ML Work Procedure Analysis for Frontline Productivity
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Solution Overview
Problem
Conventional systems are ineffective and time-consuming in identifying opportunities for productivity improvement in frontline workers, relying on methods like time and motion studies that are expensive and intrusive.
Innovation Solution
A computing system generates actionable insights using machine learning models to analyze work procedure data, providing predictive metrics for improving productivity by identifying necessary actions and optimizing work instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If time and motion studies are used to identify productivity improvement opportunities, then productivity improvement can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent uses electronic copies and digital representations of work procedures instead of physical observation. Work procedures are captured as electronic data including task sequences, time stamps, and performance metrics, allowing analysis without physical time and motion studies. This digital copying enables rapid replication and analysis of work processes.
Solution Approach 2:
The patent replaces mechanical observation methods (human observers physically watching and recording) with automated electronic systems. Sensors, software agents, and computing systems automatically capture and analyze work procedure data, eliminating the need for manual time and motion studies while providing more comprehensive and continuous measurement.
2Productivity
If time and motion studies are conducted by observing workers, then productivity insights can be gained, but the process becomes intrusive
Solution Approach 1:
The system enables workers to self-report work procedure data through electronic interfaces, mobile devices, or automated sensors that capture information without human observation. Workers input task completion data, time stamps, and performance metrics themselves, eliminating the need for external observers and reducing intrusiveness while maintaining data collection effectiveness.
Solution Approach 2:
The patent introduces electronic intermediaries (software agents, sensors, and computing systems) that mediate between workers and the analysis process. These intermediaries automatically capture work procedure data without requiring direct human observation or interaction with workers during task performance, thereby reducing intrusiveness while enabling comprehensive productivity analysis.
3Productivity
If conventional analysis methods are used to identify work procedure improvements, then some insights can be obtained, but the insights are not timely or actionable
Solution Approach 1:
The patent implements continuous electronic data collection and real-time or near-real-time analysis of work procedures. Instead of periodic manual studies, the system continuously captures work procedure data, processes it through computing systems, and generates actionable insights ongoing, enabling timely identification and implementation of productivity improvements.
Solution Approach 2:
The system provides immediate feedback loops where work procedure data is automatically analyzed and insights are rapidly communicated back to relevant stakeholders. Computing systems process electronic data and generate actionable recommendations that are quickly delivered to workers, supervisors, or process owners, enabling rapid iteration and continuous improvement rather than delayed conventional analysis cycles.
Data Source
AI summary
A system and method of generating a plurality of actionable insights is disclosed herein. A computing system retrieves data corresponding to a work procedure. Each work procedure includes a plurality of steps. The computing system generates a predictive model for each actionable insight using a plurality of machine learning models by generating an input training based on the retrieved work procedure data and learning, by the plurality machine learning models, a metric corresponding to each actionable insight based on each respective input training set. The input data set for each actionable insight includes actionable insight specific information. The computing system receives a request to generate a plurality of actionable insights for a current work procedure. The computing system generates, via the predictive models, a plurality of metrics for a plurality of actionable insights based on data corresponding to the current work procedure.