Case Study
Case Study
Industry.AI has deployed 3 of its products across the automotive sector – Predict.AI, Conserve.AI and Trust.AI.
Industry.AI has deployed 3 of its products across the automotive sector – Predict.AI, Conserve.AI and Trust.AI.
“Predict.AI” uses advanced AI and deep learning tools to improve throughput, and machine availability by using condition-based monitoring in manufacturing.
“Conserve.AI” uses AI to enhance energy efficiency and drive savings as well as reduce emissions in manufacturing.
“Trust.AI” uses advanced vision AI and deep learning to provide insights related to operations, safety, security and quality. It helps improve efficiency across airport operations and various industries.
“Predict.AI” uses advanced AI and deep learning tools to improve throughput, and machine availability by using condition-based monitoring in manufacturing.
“Conserve.AI” uses AI to enhance energy efficiency and drive savings as well as reduce emissions in manufacturing.
“Trust.AI” uses advanced vision AI and deep learning to provide insights related to operations, safety, security and quality. It helps improve efficiency across airport operations and various industries.
In the fast-paced auto component industry, ensuring high-quality production, efficient operations, and proactive maintenance are crucial for quality manufacturing and sustained success. This blog highlights how one of the largest global auto component companies overcame disruptions in production quality, deteriorating machinery health, and manual inspection inefficiencies by harnessing the power of Industry.AI’s products. By addressing these challenges head-on and adopting a data-driven approach, the company achieved remarkable improvements in its production process.
In the fast-paced auto component industry, ensuring high-quality production, efficient operations, and proactive maintenance are crucial for quality manufacturing and sustained success. This blog highlights how one of the largest global auto component companies overcame disruptions in production quality, deteriorating machinery health, and manual inspection inefficiencies by harnessing the power of Industry.AI’s products. By addressing these challenges head-on and adopting a data-driven approach, the company achieved remarkable improvements in its production process.
Challenges
Challenges
The auto component company encountered several critical challenges that impacted its production process:
The auto component company encountered several critical challenges that impacted its production process:
Batch Production Quality Issues: The company faced high failure rates and anomalies in its batch production, leading to delays and customer dissatisfaction. These quality issues resulted in increased costs due to rework and warranty claims.
Batch Production Quality Issues: The company faced high failure rates and anomalies in its batch production, leading to delays and customer dissatisfaction. These quality issues resulted in increased costs due to rework and warranty claims.
Deteriorating Rough Press Health The rough press, a vital machinery component, experienced deteriorating health due to delayed maintenance and inaction on critical issues. This led to frequent breakdowns, production downtime, and compromised output quality.
Deteriorating Rough Press Health The rough press, a vital machinery component, experienced deteriorating health due to delayed maintenance and inaction on critical issues. This led to frequent breakdowns, production downtime, and compromised output quality.
Manual Inspection Inefficiencies: The heavy reliance on manual inspection processes contributed to low productivity and operational inefficiencies. Manual inspections were time-consuming, error-prone, and unable to meet the growing production demands. This resulted in high operational and maintenance costs.
Manual Inspection Inefficiencies: The heavy reliance on manual inspection processes contributed to low productivity and operational inefficiencies. Manual inspections were time-consuming, error-prone, and unable to meet the growing production demands. This resulted in high operational and maintenance costs.
Machine downtime, Low OEE, and high maintenance costs: Auto OEMs rely on just in time delivery of components, and cost competitive supply chain. Therefore, there is constant pressure to ensure there is no down time in operations, and to maintain a high OEE in manufacturing.
Machine downtime, Low OEE, and high maintenance costs: Auto OEMs rely on just in time delivery of components, and cost competitive supply chain. Therefore, there is constant pressure to ensure there is no down time in operations, and to maintain a high OEE in manufacturing.
Industry.AI Approach
Industry.AI Approach
To address these challenges, the company implemented a data-driven approach using Industry.AI’s Orion platform, along with 3 of its core AI products – Predict.AI, Conserve.AI and Trust.AI. The approach included the following key steps:
To address these challenges, the company implemented a data-driven approach using Industry.AI’s Orion platform, along with 3 of its core AI products – Predict.AI, Conserve.AI and Trust.AI. The approach included the following key steps:
Data Integration and Collection
Integrated Industry.AI with the company's existing systems to collect data from various sources, including production lines, machinery, and inspection processes.
Captured real-time and historical data to gain comprehensive insights into production quality, machinery health, and operational performance.
Data Integration and Collection
Integrated Industry.AI with the company's existing systems to collect data from various sources, including production lines, machinery, and inspection processes.
Captured real-time and historical data to gain comprehensive insights into production quality, machinery health, and operational performance.
Advanced Analytics and Machine Learning
Leveraged Industry.AI's advanced analytics capabilities to analyze and identify patterns, anomalies, and root causes of production failures.
Employed machine learning algorithms to develop predictive models for identifying potential machinery breakdowns and optimizing maintenance schedules.
Advanced Analytics and Machine Learning
Leveraged Industry.AI's advanced analytics capabilities to analyze and identify patterns, anomalies, and root causes of production failures.
Employed machine learning algorithms to develop predictive models for identifying potential machinery breakdowns and optimizing maintenance schedules.
Proactive Maintenance and Predictive Analytics
Utilized Industry.AI's predictive analytics to monitor the health of the rough press and other critical machinery components.
Implemented proactive maintenance strategies based on real-time data and insights to address issues before they escalated into breakdowns or production disruptions.
Proactive Maintenance and Predictive Analytics
Utilized Industry.AI's predictive analytics to monitor the health of the rough press and other critical machinery components.
Implemented proactive maintenance strategies based on real-time data and insights to address issues before they escalated into breakdowns or production disruptions.
Automated Inspection and Quality Control
Integrated Industry.AI with automation technologies such as machine vision systems and sensors to automate inspection processes.
Employed AI algorithms to analyze images and sensor data in real-time, ensuring faster and more accurate inspections and reducing reliance on manual labor.
Automated Inspection and Quality Control
Integrated Industry.AI with automation technologies such as machine vision systems and sensors to automate inspection processes.
Employed AI algorithms to analyze images and sensor data in real-time, ensuring faster and more accurate inspections and reducing reliance on manual labor.
Benefits
Benefits
By adopting Industry.AI and implementing a data-driven approach, the company achieved significant benefits:
By adopting Industry.AI and implementing a data-driven approach, the company achieved significant benefits:
Improved Production Quality:
The use of Industry.AI’s advanced analytics and predictive models helped identify and address root causes of production failures and anomalies. This led to a substantial reduction in quality issues, enhancing customer satisfaction, and reducing costs associated with rework and warranty claims.
The use of Industry.AI’s advanced analytics and predictive models helped identify and address root causes of production failures and anomalies. This led to a substantial reduction in quality issues, enhancing customer satisfaction, and reducing costs associated with rework and warranty claims.
Enhanced Machinery Health and Downtime Reduction:
By leveraging Industry.AI’s predictive analytics, the company implemented proactive maintenance measures for the rough press and other critical machinery components. This resulted in reduced breakdowns, minimized production downtime, and improved overall equipment effectiveness.
By leveraging Industry.AI’s predictive analytics, the company implemented proactive maintenance measures for the rough press and other critical machinery components. This resulted in reduced breakdowns, minimized production downtime, and improved overall equipment effectiveness.
Increased Operational Efficiency and Cost Savings:
Automation of inspection processes using Industry.AI led to improved productivity, reduced manual labor requirements, and faster inspection times. This increased operational efficiency and significantly lowered operation and maintenance costs.
Automation of inspection processes using Industry.AI led to improved productivity, reduced manual labor requirements, and faster inspection times. This increased operational efficiency and significantly lowered operation and maintenance costs.
Real-time Monitoring and Decision Making:
Industry.AI’s real-time data collection and analytics capabilities provided the auto component company with granular visibility into production processes, machinery health, and quality control. This facilitated timely decision-making, enabling the company to proactively address issues and optimize operations.
Industry.AI’s real-time data collection and analytics capabilities provided the auto component company with granular visibility into production processes, machinery health, and quality control. This facilitated timely decision-making, enabling the company to proactively address issues and optimize operations.
Improved Production Quality: The use of Industry.AI’s advanced analytics and predictive models helped identify and address root causes of production failures and anomalies. This led to a substantial reduction in quality issues, enhancing customer satisfaction, and reducing costs associated with rework and warranty claims.
Enhanced Machinery Health and Downtime Reduction: By leveraging Industry.AI’s predictive analytics, the company implemented proactive maintenance measures for the rough press and other critical machinery components. This resulted in reduced breakdowns, minimized production downtime, and improved overall equipment effectiveness.
Increased Operational Efficiency and Cost Savings: Automation of inspection processes using Industry.AI led to improved productivity, reduced manual labor requirements, and faster inspection times. This increased operational efficiency and significantly lowered operation and maintenance costs.
Real-time Monitoring and Decision Making: Industry.AI’s real-time data collection and analytics capabilities provided the auto component company with granular visibility into production processes, machinery health, and quality control. This facilitated timely decision-making, enabling the company to proactively address issues and optimize operations.
Results
Results
Steady State:
Identification of steady state using AI models which forms the base for CBM.
Identification of steady state using AI models which forms the base for CBM.
No Failure:
Identification of anomalies in advance and predicting failures based on past failure history.
Identification of anomalies in advance and predicting failures based on past failure history.
Cost Reduction:
20% reduction in maintenance cost.
20% reduction in maintenance cost.
Steady State:
Identification of steady state using AI models which forms the base for CBM.
Identification of steady state using AI models which forms the base for CBM.
No Failure:
Identification of anomalies in advance and predicting failures based on past failure history.
Identification of anomalies in advance and predicting failures based on past failure history.
Cost Reduction:
20% reduction in maintenance cost.
20% reduction in maintenance cost.
Conclusion: The company’s adoption of Industry.AI and a data-driven approach revolutionized its production processes, resolving challenges related to production quality, machinery health, and manual inspection inefficiencies. Through improved production quality, enhanced machinery health, increased operational efficiency, and cost savings, the company gained a competitive edge in the industry and positioned itself for sustained growth. By harnessing the power of Industry.AI, the auto component company demonstrated the transformative potential of AI and advanced analytics in overcoming complex manufacturing challenges.
Conclusion: The company’s adoption of Industry.AI and a data-driven approach revolutionized its production processes, resolving challenges related to production quality, machinery health, and manual inspection inefficiencies. Through improved production quality, enhanced machinery health, increased operational efficiency, and cost savings, the company gained a competitive edge in the industry and positioned itself for sustained growth. By harnessing the power of Industry.AI, the auto component company demonstrated the transformative potential of AI and advanced analytics in overcoming complex manufacturing challenges.