Workforce Skill Hours Inference for Accurate Staffing Forecasts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The manual entry of hours of operation (HOO) for workforce management (WFM) skills introduces errors and neglects holiday scenarios, leading to inaccurate staffing forecasts and operational inefficiencies.
Innovation Solution
An automated system that leverages historical data analysis and forecasted reports to calculate HOO, considering factors like peak demand, seasonal variations, and holidays, reducing reliance on manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data entry is used for assigning hours of operation, then flexibility in customization is improved, but accuracy and reliability deteriorate due to entry errors and neglect of holiday scenarios
Solution Approach 1:
The system automatically determines hours of operation by self-analyzing historical data and forecasted reports, eliminating manual data entry. The automated system serves itself by computing HOO based on actual demand patterns, which improves accuracy while maintaining operational flexibility through configurable parameters.
Solution Approach 2:
The manual mechanical process of data entry is replaced with an automated computational system that processes historical data and forecasts. This substitution eliminates human errors in data entry while preserving the ability to customize hours of operation through system configuration rather than manual input.
2Ease of operation
If manual data entry is used for hours of operation, then ease of operation is improved, but productivity deteriorates due to consumption of valuable time and resources
Solution Approach 1:
The system automatically computes hours of operation without requiring manual intervention. By serving itself through automated data processing and computation, the system eliminates the time-consuming manual entry process while maintaining operational simplicity through user-friendly configuration options.
Solution Approach 2:
The automated system continuously processes historical data and forecasted reports to determine hours of operation, eliminating interruptions caused by manual data entry. This continuous automated process improves productivity by constantly updating HOO based on current demand patterns without consuming human time and resources.
3Device complexity
If manual entry method is used, then device complexity is reduced, but measurement precision deteriorates in determining skills demand trends
Solution Approach 1:
The simple manual entry system is replaced with an automated computational system that analyzes historical data and forecasted reports. This substitution increases measurement precision in determining skills demand trends by using data-driven analysis rather than manual estimation, while the system structure remains manageable through modular design.
Solution Approach 2:
The system creates accurate copies of historical data patterns and forecasted scenarios to determine future hours of operation. By copying and analyzing past demand patterns, the system achieves high measurement precision in identifying skills demand trends without requiring complex manual analysis procedures.
Data Source
AI summary
A method for automatedly determining hours of operation (HOO) is provided. A system for work allocation is also provided. A method for generating staffing forecasts is also provided. The method for automatedly determining hours of operation includes performing historical data analysis on historical data comprising past data for a plurality of skills, queue reports for the plurality of skills, or a combination thereof; identifying trends in skills demand for each of the plurality of skills based on the historical data analysis; performing analysis on forecasted reports for skills allocation to generate future demand patterns for each of the plurality of skills; performing HOO analysis for each of the plurality of skills based on the generated future demand patterns and identified skills demand trends; generating a report of the HOO analysis for the plurality of skills; and providing the analysis report to a user.