[Case Study] Work Analysis of Yokogawa Manufacturing's Production Site Using AI
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[Free Case Study Available] Discover efficiency improvement points in the assembly processes of the world's six major manufacturers using AI! Results of the comparison between veterans and newcomers included!
Facing significant challenges in the manufacturing industry, Yokogawa Manufacturing Corporation has implemented Copy's work analysis AI, achieving remarkable results. This case study introduces the detailed processes and specific outcomes. 【Challenges Addressed】 1. Personalization of tasks 2. Issues in human resource development and technology transfer 3. Workload of operators 4. Variability in quality 【Some of the Effects of Implementation】 1. Early detection of process anomalies: Using Copy's AI technology, outliers in process cycle times were identified, uncovering process anomalies such as missed preparations for necessary parts. 2. Realization of efficiency: Waiting times for assembly equipment were identified, and efficient cart operations by veteran workers were discovered. The work efficiency of veterans and newcomers was compared, clarifying each group's strengths and areas for improvement. 3. Comparative analysis of workers: The movements of newcomers and veterans were analyzed over approximately 200 cycles, clarifying differences in work efficiency and areas for improvement. Through these initiatives, reductions in work time, improvements in quality consistency, and contributions to human resource development have been achieved, establishing new standards for productivity improvement and quality management across the manufacturing industry. For detailed case studies, specific data, and analysis results, please download and view the "Related Catalog."
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basic information
【Specifications】 - AI Technology: High-precision motion recognition and time measurement technology - Supported Data Formats: Recorded video data (compatible with various formats) - Analysis Time: Analyzes several hours of video in approximately 20 minutes - Analysis Content: Identification of the worker's hands and objects, measurement of action time, detection of anomalies - Output Results: List of improvement candidates, identification of efficiency points, comparative analysis report among workers 【Details of the Implementation】 - Data Collection: Recorded actual work scenes to gather data for detailed analysis by AI. - AI Analysis: Analyzed the collected video data using high-precision motion recognition and time measurement technology. - Anomaly Detection: Identified outliers in process cycle times by recognizing the worker's hands and objects and measuring the time taken for each action. - Discovery of Efficiency: Identified waiting times in assembly equipment and discovered efficient cart operations by veteran workers. - Comparative Analysis: Analyzed approximately 200 cycles of actions by newcomers and veterans to clarify differences in work efficiency and areas for improvement.
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Applications/Examples of results
1. Optimization of the Production Process - Early Detection of Process Anomalies: Analyze the actions of workers in detail and identify outliers in the process cycle time. Early detection of potential process anomalies allows for quick improvements in preparation oversights and inappropriate work procedures. - Identification of Improvement Points: Based on the data obtained from the analysis results, identify specific improvement points to enhance the efficiency of the production process. 2. Human Resource Development - Motion Comparison Analysis: Conduct a detailed comparative analysis of the actions of new and veteran workers to clarify differences in work efficiency and areas for improvement. Effectively share the knowledge and experience of veterans. - Creation of Effective Training Programs: Based on the analysis results, create training programs tailored to individual workers to support skill enhancement. 3. Quality Control - Data-Driven Quality Improvement: Conduct detailed data analysis to reduce quality variation and improve product consistency. - Continuous Quality Evaluation: Perform regular data analysis to support ongoing improvements in quality management. 4. Promotion of Efficiency - Reduction of Waiting Time: Identify waiting times in assembly equipment, optimize cart operations, and enhance equipment efficiency to improve overall work efficiency. - Improvement of Work Efficiency: Compare the actions of veteran and new workers to develop work procedures that leverage each individual's strengths, thereby enhancing overall work efficiency.
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Company information
Our company is an AI startup founded by researchers from the University of Tokyo and the French research institution Inria, with a mission to "improve quality and efficiency through advanced AI technology" specifically for the manufacturing and logistics industries. Our technology meets the needs of quality control, production technology, and DX promotion departments, enabling optimization of production processes and risk management. The founder, Yamamoto, has conducted AI research at Yahoo Japan and Inria, presenting results at top international conferences such as WWW and RecSys. Over 80% of our team consists of researchers who studied AI and computer science at world-class universities like the University of Tokyo, Cambridge, and Imperial College. With their international perspectives and advanced skills, we provide innovative solutions to quality control challenges. In particular, in mission-critical areas that emphasize the interpretability of AI, our XAI (explainable AI) technology brings transparency to the reasoning behind AI decisions for managers in quality control and production technology departments, supporting safer and more reliable system operations. This leads to improvements in production efficiency and quality, promoting sustainable growth in the manufacturing and logistics industries.