Trust List of News Articles

Due to the retirement of veteran engineers (the 2025 problem), control programs are becoming black boxes... There is no time to teach ladder diagrams to younger or new employees, nor are there learning opportunities in educational institutions... The 'personalization of programs and development burden' lurking in the field may actually be significantly hindering the promotion of DX in factories and productivity. This document explains the latest methods and practical models for accelerating and streamlining program generation, comprehension, and standardization in PLC program development in manufacturing using generative AI! ▼ What you will learn in this seminar 【Reducing the burden of control development】 The latest trends in automatic generation of PLC code and ladder diagrams using general-purpose LLMs, dedicated LLMs, and manufacturer-specific Copilots. 【Knowledge transfer and eliminating black boxes】 Analyzing and visualizing personalized legacy code and past assets with AI to achieve standardization of veteran know-how and reduce education costs. 【Compliance with language standards (IEC 61131-3 4th edition)】 Understanding the compatibility of ST language and ladder diagrams with AI, and presenting a practical workflow that can be used immediately in the field. 【Comparison of major vendors and case studies】 Introducing the latest AI features from Mitsubishi Electric, Siemens, Rockwell, and examples of Copilot integration.

"Even though the processing costs in the catalog are the same, there are products that somehow do not generate profit..." "We are unable to grasp the causes of machine stoppages (short stops and long stops) or the speed differences among operators..." The "invisible losses" lurking in the workplace may actually be significantly squeezing the factory's profits. This document explains a method to dramatically enhance the profitability and competitiveness of the workplace by automatically acquiring the "operating time" of processing machines and the "working time" of operators, and cross-analyzing them on the same time axis! ▼ What you will learn in this seminar 【Reduce equipment downtime】 Identify priority measures for short and long stops, significantly reducing machine downtime 【Highlight unprofitable products】 Calculate the "net actual cost" from equipment and personnel data, leading to evidence-based price negotiations 【Knowledge transfer and leveling】 Visualize the work differences between skilled workers and newcomers (e.g., screw tightening) to eliminate bottlenecks in the process 【Case study of a press processing factory】 A practical model from daily report automation to data collection using RFID and QR codes ▼ Event Overview Date: November 13, 2026 (Friday) 13:30 - 14:30 Format: Online (Teams webinar) Participation fee: Free

[Reforming Manufacturing Sites] "Invisible Inventory" and "Manual Errors" Solved by AI! The Cutting Edge of DX in Inventory Management and Logistics Main Text: "Searching for where everything is takes too much time..." "Ordering based on experience and intuition leads to overstock and stockouts..." Do you face these issues in your manufacturing site? This document introduces the latest examples of automating inbound and outbound processing and achieving real-time visibility of inventory data by utilizing camera imaging, barcodes, QR codes, and AI! ▼ Three Key Points Explained in the Document Instant reading with batch imaging! Automate inbound processing and data updates to significantly reduce work time. AI automatically determines the optimal storage location! Eliminate waste in searching and moving, maximizing storage efficiency. [Case Study] A unified management model from procurement to the reuse of processed scrap in a plastic product processing factory. This is the definitive guide to DX that covers data integration with WMS and production management systems, ready for immediate application in the field. Please download and read it! ▼ Event Overview Date: October 9, 2026 (Friday) 13:30 - 14:30 Format: Online (Teams Webinar) Participation Fee: Free

【What if a massive earthquake halts production at factories, resulting in losses of hundreds of millions of yen!?】 Revealing successful case studies of "DX × Earthquake Countermeasures" to protect the future of manufacturing! A massive earthquake is not something that "will happen someday," but rather "can happen at any time." If a major earthquake were to occur now and halt production lines in factories... businesses would face critical risks to their survival (losses ranging from millions to billions of yen) due to not only damage to buildings and equipment but also delays in delivery and withdrawal of business partners. This document presents real examples of "actual loss amounts and reconstruction costs" that occur in factories during an earthquake, based on data from the Great East Japan Earthquake and the Kumamoto Earthquake. ▼ What you will learn in this seminar Actual damages during an earthquake: Real data on sales losses and reconstruction costs associated with factory shutdowns On-site action guide: A chronological action plan and checklist to follow without hesitation from initial response to recovery Safety measures utilizing DX and AI: The mechanism and estimated costs of systems that detect shaking and automatically stop equipment and enable remote recovery Keys to successful business continuity: How to effectively use earthquake insurance to expedite recovery and case studies from advanced companies ▼ Event Overview Date: September 11, 2026 (Friday) 13:30 - 14:30 Format: Online (Teams Webinar) Participation fee: Free

On September 16, 2026 (Wednesday), we will hold the "4th Manufacturing and Infrastructure AI Study Group & Meetup: Success Stories of Field Reform Expanded by Practice × AI" on-site. On that day, we will welcome special guests and conduct various lectures themed around the current state of AI utilization in the manufacturing and infrastructure industries. Additionally, we plan to hold a Meetup (networking event) for all participants after the event. We look forward to your enthusiastic participation.

[For the manufacturing industry / Free participation] AI visual inspection is not just about "detecting and ending." A method to identify defect causes from inspection data and link it to process improvement. In manufacturing sites, there are various challenges in the inspection process, such as labor shortages, skill transfer, and variability in visual inspections. As a response to these challenges, the use of AI visual inspection is advancing, but simply determining good and defective products with AI does not lead to process improvement. What is important is to accumulate and analyze inspection data to clarify "when, where, and why defects are occurring." In this seminar, we will introduce specific examples from the basics of AI visual inspection to methods for analyzing defect causes using inspection data, as well as how to improve upstream processes and prevent recurrence.