How it Works
How It Works

Solutions

AI/ML for Manufacturing

AI/ML doesn’t work on raw operational data. What’s needed is standardized data. Sight Machine makes plant data ready for data science.

Artificial Intelligence & Machine Learning

Representative AI/ML Applications

Use AI/ML on the Sight Machine platform, or do it yourself on standardized data.

Essex Furukawa

Application

Quality Root Cause Analysis

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Use Case

Analyze product, temperature, speed, and machine setup data to identify factors that affect quality for 4 plants supplying EV vehicles with precision-engineered electrical components.

Techniques Applied

Gradient Boosted Regression

Application

Predictive Quality

Use Case

Combine warranty claim data with production data to do root cause analysis on factors affecting warp in boxboard production.

Techniques Applied

Gradient Boosted Decision Trees

Application

Predictive Quality, Energy use

Use Case

Provide predictions with high degree of accuracy up to three days in advance of potential quality problems in furnace for float glass line. Minimized energy use per unit of saleable glass by optimizing quality.

Techniques Applied

Support Vector Machines

Application

Asset and production scheduling

Use Case

Combine daily production schedules, real-time plant floor data, and highly variable raw material arrival to dynamically optimize asset utilization and minimize energy use.

Techniques Applied

Genetic algorithms

Application

Performance optimization

Use Case

Incorporating client chemistry-based modeling and statistics to create predictions of fermentation time on cheese production time. Used to maximize output and yield while maintaining quality.

Techniques Applied

Monte Carlo simulations, GLM regression

Application

Predictive Maintenance

Use Case

Perform anomaly detection on 300 robots on automated final assembly process (Body Shop) to provide predictions and root cause analysis for downtime.

Techniques Applied

Breakout Analysis: e-divisive with medians (anomaly detection)

Application

Downtime Prevention: Micro-stops in high-speed packaging

Use Case

A simple analysis of alarms was not identifying the true root causes of alarms in packaging lines. Problems had been unresolved for 20 years. Used a combination of AI techniques to trace problems to true root causes in real-time.

Techniques Applied

NLP, Agglomerative clustering,
Sequence analysis

Application

Predictive Downtime

Use Case

Analysis of 500 million data points from 600 welding robots to provide early warning of potential downtime and other welding faults.

Techniques Applied

Functional Data Analysis

Application

Downtime prevention: Microstop and alarm analysis to identify root cause of downtime

Use Case

Analysis of 10,000 alarms across automated, integrated contact lens line. Identified causation of frequent micro-stops with high level (95% +) predictive power.

Techniques Applied

NLP, Agglomerative clustering, Sequence analysis

Application

Quality: Computer vision inspection on vehicle final assembly line at a $12B/yr Detroit assembly plant.

Use Case

Continuous inspection on the final assembly line for Jeep Grand Cherokees and Dodge Durangos: 1,100 vehicles per day, inspecting 15 exterior elements, 25+ models, 11 colors, 24/7 operation, 99.9% accuracy. Integrating MES data, image analysis, ML and edge/cloud compute.

Techniques Applied

Machine learning continuously applied to large image sets analyzed with Simple CV, a company – developed open source CV framework

Manufacturing’s Opportunity is to Go Beyond Pilots and Deliver Scale, Sustainability and Impact

Pilot Purgatory

Among enterprise manufacturers, the case for AI has been made. Still, despite the many billions invested, manufacturers remain mired in “Pilot Purgatory.”

The reason for Pilot Purgatory is poorly understood. It is not that manufacturers don’t believe in AI. Data is abundant, manufacturers have moved plant data to Cloud, and good algorithms and models are widely available. Instead, the main problem is the state of manufacturing data itself. Unlike modern data in most business activities, plant data is not “IT ready” and is not suited to the application of AI.

What’s Needed to Drive AI at Scale?

What’s the breakout move? Standardized data.

What creates standardized data? Standardized models constructed through stream processing.

What else is needed by a manufacturer to manage hundreds of stream processing pipelines concurrently? Pipeline management tools.

Real Outcomes In Weeks

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