Shift Schedule Management Using Machine Learning to Identify Problem Shifts
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Solution Overview
Problem
Existing shift scheduling systems face challenges in predicting and mitigating unscheduled shifts across multiple job categories, industries, and locations, as patterns of absenteeism and call-offs can be complex and difficult to automate, leading to reduced productivity and employee morale.
Innovation Solution
A computer-implemented method using machine learning algorithms, specifically logistic regression and clustering, to identify problem shifts by analyzing shift schedules and time and attendance data, and providing incentives to employees to fill these shifts, with a graphical user interface for bid submission and schedule adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated determination of problem shifts is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual analysis of shift patterns with machine learning algorithms that automatically analyze large datasets of schedule data. The system uses automated computational methods to identify problem shifts, eliminating the need for manual HR analysis while improving productivity through scalable automated processing.
Solution Approach 2:
The system enables self-service functionality by automatically identifying problem shifts and generating insights without requiring manual intervention. The machine learning model autonomously processes schedule data, detects patterns, and provides recommendations, allowing the system to serve itself in analyzing complex absenteeism patterns across multiple dimensions.
2Measurement precision
If machine learning algorithms are applied to large datasets, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing schedule data before applying machine learning algorithms. The machine learning model is trained in advance on historical data to learn patterns of absenteeism, so when new schedule data arrives, the system can quickly make accurate predictions about problem shifts without extensive real-time computation.
Solution Approach 2:
The patent replaces time-consuming manual data analysis with automated machine learning processing. The system uses computational algorithms to rapidly analyze large datasets of shift schedules and attendance records, achieving high measurement precision in shift compliance metrics while reducing the time required compared to manual HR analysis of individual employee patterns.
3Reliability
If patterns across large datasets are analyzed, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal machine learning model that can analyze patterns across multiple dimensions including different employers, locations, industries, and time periods. The same core algorithm handles diverse data types and patterns, providing reliable predictions across various contexts without requiring separate specialized systems for each analysis scenario.
Solution Approach 2:
The system replaces complex manual analysis methods with automated machine learning that can process large datasets reliably. The machine learning model automatically identifies patterns in absenteeism across multiple employers and locations, providing consistent and reliable predictions without the complexity of manual data collection, cleaning, and analysis procedures that would be required to achieve similar reliability.
Data Source
AI summary
Implementations relate to methods and systems to identify problem shifts. In some implementations, a method includes obtaining a plurality of published shift schedules, each published shift schedule associated with a respective shift of a respective employer of one or more employers, wherein each published shift schedule includes a location attribute, industry code attribute, and week indicator attribute; for each published shift schedule, obtaining a corresponding time and attendance record; programmatically analyzing the published shift schedule and the corresponding time and attendance record to determine unscheduled shifts; and adding unscheduled shift data associated with one or more unscheduled shifts to a training corpus, wherein the unscheduled shift data includes two or more of an employer identifier, a location identifier, a shift identifier, an industry identifier, an employee identifier, a job type identifier; and applying a machine learning algorithm to the training corpus to determine a plurality of problem shifts.