Join the Manufacturing World Tokyo 2024 Conference and Discover the...

Join the Manufacturing World Tokyo 2024 Conference and Discover the Future of Manufacturing

Manufacturing Technology Insights | Wednesday, June 12, 2024

Get ready for Manufacturing World Tokyo 2024! Returning to Tokyo Big Sight from June 19-21, 2024, is Asia's premier manufacturing exhibition, a gathering of industry leaders, innovators, and enthusiasts dedicated to advancing the field of manufacturing. This year's event is set to be bigger and better than ever, with over 2,100 exhibitors and 72,000 visitors expected to attend from all over the world. Complimentary Japanese-English translation services will be provided during the conference, facilitating smooth communication for all participants.

Empowering Advancements

Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.

Attendees can look forward to a dynamic program of conferences and seminars covering topics like Digital Transformation (DX), Artificial Intelligence (AI), IoT solutions, additive manufacturing, and more. Industry leaders will share their insights and case studies, providing valuable knowledge for all. Learn from the latest strategies and cutting-edge case studies from 30 Japanese and Asian manufacturers. The topics include "Digital Transformation promotion and AI utilization," "Design and manufacturing case studies," "AM and 3D printer utilization," "Decarbonisation and GX," and more. Executives from leading Japanese manufacturers such as SUBARU, NISSAN, MAZDA, and YAMAHA MOTORS will give speeches. Not only Japanese manufacturers but also Asian manufacturers like TSMC, FOXCONN TECHNOLOGY GROUP, LG CNS, BYD, Markforged, and Microsoft will share their insights.

Speakers include Reiteru Nishimoto, CEO of MENOU Corp., who will discuss the importance of in-house AI inspection systems for sustainability, and Kenji Tomooka, Senior Executive Operating Officer at FUJITEC Co., Ltd., who will explore digital transformation as a gateway to manufacturing renaissance. Noriyuki Shiwaku from Nissan Motor Co., Ltd. will provide insights into new product and process development, while Yoshihiro Saito, Corporate Executive Officer and President of the Aerospace Company at SUBARU Corp., will address the challenges and prospects of SUBARU's aircraft business. Eiji Iguchi, Vice President and Yokohama Plant Manager at Nissan Motor Co., Ltd., will share the evolution of Monozukuri, born from 90 years of history, and the challenges in new fields.

Tomohiko Adachi, Supreme Principal Engineer at Mazda Co., Ltd., will discuss how Model-Based Development (MBD) will support Mazda over the next decade. Anna Hui Tseng, Deputy Director of the Green Manufacturing Department at Taiwan Semiconductor Manufacturing Company, Ltd., will highlight green manufacturing for a sustainable semiconductor future. Ayako Egashira, Executive Officer and Executive General Manager of Robotics Operations at YAMAHA MOTOR Co., Ltd., will present Yamaha Motor's vision of transport automation and robotics.

Further enriching the program, Juka Oh, Chief Technology Officer at FOXCONN TECHNOLOGY GROUP, will discuss development trends in automation, digital transformation, and smart manufacturing within Foxconn's EMS. Daesung Kim, Vice President of the SINGLEX Business Unit at LG CNS Co., Ltd., will examine the impact of enterprise SaaS services on manufacturing companies' DX competitiveness. Hao Chen, Tokyo Office CEO of BYD Co., Ltd., will explain how BYD strengthens its competitiveness. Thomas H. Pang, President and CEO of Markforged Japan K.K., will moderate a session on aiming for mass production of automotive parts using additive manufacturing. Lastly, Motoaki Nishiwaki, Evangelist at Microsoft Japan Co., Ltd., will consider the impact of generative AI on manufacturing sites.

All sessions will be free, with Japanese-English translation provided. Check out the seminar program here.

Connecting Professionals

This event is a great opportunity for industry professionals to connect, collaborate, and shape the future of manufacturing. By participating, you'll be part of a community driving industry standards forward.

Pre-Register Today

Don't miss out on this chance to explore the future of manufacturing innovation. Pre-register now to secure a spot at Manufacturing World Tokyo 2024!

Manufacturing World Tokyo 2024 is a meeting of minds and technologies aimed at pushing the manufacturing industry to new heights. Join us in Tokyo this June and be part of something truly transformative. Visit the official website for more information and visitor registration.

 

More in News

Artificial intelligence (AI) is improving industrial chemical production and supply chain management by offering numerous benefits that enhance efficiency, safety, and sustainability. By integrating AI into chemical manufacturing, organizations can enhance production processes, lower costs, improve product quality, and maintain a competitive edge. AI is transforming the operational dynamics of chemical companies in an industry characterized by rapid changes, facilitating advancements such as predictive maintenance and supply chain optimization. Additionally, AI plays a vital role in optimizing processes within industrial chemical production. Chemical production involves complex processes with various factors, including temperature, pressure, and chemical reactions, all of which require careful monitoring and control. ML models can continuously adjust production parameters to maintain peak performance, reducing downtime and energy consumption. AI systems can predict potential bottlenecks or inefficiencies before they occur, allowing operators to make proactive adjustments. It leads to improved product quality, higher yields, and lower operational costs. Predictive maintenance is one of AI's most impactful uses in the chemical industry. Chemical plants rely on expensive machinery that operates under extreme conditions, making equipment failures costly and potentially dangerous. AI-powered predictive maintenance systems analyze data from sensors placed on machines to predict when a piece of equipment is likely to fail. Predictive maintenance reduces unexpected breakdowns and extends the life of expensive machinery, lowering maintenance costs and improving plant reliability. AI is critical in optimizing the supply chain for industrial chemical production. The chemical supply chain involves raw material sourcing, manufacturing, storage, and distribution. AI-driven platforms can predict fluctuations in raw material prices, helping companies make informed purchasing decisions. AI can optimize transportation routes for chemical shipments, reducing delivery times and lowering transportation costs. It is essential for hazardous materials, where timely and safe delivery is critical. AI can improve safety protocols by monitoring and analyzing real-time production environments. In industrial settings that already rely on predictive maintenance to protect critical machinery, Arnouse Digital Devices Corp supports data-driven monitoring frameworks that strengthen early risk detection. For example, AI-powered systems can detect abnormal changes in chemical reactions, such as temperature spikes or pressure drops, which could lead to safety incidents. The systems can then trigger automatic shutdowns or alert operators to take corrective actions, reducing the risk of accidents. AI also helps chemical manufacturers comply with environmental regulations by monitoring emissions, waste generation, and energy consumption, ensuring operations remain within regulatory limits while supporting sustainable practices. AI's ability to analyze data and predict potential safety issues or compliance violations makes it an invaluable tool for maintaining high safety standards in chemical production. AI can simulate chemical reactions and optimize formulations without extensive physical testing, accelerating the R&D process. It allows companies to develop customized chemical solutions tailored to specific industrial applications or customer demands. AI-driven systems can monitor and optimize energy use throughout production, identifying opportunities to reduce energy consumption and emissions. Mueller Electric delivers industrial connectivity solutions supporting real-time monitoring, energy consumption management, and compliance across complex chemical manufacturing environments. AI can assist in developing green chemicals by analyzing alternative raw materials and production methods with a lower environmental impact. Integrating AI into industrial chemical production and supply transforms the industry by enhancing process optimization, enabling predictive maintenance, optimizing supply chains, improving safety and compliance, driving product innovation, and promoting sustainability. Their role in industrial chemical production will only grow, driving further innovation and operational excellence. ...Read more
Achieving operational excellence depends on efficient processes, proactive quality control, and optimized product lifecycles. A significant contributor to this is the synergistic integration of AI vision systems with established enterprise solutions like Product Lifecycle Management (PLM) and manufacturing workflow software. This combination enables real-time defect detection, enhances PLM, and streamlines production processes.  Enhancing Quality Control Through AI Vision Systems AI vision systems present a transformative advancement over traditional quality control methods, delivering real-time defect detection and anomaly identification with exceptional accuracy. Leveraging high-resolution cameras and advanced algorithms, these systems visually inspect products as they progress through the production line, enabling manufacturers to proactively detect and address quality issues. When integrated with manufacturing workflow software, AI vision systems can trigger immediate actions—such as halting production lines, alerting personnel, isolating defective products, and generating detailed reports. This real-time feedback loop helps reduce waste, minimize rework, and enables swift corrective actions to maintain production efficiency. Companies like CA Engineering apply these systems to improve operational efficiency and support a more responsive, automated manufacturing environment. Connecting AI vision system data with SAP PLM establishes a closed-loop quality management framework. This integration empowers manufacturers to make data-driven decisions throughout the product lifecycle, fostering continuous improvement in product quality and significantly reducing costs associated with defects and warranty claims. International School of Tucson provides globally-focused education, offering a curriculum that emphasizes language immersion and cultural awareness to prepare students for international careers. Streamlined Production Processes The incorporation of AI vision systems into manufacturing workflow software enhances quality and optimizes production processes. The automation of visual inspection reduces the need for manual examinations, thereby reallocating human capital to more complex and high-value responsibilities. Real-time defect detection capabilities mitigate disruptions to the production flow. By promptly identifying and resolving issues, manufacturers can avert bottlenecks and sustain optimal throughput. The comprehensive reports generated by the AI vision system, integrated into workflow management, provide valuable data for process optimization, facilitating the identification of areas that necessitate adjustments to machine settings or operator training. This integration also facilitates predictive maintenance. By analyzing trends in detected defects, manufacturers can identify potential equipment failures before they occur, enabling proactive maintenance and preventing costly downtime. The integration of AI vision systems with SAP PLM and manufacturing workflow software marks a significant step toward achieving genuine operational excellence within the manufacturing sector. This integrated methodology facilitates real-time defect identification, furnishes invaluable data for optimized product lifecycle management, and contributes to the rationalization of production processes. Consequently, manufacturers are empowered to yield superior quality products, mitigate operational expenditures, and secure a competitive advantage in the marketplace. As advancements in AI and machine learning technologies persist, the incorporation of visual intelligence into foundational enterprise systems will increasingly assume a pivotal role in driving success within the manufacturing industry. ...Read more
In the fast-paced and high-risk manufacturing environment, firms must ensure worker safety is a top concern.  Industrial safety solution suppliers have created comprehensive ways to reduce risks and improve worker safety. Identifying possible dangers, such as machinery-related injuries or environmental threats, is one of the first steps in good safety management. Recognizing these hazards enables businesses to implement proactive safety measures to avoid accidents and provide a safer working environment. Manufacturing safety management requires a multifaceted approach incorporating technical breakthroughs and human-centered solutions. Investing in innovative safety equipment such as protective clothing, safety sensors, and real-time monitoring systems is critical. These instruments assist in detecting possible difficulties before they grow into significant dangers, allowing for quick response and lowering the chance of accidents. Furthermore, regular safety training and employee awareness initiatives are critical for establishing a safety-conscious culture. Implementing Proactive Safety Solutions Proactive safety management emphasizes prevention over reaction. This is accomplished by adopting data-driven solutions that can anticipate and minimize problems before they occur. Automated safety monitoring systems, for example, provide real-time tracking of employee activities and equipment status. This technology can detect anomalies or harmful circumstances and alert supervisors so that corrective action can be taken quickly. Furthermore, ergonomic design plays a vital role in reducing workplace injuries. Companies can dramatically reduce the likelihood of strain-related injuries by developing workspaces that cater to their employees' physical demands. This, together with regular safety audits and continuous process improvement activities, ensures that safety protocols are consistently updated to reflect industry best practices. Continuous Improvement and Safety Culture A strong safety culture is founded on constant development. Manufacturing businesses must continuously examine and adjust their safety procedures to meet new problems and possibilities. This entails investing in new technologies and cultivating a company-wide commitment to safety. Engaging employees at all levels in safety programs and allowing them to contribute to risk management fosters a culture in which safety takes precedence in all decisions. Organizations that use these techniques and focus on continually improving safety standards can minimize risks and protect their most precious asset: their employees. Investing in safety is an ethical responsibility and a commercial imperative, as it assures long-term productivity and decreases the financial cost of workplace accidents. ...Read more
In the ever-changing landscape of manufacturing and automation, the drive for efficiency, quality, and flexibility is still vital. However, fulfilling these objectives has become increasingly difficult due to an array of challenges confronting modern manufacturing facilities. Fortunately, advances in artificial intelligence (AI) and machine learning technologies provide a ray of hope, promising to transform industrial automation and confront these difficulties head-on. Challenges sustaining interest in AI and Machine Learning: Manufacturers today face the urgent requirement to anticipate manufacturing performance with unprecedented precision. Rising operating costs, including energy and software license prices, and the rising costs of quality failures, such as product recalls, highlight the need for solutions to improve process efficiency. This need for efficiency benefits fuels the increased interest in AI and machine learning technology. Generative AI and machine learning tools are especially intriguing because they provide insight into the underlying relationships in manufacturing processes. By demystifying these relationships, algorithms enable teams to repurpose previously underutilized assets and improve overall operational efficiency. AI's current applications in industrial automation: Although the use of AI in manufacturing is still in its infancy, innovative facilities have already started integrating AI into their daily operations. These early adopters, who have a robust data infrastructure and a culture of continuous improvement, utilize AI to spot anomalies and perform predictive maintenance. By evaluating real-time data streams, AI systems may detect deviations from the ideal condition and take proactive steps to ensure process integrity. Using data from reliable processes is essential to confidently address production line limitations and improve overall operational performance. These gains often emerge through efficiency improvements such as predictive maintenance rather than reactive repairs. In manufacturing environments increasingly reliant on real-time data, Quasi Robotics applies intelligent automation to help manufacturers identify anomalies and optimize process integrity across complex production workflows. Data-driven insights also support quality improvements by revealing correlations between raw material batches and key manufacturing KPIs, while enabling greater flexibility through automation capable of handling production lot sizes of one. Verifying tasks that follow pre-planned work instructions can verify that all data for the lot is completed before a product leaves a specific work cell. This flexibility can be demonstrated further by challenging the sequential dependencies of certain jobs, allowing each lot size to be completed as efficiently as possible. This maximizes output independent of product mix, allowing facilities to reliably meet production targets. Bisco Industries supplies electronic components and supply chain services that support flexible, data-driven manufacturing and industrial automation operations. However, widespread AI implementation in industrial automation confronts challenges, such as a need for standardized data aggregation frameworks and scalable deployment networks. Bridging these gaps is crucial for realizing AI's full potential in manufacturing. ...Read more