Workforce Management Interval Decomposition
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Solution Overview
Problem
Traditional workforce management systems inaccurately calculate staffing requirements due to treating work items that span multiple time intervals as single events, leading to sub-optimal resource utilization and inaccurate key performance indicators, especially in blended environments with asynchronous and synchronous work items.
Innovation Solution
The system decomposes asynchronous and synchronous work items into interval-specific activity records, analyzing real-time and historical event streams to capture true handle time and volume across their lifespan, feeding this data into forecasting algorithms for accurate staffing requirement calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional workforce management systems treat work items that span multiple time intervals as single events, then the system complexity is reduced and data processing is simplified, but staffing requirement calculations become inaccurate and resource utilization becomes sub-optimal
Solution Approach 1:
The patent segments work items that span multiple time intervals into interval-specific activity records. Each work item is decomposed into discrete activities associated with specific time intervals, allowing accurate tracking of volume and handle time for each interval separately. This segmentation resolves the contradiction by maintaining system simplicity while achieving precise staffing calculations through structured data organization.
2Ease of operation
If work items are counted only once in the interval they ended or started, then key performance indicators can be calculated, but interval-specific workload representation becomes inaccurate especially for long-duration work items
Solution Approach 1:
The patent segments the counting of work items by creating interval-specific activity records for each time interval in which a work item is active. Instead of counting each work item once, the system generates separate activity records for each interval, enabling both accurate KPI calculation and precise interval-specific workload representation simultaneously.
Solution Approach 2:
The patent adds a temporal dimension to work item counting by creating activity records that span multiple time intervals. Each work item generates activity records in each interval it remains active, transforming the single-dimension counting approach into a multi-dimensional representation that captures workload distribution across intervals while maintaining KPI calculability.
3Productivity
If traditional data acquisition methods assume all work activities occurred in the ending time interval, then data processing is simplified, but handle time and volume data per time interval become inaccurate
Solution Approach 1:
The patent segments work activities into interval-specific components by creating activity records that attribute volume and handle time to the specific time intervals in which activities occurred. This segmentation maintains data processing efficiency through automated record generation while achieving accurate handle time and volume measurement for each interval through proper temporal attribution.
Data Source
AI summary
Interval-specific, activity-based systems and methods, and non-transitory computer readable media, including activity-based data acquisition, activity-based forecasts, and activity-based staffing. Work items are automatically decomposed into data that is usable for workforce management purposes at the interval level. Volume/average handle time forecasts, staff requirement calculations, and schedules are driven by historical patterns of interval-specific activity required to resolve long duration work items.