Work Schedule Optimization Using Predicted Worker Risk Thresholds
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Solution Overview
Problem
Current methods for assessing workplace safety risks rely heavily on worker self-assessment, which is insufficient for new or inexperienced workers, leading to inadequate risk evaluation and increased accident rates.
Innovation Solution
A system and method that generate temporal and event-based risk predictions for workers using a processor-based server, optimizing work schedules to maintain cumulative workplace risk below a threshold by considering factors like time of day, worker proximity, equipment, and performance patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If worker self-assessment is used to evaluate safety risks, then the method is simple and easy to implement, but the accuracy and reliability of risk evaluation is insufficient, especially for new or inexperienced workers
Solution Approach 1:
The patent introduces an intermediary system (computing device with processor and database) that mediates between workers and risk assessment. The system automatically collects data from multiple sources, processes it through algorithms, and generates risk predictions without requiring workers to perform complex self-assessments, thus maintaining ease of operation while significantly improving reliability through objective, data-driven evaluation
Solution Approach 2:
The patent replaces the mechanical/manual self-assessment process with an automated electronic system. Instead of workers manually evaluating their own risk levels, the system uses processors to analyze data from wearables, environmental sensors, and worker profiles, substituting human judgment with computational algorithms that provide more consistent and accurate risk predictions
2Reliability
If comprehensive data collection and analysis systems are implemented to improve risk prediction accuracy, then the reliability of safety evaluation is improved, but the device complexity and implementation cost increase
Solution Approach 1:
The patent makes the safety system multi-functional by having a single computing device perform multiple tasks: collecting data from wearables, processing environmental sensor information, analyzing worker profiles, generating risk predictions, and creating work schedules. This consolidation reduces overall system complexity compared to having separate specialized systems for each function while maintaining high prediction accuracy
Solution Approach 2:
The system performs self-service by automatically collecting and processing its own operational data without requiring external intervention. The computing device autonomously gathers data from sensors and wearables, processes it through its algorithms, and generates predictions independently, reducing the need for complex manual monitoring and analysis processes
3Reliability
If work schedules are optimized based on predicted risk to maintain cumulative risk below threshold, then workplace safety is improved, but the complexity of schedule creation and management increases
Solution Approach 1:
The patent applies preliminary action by generating work schedules in advance based on predicted risk patterns. The system analyzes historical data and predicts future risk scenarios, then pre-creates optimized schedules that assign workers to tasks and locations before high-risk periods occur, allowing proactive safety management rather than reactive response to complex real-time conditions
Solution Approach 2:
The system implements feedback loops where actual worker performance and safety data are continuously fed back into the risk prediction algorithms. This feedback mechanism allows the system to refine its predictions and improve schedule optimization over time, managing complexity through adaptive learning rather than requiring complex manual adjustments
Data Source
AI summary
A method and system are provided. The method includes generating, by a server having a processor, temporal and event based risk predictions for each of a plurality of workers at a workplace, using a prediction window of a work shift. The method further includes creating, by the server having the processor, a work schedule for the plurality of workers that is optimized to maintain a cumulative workplace risk below a given threshold, based on the temporal and event based risk predictions.