AI for the Manufacturing Industry

Leading Manufacturers Deployed Our AI Solutions

AI for Manufacturers: Accessibility, Use Cases, Solutions, Value Created & ROI Impact

At ThirdEye Data, we develop AI solutions for the manufacturing industry to shift their operation from reactive to proactive. Like any business investment seeking higher ROI, manufacturers investing in AI programs are no different. Therefore, we focus on AI applications directly impacting the company’s revenue.  

Our recently developed AI solutions are fundamentally reshaping manufacturing operations by improving overall equipment efficiency or OEE, saving costs with predictive maintenance or PdM of the machines, and enhancing product quality in real time.  

Let us discuss our experience, solutions, and commitment to AI implementation in manufacturing operations. 

Accessibility: Making AI Solutions Accessible to All Sizes of Manufacturers 

Five years ago, when we first developed our AI solution for a manufacturing company, integrating AI solutions was seen as a complex, costly endeavor only feasible for large manufacturers with big budgets.   
 
That makes us think that AI must be accessible to all sizes of manufacturers, as it has a huge prospect of generating revenue. We are committed to democratizing AI for all.  

AI Use Cases in the Manufacturing Industry

ThirdEye has spent over ten years delivering AI-powered solutions and worked on more than 20 use cases for the manufacturing industries. From the AI use cases we have worked on, we have found that the primary business needs in the manufacturing sector center around efficiency, quality, cost management, and adaptability. Here are some of the primary AI use cases in the manufacturing industry: 

Reducing Downtime for the Equipment: Factory AI Use Case  

One of the biggest applications of industrial artificial intelligence is in predicting equipment failures before they occur by analyzing and detecting anomalies in sensor data, historical maintenance records, and operation floors data.  

Manufacturers prioritize minimizing unplanned downtime that disrupts production schedules and reducing the financial burden of unexpected machine breakdowns and lengthy repair times by deploying factory AI solutions. To achieve this business goal, we develop AI-powered predictive maintenance (PdM) solutions that analyze the processed data and generate results in real time. You can find more details on PdM in our solutions section below. 

Enhancing Product Consistency: AI-Powered Quality Control  

Manufacturers need to cut down on material waste and rework costs, which directly affect profitability. Therefore, they are taking two different approaches – Reactive and Proactive. And, the AI solutions are getting deployed in assembly lines, welding, painting, and packaging processes.  

In a reactive approach to maintaining product quality and reducing waste, they prefer to leverage computer vision and deep learning technologies to build AI-driven quality control systems.  These systems automatically inspect products for defects by analyzing image data from machine-mounted cameras, historical product data, and real-time operational data, ensuring they meet precise specifications. This shift from manual inspections to AI-driven processes enhances accuracy, speed, and consistency in detecting defects on production lines. 

With a proactive approach, manufacturers focus on ensuring that measurement systems remain precise and calibrated throughout the production process in real-time. By predicting potential measurement errors early, the integrated system ensures that products are manufactured with tighter tolerances, reducing the likelihood of producing defective parts with such as dimensional inaccuracies or surface irregularities, because they are based on reliable data. To achieve this goal, manufacturers need predictive metrology systems that identify a potential deviation in measurements (e.g., due to tool wear), it can trigger the quality control system to inspect products more rigorously or make real-time adjustments to the machine settings, preventing quality issues. 

Supply Chain Optimization: Smarter Forecasting and Logistics

Supply chain disruptions can be catastrophic for smaller manufacturers. Manufacturers look for an AI-powered system that can forecast demands by analyzing market data, provide real-time analysis reports to enhance inventory management, and offer alternative routes for faster and smoother transition by analyzing GPS data. The business goals are to reduce stockouts and cut inventory holding costs. 

AI-powered supply chain analytics platform empowers manufacturers with agility and flexibility in their operations.  The AI models analyze supply chain data like information on material flow, inventory levels, market demands, and shipment status in real time and recommend solutions. 

AI Solutions for the Manufacturing Industry

Keeping the business goals in mind, we develop customized AI solutions for the manufacturing industry, which are realistic, results-driven, and impact directly on ROI. Our primary AI applications include:

We leverage machine learning, artificial intelligence, computer vision, and IoT technologies to analyze large datasets, identify patterns, and make real-time decisions. Our AI solutions are driving significant improvements across manufacturing operations. 

Let us explore the solutions in detail. 

Predictive Maintenance Flow

Predictive Maintenance

Predictive maintenance also referred to as PdM is the use of AI and machine learning algorithms to detect anomalies in sensor data and to predict when equipment is likely to fail or require maintenance, reducing unplanned downtime and optimizing maintenance schedules. PdM shifts maintenance strategies from reactive, that is fixing machines after breakdowns, to proactive like preventing breakdowns.

Tools & Technologies Used in Predictive Maintenance
  • Machine Learning Algorithms: We train our predictive models on historical and real-time machine data to identify patterns leading to failures. 
  • IoT Sensors: We rely on IoT sensors to collect data from machines, such as vibration, temperature, pressure, and operating speed, to monitor machine health. 
  • Computer Vision: Sometimes, we leverage computer vision technologies for visually inspecting equipment, and detecting surface-level issues such as corrosion, wear, misalignment, or component damage. 
  • Big Data Analytics: The model processes and analyzes large volumes of sensor data in real-time to detect anomalies and degradation patterns. And display the results through the analytical dashboard, powered by PowerBI and Tableau. 
Data We Analyze
  • Sensor Data: By sensor data, we mean the data related to vibration, pressure, temperature, and humidity from the IoT devices attached to machinery. 
  • Visual Data: Our AI models analyze the images or video feeds received from the machine-mounted cameras monitoring equipment surfaces for visible signs of wear, misalignment, or damage. 
  • Historical Maintenance Data: Our system also considers records of past equipment failures, repairs, and maintenance activities. 
  • Operational Data: On-ground performance metrics, usage patterns, and load profiles are important to deliver accurate results.
Business Value Proposition
  • Reduced Downtime: Manufacturers can predict equipment failures before they happen, which enables them to reduce unplanned downtime, and increase overall equipment effectiveness (OEE). 
  • Cost Savings: Optimized maintenance schedules reduce the frequency of unnecessary maintenance tasks and prevent costly emergency repairs. 
  • Extended Equipment Life: Timely maintenance and repair ensure that machines operate under optimal conditions, extending their lifespan. 
  • Improved Worker Safety: Predicting failures reduces the risk of sudden equipment malfunctions, minimizing safety hazards on the factory floor. 
Real-World Impact of Our Predictive Maintenance Solutions
  • Manufacturers have confirmed around 30% reduction in maintenance costs after deploying our predictive maintenance AI models. 
  •  Our clientele from the manufacturing industry reported around a 45% reduction in unplanned downtime post-deployment. 
  • Manufacturers witnessed around 7% increase in production after optimizing machine uptime.

AI-Powered Quality Control System

We use computer vision and machine learning to build AI-powered quality control systems that automatically inspect products for defects, ensuring they meet precise specifications. This shift from manual inspections to AI-driven processes enhances accuracy, speed, and consistency in detecting defects on production lines.

Tools & Technologies Used
  • High-Resolution Industrial Cameras: We deploy machine-mounted industrial cameras to capture detailed images and video streams of products on the production line. They work in conjunction with our computer vision systems to ensure precise visual inspection. 
  • Computer Vision: We develop visual data processing algorithms to analyze products’ defects, like scratches, incorrect assembly, or shape irregularities. 
  • Machine Learning: We train the ML models on large datasets of defective images to classify products as acceptable or defective. 
  • Deep Learning: We also leverage neural networks to identify complex patterns and subtle differences in images that traditional methods might miss. 
Data We Analyze
  • Visual Data: Our computer vision models heavily depend on the visual data captured by the machine-mounted cameras. 
  • Sensor Data: The machine learning models analyze the data from sensors that measure dimensions, weight, temperature, and other physical characteristics of products. 
  • Historical Defect Data: Records of past product defects and production failures are extremely important to train AI models to increase the accuracy rate. 
  • Environmental Data: Our AI-powered quality control systems also consider conditions such as lighting, temperature, and humidity that can affect the quality of products and their visual appearance.
Business Value Proposition
  • Improved Defect Detection: This type of AI-powered quality control system can detect even minor defects with greater accuracy, which manual inspection may miss. 
  • Increased Throughput: Automation of the quality control process allows the manufacturers to maintain or even increase production speed while ensuring consistent product quality. 
  • Reduced Waste: Naturally, early detection of defects helps manufacturers to correct issues in real-time, reducing the number of scrap materials and parts. 
  • Real-Time Feedback: Our system sends real-time feedback to operators and allows for quick adjustments in production processes. 
Real-World Impact of Our AI-powered Quality Control Systems
  • The manufacturers who deployed our quality control systems reported around 70% improvement in accurate defect detection.  
  • The manufacturers have witnessed around 30% reduction in quality variation
  • As per their annual reports, the manufacturers are generating close to 25% less scrap than the earlier process.
Supply Chain Optimization Process

Supply Chain Optimization

Our AI-powered supply chain optimization solutions help manufacturers with demand forecasting, inventory management, and smart logistics approaches by analyzing vast amounts of data from across the supply chain.  

By identifying patterns and trends, the system enables manufacturers to streamline processes, minimize delays, and optimize resource allocation. 

Tools & Technologies Used
  • GPS Data: We use GPS data for real-time tracking of vehicles, shipments, and assets, enabling route optimization and timely deliveries. 
  • NLP (Natural Language Processing): We deploy NLP technologies to automate communication with suppliers, customers, and logistics providers through chatbots, email automation, and intelligent document processing.  
  • LLMs (Large Language Models): LLMs become extremely useful for supply chain optimization to analyze and summarize vast amounts of textual data from reports, contracts, and market research.  
  • Machine Learning Algorithms: Our predictive models built by machine learning algorithms analyze historical demand, inventory levels, and transportation routes to optimize stock replenishment and logistics planning. 
  • IoT Sensors and Cameras: We deploy sensors and cameras on vehicles and shipping containers, inventory stores to monitor conditions like temperature, humidity, and shock levels, ensuring product safety. 
  • Big Data Analytics: We deliver an actionable and insightful supply chain analytics platform by processing large datasets from suppliers, manufacturers, and logistics networks to enable decision-makers enable to identify inefficiencies and find opportunities for optimization across the supply chain. 
Data We Analyze
  • Demand Data: Our system analyzes historical sales data, market trends, and seasonal variations that inform production planning. 
  • Inventory Data: The models need to be updated with real-time data on stock levels, materials in transit, and storage conditions. 
  • Supplier Data: Our system uses data on supplier reliability, lead times, and performance, used to predict and mitigate supply chain risks. 
  • Logistics Data: We feed the model with logistics data for shipment tracking, route optimization, and carrier performance data. 
Business Value Proposition
  • Demand Forecasting: AI improves demand forecasting accuracy, enabling manufacturers to produce the right number of products at the right time. 
  • Inventory Management: Optimized inventory management reduces the need for excess stock, cutting carrying costs while ensuring sufficient materials are available when needed. 
  • Supply Chain Resilience: Our supply chain optimization system anticipates potential disruptions and recommends alternative suppliers or routes, improving supply chain resilience. 
Real-World Impact of Our Supply Chain Optimization Systems
  • With our supply chain optimization system, manufacturers see up to 40% reduction in Stockouts
  • Manufacturers using our supply chain optimization system witness around 23% reduction in overall excess inventory
  • Manufacturers reported that our system predicted or forecasted market demand with 90% accuracy
Predictive Metrology for Controller

Predictive Metrology

Our predictive metrology system is a popular application of AI and machine learning to monitor and predict the calibration needs and accuracy of measurement tools and systems in manufacturing. It combines the principles of predictive maintenance, process control, and metrology or the science of measurement to anticipate measurement deviations and ensure consistent product quality. Predictive Metrology and AI-powered Quality Control are closely integrated, as both aim to enhance product quality and consistency throughout the manufacturing process.

Predictive Metrology is focused on ensuring that measurement systems remain accurate throughout the production process, while AI-powered Quality Control is focused on detecting defects in real-time. 

Tools & Technologies Used: 
  • Machine Learning Algorithms: We develop and train our ML models on historical and real-time data to detect early signs of calibration drift or measurement errors in metrology equipment. 
  • IoT Sensors: Our system processes the data collected by IoT sensors on environmental conditions (e.g., temperature, humidity) and equipment parameters (e.g., wear and tear, alignment) that influence measurement accuracy. 
  • Computer Vision: We use computer vision technologies in non-contact measurement systems (e.g., optical metrology) to detect surface defects or misalignment. 
Data We Analyze: 
  • Measurement Data: We feed the output data from measurement tools such as coordinate measuring machines (CMMs), laser scanners, and gauges. 
  • Environmental Data: Our system considers conditions like temperature, humidity, and vibration that affect metrology equipment’s precision. 
  • Historical Calibration Data: For accurate results, the system needs records of past calibrations, equipment usage, and deviations from accurate measurements. 
  • Machine Operating Data: The system needs data on how long and under what conditions measurement tools have been in use.
Business Value Proposition: 
  • Improved Product Quality: By ensuring continuous precision in measurement tools, manufacturers can produce high-quality parts with fewer defects or rework. 
  • Reduced Rework and Scrap: Accurate measurements from properly calibrated systems prevent errors that lead to costly scrap or rework. 
  • Enhanced Compliance: Predictive metrology helps manufacturers meet industry standards and tolerances, avoiding penalties or recalls for non-compliance. 
  • Cost Savings: Proactively maintaining metrology equipment reduces the cost of unplanned downtime and prevents the need for frequent manual recalibrations. 
Real-World Impact: 
  • Manufacturers using predictive metrology solutions report up to a 40% decrease in parts failing dimensional accuracy checks
  • Predicting when recalibration is truly needed allows manufacturers to see around a 25% increase in calibration intervals
  • Enhanced measurement accuracy results in more parts passing quality inspections the first time, reporting a 25% improvement in first-pass yield

Customer Success Stories

Enhancing Sales Operations for a Fabric Manufacturing Company

Establishing a fully integrated AI system capable of making informed predictions to optimize inventory and sales strategies by integrating existing data sources and external data sources.

Predictive Maintenance & Component Failure Analysis of Aircrafts

Developed a suite of predictive maintenance algorithms to analyze data from various sources to predict aircrafts' component health and optimize maintenance schedules.

Predictive Metrology for Control Systems of Glass Manufacturing

Developed an Open-Loop-System that aids factory operational personnel to control the glass coating process, improves the product quality, and reduces waste.

Battery Life Predictions of Medical Equipments

Built a medical equipment’s battery remaining life prediction system with custom ML models based on early life cycle test data. The model predicted the remaining life in terms of the number of cycles.

Optimizing Manufacturing Operations for Conglomerate’s Manufacturing Company

To leverage AI and optimize the conglomerate’s manufacturing processes across various areas: logistics, supply chain management, distribution, and production itself.

Product Quality Control System for Plywood Manufacturer

Developing an AI-based real time alerting system for the operating personnels to address the issue of maintaining the optimum size of plywood sheets during the manufacturing process.

Planning to Implement AI into Your Factory? Let Us Know.

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