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Posted by Sadia Waseem
The manufacturing industry is constantly modifying its traditional methods through integrating cutting-edge technology into operations. Artificial Intelligence (AI) has emerged as the most prominent technology in the modern manufacturing era. The use of AI is constantly increasing in manufacturing through digital twin technology, quality control, supply chain management AI, copilots, etc. With the increasing use of AI in manufacturing, its market share is estimated to grow at the rate of 45.6%, jumping from 3.2 billion in 2023 to 20.8 billion by 2028.
Among this cutting-edge AI technology, the AI copilot stands out as an AI-based assistant that facilitates manufacturing floor operations. These systems collaborate with humans to guide and validate the process using machine learning and advanced computer vision. AI copilots address critical manufacturing issues like bad quality, training inefficiencies, and production errors through real-time guidance and verification. In this article, we will explore AI copilots in detail and how they can transform modern manufacturing operations.
After all these years of process optimization, manufacturing operations continue to face significant challenges. Traditional methods struggle to mitigate these challenges effectively. The most common ones include constant attention and monitoring during the assembly process. Workers are prone to overlook errors due to varying performance levels and fatigue. Studies show that 23% of unplanned downtime is due to human errors. This high error rate is mostly due to inconsistent quality control since manual inspection heavily depends upon individual attention and experience.
Furthermore, the complex modern manufacturing environment further elevates these challenges. Repeatedly training new operators is a time-consuming and resource-intensive job that often leads to operational bottlenecks. This lack of proper training causes errors to go undetected, creating problems in the later stages of production. It can result in more costly rework and sometimes defective products. A study held in the UK in 2011 related to human errors concluded that inadequate training contributes to 21% of the total errors. Product complexity continues to rise, making operator training more challenging than ever before.
Another critical challenge is the lack of standardization across different shifts in manufacturing. Operators often develop their own way of working, usually in the form of shortcuts. These variations cause inconsistencies in procedures, leading to different variants of products. Without proper monitoring and guidance, different shifts can produce varying quality levels of the same product. For instance, a study highlighted that insufficient testing and poor process control, often due to a lack of standardization, contribute to product defects. This variation in processes makes it difficult to identify the root cause of quality issues and implement effective improvements.
These challenges, combined with increasing market demands, create a need for intelligent automation solutions. Manufacturers are constantly looking for solutions that provide real-time feedback as they face constant pressure to improve quality while maintaining efficiency. The traditional process of manual inspection and paper-based procedures do not meet the high manufacturing complexity and demands. This has created a significant gap to integrate intelligent solutions like AI Copilots to address these challenges and automate the feedback and validation process.
Traditional automation processes generally focus on replacing human operators, whereas AI copilots are specifically designed to assist human operators and make their jobs easier. AI copilots integrate computer vision and advanced machine learning algorithms to assist in manufacturing. Let’s look at how they operate to address critical manufacturing challenges:
Step 1: Visual Monitoring
AI copilot begins working with advanced cameras that continuously monitor the manufacturing floor. These cameras capture every moment of the workers, the part’s movement, and the entire process. This process is like a supervisor who watches an entire assembly line and ensures everything works smoothly. Multiple cameras positioned at different angles ensure complete work area coverage, leaving no blind spots in the monitoring process.
Step 2: Image Processing and Recognition
The captured visual data is immediately processed through computer vision algorithms. Based on their training, the system processes and recognizes different components, tools, and assembly stages. Just as an experienced supervisor can quickly spot correct and incorrect procedures, the AI copilot instantly recognizes various parts, tools, and assembly steps. For example, during circuit board assembly, it can instantly recognize if an operator has picked the correct component or if a part is oriented properly before placement.
Step 3: Real-time Analysis
The processed data is analyzed by machine learning models. These models are trained on various correct assembly procedures to evaluate whether each step is followed correctly and according to Standard Operating Procedure (SOP). For example, during an automotive harness assembly, it validates whether the connections are fully integrated or whether wire routing follows the specified path. These systems can detect deviations, missing components, or incorrect placement of part in a fraction of a second.
Step 4: Immediate Feedback
Once the system detects deviations from the standard procedures, it immediately sends a visual or audio cue to alert the operators. These alerts could be in the form of displaying the correct part location on the screen or triggering sounds for incorrect actions. This real-time feedback helps to prevent errors even before they occur to ensure consistent product quality and maintain production standards.
Step 5: Data Collection and Analytics
AI copilots collect data about the assembly process throughout the complete process. This data includes product completion time, error rates, improvement areas, or quality issues. The information is utilized to gain valuable insights into the operations to optimize the process and drive continuous improvement. For example, managers can identify which assembly steps consistently take longer across different shifts, indicating opportunities for process improvement or additional training.
This comprehensive framework enables AI Copilot to address key manufacturing challenges while providing valuable insights for continuous improvement.
Now that we understand the working mechanism of AI copilots, it is important to explore the benefits associated with using AI copilots to utilize the technology effectively:
AI copilots facilitate continuous production by providing real-time feedback. The real-time feedback allows workers to work without delays or cause any unplanned downtime. The system’s instant validation and guidance accelerate assembly processes while maintaining quality standards. According to research published by Cornell University, AI copilots tend to improve productivity by 55% in manufacturing operations. Overall, all these factors contribute to improved productivity with increased output.
AI copilots produced large amounts of data through constant monitoring of the process. This data can be turned into an opportunity for improvement by analyzing it and extracting actionable insights from it. This way, the decision-making process is improved because the decisions are taken through data analysis. A study published in the Information Technology and Management Journal highlights that AI technologies facilitate improved decision-making in production by providing advanced data analytics, predictive insights, and real-time monitoring capabilities.
AI copilots act as quality inspectors, continuously validating each step against the established standards. Constant monitoring ensures that each step is followed correctly, and each component is selected and positioned. As explained in a detailed review, these systems monitor production processes in real time and use advanced analytics to catch defects before they become costly problems. This proactive approach ensures consistent quality is maintained across the shifts with fewer customer returns or warranty claims.
AI copilots are changing the conventional operator training methods by serving as virtual instructors who provide step-by-step guidance. According to recent research, AI copilots can automate the training process by providing individual training and adjusting the pace according to each individual’s needs. This intelligent assistance reduces training time without the need to train operators about new processes repeatedly.
These benefits demonstrate the capabilities of AI to improve traditional manufacturing processes through real-time feedback, data analytics, and intelligent guidance.
AI Copilots have proven to be effective in diverse industries. Here are some notable examples that show the impact of AI copilots in manufacturing:
Manufacturing Sector | Use Case | AI Copilot Implementation | Impact |
Automotive (Khenda, | Process optimization | | Productivity increased by 25% |
Waste and defective products are reduced, eliminating error costs. | |||
Electronics (Ethon AI, | Quality inspection in assembly verification | | Quick implementation with 10 times less effort |
Consistent and reliable performance while enhancing overall equipment effectiveness | |||
Medical (Körber Digital, | Quality | Recognizing the patterns instead of measuring physical image values to decrease the false-reject of products | Reduction of false-reject rate by |
Lead time reduced by 50% |
These cases demonstrate the effectiveness of AI copilots in diverse industries for improved productivity, efficiency, and quality. Various companies offer AI copilot solutions, and Retrocausal stands out due to its distinctive features and capabilities.
Retrocausal offers two AI Copilots, Kaizen Copilot, and Assembly Copilot. Assembly Copilot improves quality and compliance through real-time monitoring, whereas Kaizen Copilot facilitates industrial engineers by automating tasks to increase productivity. Here are some distinct features offered by Retrocausal’s AI Copilots:
Assembly Copilot provides real-time feedback to operators. The feedback minimizes errors and improves quality and compliance issues in the final product.
Kaizen Copilot conducts automated time and motion studies using advanced computer vision technology. By analyzing a single video of a cycle, the system offers instant recommendations, facilitating quicker implementation of improvements.
Ergo Copilot increases the safety and wellbeing of workers through automated ergonomics risk assessments. It analyzes the whole process and develops ergonomic metrics based on the video to guide any associated risk.
Retrocausal’s AI Copilot is easy to implement and integrate with the existing system within a few hours. Assembly Copilot works with existing tools like barcode scanners and light towers, while Kaizen Copilot requires only a smartphone or web camera.
All these features make Retrocausal’s AI copilot an innovative AI solution to improve productivity, safety, and quality of the manufacturing process.
Manufacturers require more than traditional automation tools to keep up with the competitive market. They require intelligent assistance that facilitates humans and enhances their capabilities rather than replacing them. AI copilots have proven to be effective across industries, with increasing productivity, quality, faster lead times, and a reduction in false rejections.
Retrocausal’s AI Copilot provides this intelligent assistant through real-time operator feedback, AI-powered time studies, and automated ergonomic assessments. With seamless integration capabilities and proven results, it addresses the core challenges of quality, productivity, and standardization that manufacturers face today. Ready to experience all these features to achieve operational excellence?
Book a free demo today!
Brandin and his team help our customers with setting up the Pathfinder platform and getting up-to-speed on all the features and functionalities, in addition to assisting them with standardizing their processes where needed.
Brandin is a seasoned manufacturing leader, who has spent more than a decade designing and setting up assembly lines in both the US and China, optimizing them, and working with human operators to maximize productivity and quality.
Jesse works closely with our marquis customers to understand their needs, and figures out the best solution that immediately adds dollars to their bottom line.
Jesse has two decades of enterprise sales experience. He has held sales leadership roles at pioneering technology startups.
Andrey takes inventions and converts them into innovations. He leads our product engineering team to turn our scientific prototypes into robust customer-facing products.
Andrey co-founded and successfully exited a startup to immigrate to the US. He spent his next eight years building the core data science infrastructure for Amazon’s supply chain planning team, and the CV/ML infrastructure for Microsoft’s HoloLens organization.
Zeeshan focuses on delighting our customers by understanding their needs and defining product and strategy to meet and exceed these needs. He works closely with our sales leadership to grow our customer base.
Zeeshan was a scientific leader at Microsoft’s HoloLens division, where he invented AI technologies to assist frontline workers across industries. Earlier, he shipped IoT platforms and visual defect detection software for manufacturers at Siemens and MVTec GmbH.
Huy takes our product roadmap and figures out what’s achievable with today’s AI advances, and what we need to invent to solve our customers’ pain points.
Huy led teams of scientists and engineers at NEC and invented technologies for driver assistance systems that are part of 500,000+ Honda cars. He is an inventor on more than 20 US patents, and his research papers are cited by every lab from MIT, Stanford, and CMU to Apple, Google, and Baidu.
