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Many sites rely on human experience and perception to inspect manufacturing equipment, but to improve operational efficiency and accuracy, it is essential to effectively utilize IoT data. Attention should be paid to IoT solutions that achieve condition-based maintenance of equipment through multi-sensing.
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This document introduces eight case studies where common challenges in manufacturing, such as yield improvement, inspection and equipment maintenance efficiency, and chocolate stops, have been resolved through IoT implementation. Each case study utilizes four technologies and systems available through our IoT solution 'LOSS0': data collection and sensing, data processing, data analysis, and notification/visualization. It also includes a diagnostic chart that combines "challenges in advancing IoT implementation" with "challenges in the field that need improvement," allowing companies to understand their current status of IoT implementation and future direction. [Featured Case Studies] ◎ Reduction of time for identifying defect causes ◎ Reduction of inspection operations (labor-saving) ◎ Improvement of inspection quality ◎ Efficiency of equipment maintenance tasks ◎ Reduction of investigation operations for chocolate stop causes ◎ Reduction of equipment downtime ◎ Identification of improvement areas in bottleneck processes ◎ Identification and specification of challenges *For more details, please refer to the document. Feel free to contact us with any inquiries.
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Are production managers facing the following challenges? - Want to identify the causes of production schedule delays. - Want to quickly detect problems during production. - Want to achieve planned production and reduce excess inventory. The "abnormal detection and recording solution for manufacturing sites for production managers" provided by 'LOSSØ' has the following features: 【Features】 - Automatically collects cycle times when producing products. - Analyzes cycle times to quickly detect production anomalies. - Records and allows confirmation of the manufacturing site conditions before and after anomalies occur. As a result, production managers can gain the following benefits: - Quickly detect anomalies in the manufacturing site remotely and understand their impact on the plan. - Develop plans that take into account the conditions of the manufacturing site when considering production schedules. - Implement specific improvement measures based on footage from when problems occur. *For more details on the solution, please refer to the materials available for "PDF download."
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NTC offers the IoT solution 'LOSS0 IoT Series' for the manufacturing industry. 'LOSS0' enables the collection and visualization of data from PLCs (programmable logic controllers) that control equipment and external sensors with simple operations. We support customers who are hesitant to take the first step towards IoT implementation in their factories by advising them on what data to collect and how to utilize it. Additionally, by leveraging statistical analysis and AI, we analyze the various data collected to identify actual process times and detect early signs of equipment failures and quality issues. We assist customers facing challenges in utilizing data to enhance their factory IoT capabilities. NTC's LOSS0 IoT Series can be utilized by customers such as: ■ Those who want to start monitoring and controlling equipment with minimal investment ■ Those who want to install external sensors and begin visualization immediately ■ Those who want to start monitoring equipment in an environment with multiple PLC manufacturers ■ Those who want to analyze existing accumulated equipment data to see what failure signs can be detected, etc. *For more details, please refer to the PDF materials or feel free to contact us.
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In recent years, there has been an increase in cases where AI and IoT are used to solve the concerns of production sites, such as "I want to identify the root causes of equipment troubles" and "I want to detect equipment troubles a little earlier." However, considering costs, many companies may find it difficult to take that first step. NTC offers an AI-based predictive maintenance solution that allows for a small start to meet the needs of customers who say, "I want to utilize IoT, machine learning, and artificial intelligence (AI) for predictive maintenance of equipment, but I want to keep costs down..." This solution creates "rules" from past data regarding potential trouble cases and predicts whether future data will lead to troubles. When a predictive result detects a sign of trouble, it can facilitate early detection of issues and predictive maintenance by sending emails to operators or activating warning lights on-site. These mechanisms allow customers to accumulate know-how within their organization as a unique AI foundation, and by implementing a PDCA cycle, it is possible to improve the detection of more advanced troubles and enhance detection accuracy. *For more details, please download the catalog or contact us.*
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