Human Resource Scheduling with Predictive Roster Utilization
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Solution Overview
Problem
Existing human resource management platforms are limited in effectively managing large rosters of irregularly scheduled staff, relying on manual distribution or standardized routing rules, and fail to consider a wide range of variables in scheduling and communication.
Innovation Solution
A system and method that includes a human resource management module with registration and tracking, scheduling, rating, and intelligence blocks to automate complex scheduling and communication, utilizing predictive analysis and artificial intelligence to optimize roster utilization and manage human resources efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual distribution or standardized routing rules are used for scheduling, then ease of operation is maintained, but productivity and effectiveness deteriorate due to inability to manage large rosters of irregularly scheduled staff efficiently
Solution Approach 1:
The system enables self-scheduling where human resources automatically receive shift offers based on their profiles, availability, and performance ratings. The automated framework eliminates manual intervention while maintaining operational simplicity through intelligent algorithms that match staff to shifts based on multiple criteria including performance history, availability, and skill matching.
Solution Approach 2:
The patent replaces manual scheduling mechanisms with an automated computer-based system that uses algorithms to optimize shift distribution. The system automatically processes scheduling decisions by evaluating multiple variables simultaneously, substituting human judgment with a systematic automated decision-making process that handles large rosters efficiently.
2Adaptability or versatility
If standardized routing rules are used for automated scheduling, then ease of operation is improved, but adaptability deteriorates due to limited scope of variables that can be considered at a given time
Solution Approach 1:
The system dynamically adjusts scheduling criteria and weights based on real-time data and organizational needs. It evaluates multiple variables including performance ratings, availability, skill matching, and historical data, adapting the scheduling algorithm to prioritize different factors based on current operational requirements and organizational goals.
Solution Approach 2:
The patent incorporates multiple adjustable parameters and variables in the scheduling algorithm, including performance thresholds, availability windows, skill requirements, and priority weights. These parameters can be modified to change scheduling behavior, allowing the system to adapt to different scenarios while maintaining a unified scheduling framework.
3Productivity
If automated scheduling systems are implemented, then productivity is improved, but loss of information deteriorates due to insufficient consideration of multiple variables in scheduling decisions
Solution Approach 1:
The system serves multiple functions within a single automated framework: it evaluates performance data, checks availability, matches skills to requirements, optimizes shift distribution, and communicates with staff. This multi-functional approach ensures comprehensive consideration of all relevant variables while maintaining automated efficiency.
Solution Approach 2:
The system incorporates feedback loops where scheduling outcomes are evaluated against performance metrics and organizational goals. Performance data from completed shifts feeds back into the scheduling algorithm, allowing the system to learn from past decisions and improve future scheduling accuracy while maintaining automated operation.
Data Source
AI summary
Embodiments herein disclose systems and methods for scheduling and managing human resources. The system tracks one or more shifts for one or more empty tasks or staffing spots in an organization and triggers an action upon a detection of at least one predefined event. When triggered, the action includes compiling a list of human resources and communicating with each of the human resources on the list via a suitable channel to receive confirmation of interested human resources to attend the one or more shifts. The action is triggered upon determining that not enough human resources will sign up for the one or more empty spots, or that an admin has not filled the one or more empty spots. Further, the system is configured to facilitate self-scheduling, rate the human resources and perform predictive analysis such as roster utilization predictions and predictive analysis.