reducing operational complexity while maintaining staffing adequacy through intelligent self-management.
Solution Approach 2:
The system replaces manual mechanical adjustment processes with automated computational algorithms. By substituting supervisor manual calculations with computer-based optimization algorithms that consider skill buffers and availability, the system maintains staffing adequacy while dramatically reducing operational complexity.
4Reliability
If sessions are cancelled due to understaffing, then staffing requirements are met, but agent schedule stability deteriorates
Solution Approach 1:
The system performs preliminary optimization calculations before scheduling sessions to ensure staffing requirements are met in advance. By pre-calculating optimal session numbers based on skill buffers and agent availability, the system prevents understaffing situations that would lead to cancellations and schedule instability.
Solution Approach 2:
The system implements beforehand cushioning by incorporating skill buffers in the session optimization calculations. These pre-calculated buffers act as a cushion against variability in agent availability, ensuring staffing requirements are met without requiring last-minute cancellations that disrupt schedule stability.
Data Source
AI summary
A computer-implemented method for optimizing a number of sessions of a multi-session meeting based on agents skill requirement during a time-range of scheduled work-shifts, in a cloud-based contact center. The computer-implemented method includes receiving a time-range of scheduled work-shifts, a maximum number of agents in each session of the multi-session meeting, one or more skill-types and a buffer-level for each skill-type, operating a schedule manager MS to provide scheduled work-shifts of agents in the time-range of scheduled work-shifts that include open slots and net staffing data of each skill-type, determining a total number of agents, calculating a lower-bound of sessions and an upper-bound of sessions, iteratively determining a number of sessions of the multi-session meeting and allocating the total number of agents to the determined number of sessions of the multi-session meeting until an optimal number of sessions of the multi-session meeting is reached.