Industrial Layout Optimization Using ML Trajectory Mapping
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Solution Overview
Problem
Conventional methods for analyzing and optimizing industrial environments face challenges in efficiently tracking worker movements, coordinating material handling equipment, and designing layouts to minimize inefficiencies and safety risks, particularly during changeovers, which lead to extended setup times and reduced productivity.
Innovation Solution
A machine learning-based system that utilizes image and video data from video capturing units and positioning systems to compute trajectories, determine metadata, and predict optimized industrial processes using spatio-temporal reasoning, providing recommendations for improved layout designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation and analysis by industrial engineers is used, then detailed notes and insights can be captured, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual observation and note-taking by industrial engineers with an automated computer vision system using cameras and machine learning algorithms. The system automatically captures worker movements, equipment coordination, and layout analysis through image processing, eliminating the need for manual tracking while maintaining detailed analytical capabilities.
Solution Approach 2:
The system enables self-service analysis where the industrial environment automatically generates its own analytical data through embedded cameras and sensors. The machine learning models process the visual data autonomously to produce insights about worker movements, bottlenecks, and optimization opportunities without requiring external manual intervention.
2Reliability
If assets are poorly located, then tool availability may be compromised, but workers waste time moving across the facility to gather tools or materials
Solution Approach 1:
The system performs preliminary analysis of asset locations and worker movement patterns to identify optimization opportunities before implementing changes. By analyzing historical data and simulating different layout configurations, the system determines optimal locations for tools and materials that will minimize future walk time while ensuring tool availability.
Solution Approach 2:
The patent applies local optimization by analyzing specific zones and workstations individually to determine the best asset placements for each location. Rather than uniformly redistributing all assets, the system tailors asset locations to specific operational needs of different areas, minimizing local walk time while maintaining overall system efficiency.
3Productivity
If conventional manual analysis methods are used, then layout optimization can be attempted, but congestion from multiple workers or equipment creates bottlenecks that are difficult to identify and resolve
Solution Approach 1:
The system implements continuous feedback by monitoring worker movements and equipment coordination in real-time, identifying bottlenecks and congestion points as they occur. The machine learning models analyze this feedback data to detect patterns of inefficiency and provide recommendations for layout adjustments that will eliminate identified bottlenecks.
Solution Approach 2:
The patent adds a temporal dimension to layout analysis by analyzing how worker and equipment movements evolve over time. Rather than static layout evaluation, the system captures dynamic movement patterns across multiple time points, enabling identification of transient bottlenecks that occur during specific operations or changeovers.
4Productivity
If changeover processes are not optimized, then productivity is reduced, but inefficient movement patterns and poor layout design extend setup times
Solution Approach 1:
The system applies dynamic analysis to changeover processes by tracking and analyzing worker movements specifically during changeover events. The machine learning models identify inefficiencies in changeover sequences and recommend dynamic adjustments to asset locations and workflows that reduce setup time while maintaining productivity during normal operations.
Data Source
AI summary
A ML-based method and system for optimizing industrial processes, is disclosed. The ML-based method includes: obtaining image data and video data associated with objects, from video capturing units installed on industrial floors; obtaining positioning information of the objects from positioning systems configured on the objects; computing trajectories for the objects, using a coordinate transformation module based on streams corresponding to the image data, the video data, and the positioning information; combining the trajectories associated with the objects, into a common coordinate frame, using the coordinate transformation module; determining data associated with tracks, and metadata, for the objects, using the coordinate transformation module; and combining information associated with the data associated with the tracks, and the metadata, to predict optimized industrial processes, using a combiner of a spatio-temporal reasoning engine with a machine learning (ML) model.