Category: Industry trends

Key Points

Boeing could assess moving up production of its bestselling Max jets to 47 a month by the end of the year, its CEO said.

The company also plans to resume deliveries of airplanes to Chinese airlines next month after a pause during a trade battle between the country the Trump administration.

Boeing CEO Kelly Ortberg largely brushed off the tariff impact and said he didn’t expect all the duties to be permanent.

Boeing’s airplane deliveries to China will resume next month after handovers were paused amid a trade war with the Trump administration, CEO Kelly Ortberg said Thursday, as he brushed off the impact of tit-for-tat tariffs with some of the United States’ largest trading partners this year.

Ortberg had said last month that China had paused deliveries.

“China has now indicated … they’re going to take deliveries,” Ortberg said. The first deliveries will be next month, he told a Bernstein conference on Thursday.

Boeing, a top U.S. exporter whose output of airplanes helps soften the U.S. trade deficit, has been paying tariffs on imported components from Italy and Japan for its wide-body Dreamliner planes, which are made in South Carolina, Ortberg said, adding that much of it can be recouped when the planes are exported again.

“The only duties that we would have to cover would be the duties for a delivery, say, to a U.S. airline,” he said.

Regarding the rapidly changing trade policies that have included several pauses and some exemptions, Ortberg said, “I personally don’t think these will be … permanent in the long term.”

He reiterated that Boeing plans to ramp up production this year of its bestselling 737 Max jet, which will require Federal Aviation Administration approval.

The FAA capped output of the workhorse planes at 38 a month last year after a door plug that wasn’t secured when it left Boeing’s factory blew out midair in the first minutes of an Alaska Airlines flight.

Ortberg said the company could produce 42 Max jets a month by midyear and assess moving up to 47 a month about half a year later.

The company’s long-delayed Max 7 and Max 10 variants, the largest and smallest planes in the narrow-body family, are scheduled to be certified by the end of the year, he said.

Many airline executives have applauded Ortberg’s leadership since he took the reins at Boeing last August, tasked with stemming years of losses and ending reputational and safety crises, including the impact of two fatal Max crashes.

CEOs have long complained about delivery delays from the company that left them short of planes during a post-pandemic travel boom.

“I do think Boeing has turned the corner,” United Airlines CEO Scott Kirby told CNBC’s “Squawk Box” earlier Thursday. He said supply chain problems are limiting deliveries of new planes overall.

“We over-ordered aircraft believing the supply chain would be challenged,” he said.
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Electric car maker Lucid Group said Tuesday that Chief Executive Officer Peter Rawlinson has resigned as the company expects to more than double vehicle production to 20,000 this year.

Lucid said Chief Operating Officer Marc Winterhoff has taken over as interim CEO. The company said Rawlinson will serve as “strategic technical advisor to the chairman of the board and will no longer serve in his previous position.”

Winterhoff told CNBC on Tuesday that Rawlinson made the decision to resign on Friday, but he declined to provide further details.

Winterhoff, who joined Lucid from Roland Berger in December 2023, said: “After 12 years of day-to-day work and day-to-day activities, Peter has brought the company to where it is today, and now it is time to exit and hand over the baton.”

Rawlinson said in a statement on LinkedIn on Tuesday that he decided the time was “finally right” to step down after the “successful” launch of the company’s second product, a three-row SUV called Gravity. He did not elaborate further on the decision in the lengthy post.

Rawlinson’s departure was unexpected. As one of the company’s largest shareholders, Rawlinson also served as chief technology officer and had often touted his passion for and stake in the automaker. In July 2021, he took Lucid public through a reverse merger with a special purpose acquisition company (SPAC).

“My mission and dedication are unwavering. Other than when needed for tax purposes, I have not sold a single share,” Rawlinson said during the company’s third-quarter conference call in November. “As a result, I am committed to continuing to work tirelessly day and night to drive long-term shareholder value.”

Lucid said its board of directors has begun a search for a new CEO.

The announcement of the CEO change and production targets came alongside the automaker’s fourth-quarter financial results. For the year ended Dec. 31, the company reported a net loss attributable to common shareholders of $636.9 million, or 22 cents per share, on revenue of $234.5 million.

Analysts polled by the London Stock Exchange expected a loss of 25 cents per share on revenue of $214 million.

A year earlier, Lucid reported a net loss attributable to common shareholders of $653.8 million, or 29 cents per share, on revenue of $157.2 million.

The 2025 production target announced Tuesday is a decline from 9,029 vehicles produced and 10,241 delivered in 2024.

Winterhoff said production of the Gravity SUV would gradually increase this year. He declined to speculate on what percentage of the 20,000 production target the vehicle would account for.

Lucid shares rose about 8% during after-hours trading on Tuesday.

As of the close, the company’s shares have fallen about 13% this year amid slower-than-expected adoption of all-electric vehicles and uncertainty over federal support for electric vehicles under the Trump administration. Last year, the company’s shares fell about 28%.

Lucid’s main backer is Saudi Arabia’s Public Investment Fund. Its first product is the Air sedan, which will begin deliveries in late 2021.
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Siemens Mendix low-code development platform uses Amazon Web Services Serverless services to help enterprises build applications 10 times faster and save 70% of resources

Siemens Mendix low-code platform uses Amazon Web Services’ leading Serverless services to help enterprises achieve 10 times faster application building speed and 70% reduction in resource requirements. Siemens Mendix low-code development platform includes data integration, end-to-end process automation, visual development and AI connection functions, and widely supports manufacturing, automobiles, retail fast-moving consumer goods, finance, life sciences and logistics industries, providing enterprises with a wide range of enterprise-level solutions and business scenarios.

Siemens Mendix focuses on enterprise-level low-code application development and has been rated as a leader in Gartner’s Magic Quadrant for Enterprise Low-Code Application Platforms for four consecutive years from 2019 to 2023. The global cooperation between Amazon Web Services and Siemens Mendix began in 2015 and will be expanded to China in 2022.

Siemens Mendix low-code development platform combines Amazon Web Services Serverless services with low-code efficiently, and can flexibly support large-scale, high-complexity, and high-tech standard application development. The platform has created six core technology engines: visual, cloud, terminal, data, intelligence, and flow, which support drag-and-drop visual development methods, elastically scalable cloud native capabilities, automatically adaptable multi-terminal experience, open and extensible data integration capabilities, seamlessly connected artificial intelligence, and end-to-end intelligent process automation, helping enterprises deliver quickly and with high quality to meet changing business needs.

At the underlying architecture level, the Siemens Mendix low-code platform can fully integrate a series of Amazon Web Services Serverless and container services, including serverless computing services Amazon Lambda and Amazon Fargate, to achieve millisecond-level response; in terms of application integration, it uses the continuous integration and continuous delivery service Amazon CodePipeline, combined with the complete tool chain of Amazon Web Services, to automate the release process; at the same time, it uses the fast and flexible NoSQL database service Amazon DynamoDB to achieve data persistence, and uses the container application monitoring service Amazon Managed Service for Prometheus and the data visualization service Amazon Managed Grafana to build observability.

Siemens Mendix also puts the low-code development platform on the Amazon Web Services Marketplace (overseas region) and Amazon Web Services Marketplace (China region) to facilitate more enterprises to purchase and deploy.

The Siemens Mendix low-code development platform can accelerate the bottom-up, end-to-end digital transformation of enterprises.

Royal Post of the Netherlands is an independent express parcel delivery company designated by the Dutch government, providing services to nearly 20 million people every day. In the Netherlands, express deliveries below 22 euros are exempt from VAT, so many express deliveries are in the form of small packages, accounting for 95% of the daily express delivery volume. At the same time, the business volume of the Royal Post of the Netherlands has increased by 20% year by year, the core system is overwhelmed, and the system architecture is relatively old, and it is in urgent need of modernization. With the help of Amazon Cloud Technology, Siemens Mendix helped the Royal Post of the Netherlands to achieve rapid production deployment and operation and maintenance online, quickly develop scenario applications, and decouple complex core systems, split them into nearly 100 microservices, using a highly logical low-code platform to achieve high performance, and easily cope with a peak throughput of 1,000 orders per second. In the end, the Royal Post of the Netherlands completed a backlog of 2 years of work in just 6 months, and was able to process more than 1.5 million orders per day, and ensure that 99.95% of express deliveries can be delivered during normal operating hours. Through Amazon Cloud Technology, the Royal Post of the Netherlands saved a lot of manpower in infrastructure operation and maintenance, reduced costs by 80%, and obtained a return on investment within 3 months. It can complete a month’s development work in a few hours and deliver functions in a few minutes.

Similarly, with the help of Siemens Mendix low-code development platform based on Amazon Web Services, Thailand’s CP Group developed 200 apps in less than two years, with an average of at least 8 apps launched every month. Malaysia’s Kaneka Chemical quickly launched 55 manual processes and 13 subsystems in less than two years, and the quality management system was quickly launched within three weeks.

Wang Jiong, general manager of Siemens Mendix China, said: “The value that Siemens Mendix low-code platform brings to enterprises is inseparable from the deep integration with Amazon Web Services. Amazon Web Services’ robust underlying infrastructure and rich cloud service portfolio provide Siemens Mendix with a strong technical foundation, allowing us to focus on providing enterprises with differentiated value of low-code platforms, thereby helping enterprises realize business value in a faster and more economical way and cope with uncertain challenges in the future.”
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Industry 4.0″, “new quality productivity”, “artificial intelligence +”… We are experiencing an era of productivity transformation. New quality productivity with technological innovation as the core and breaking the boundaries of industrial scenarios has emerged, and the important representative force is the development of artificial intelligence.

In March of this year, “artificial intelligence +” was written into the government work report for the first time, which means that China will accelerate the formation of new quality productivity with artificial intelligence as the engine. This coincides with Schneider Electric’s long-term layout of digitalization and intelligence. Schneider Electric firmly believes that putting AI technology into practical applications in the industrial industry will stimulate unlimited potential for the “advancement” of China’s industry.

With the continuous iteration and evolution of AI technology, how to play its value in the complex and changeable industrial field to promote the development of new quality productivity? What scenarios can make AI play its greatest role and realize the large-scale application of AI, thereby accelerating the new industrialization?

Efficiency evolution, exponential transformation of productivity

In the transformation from traditional industry to new industry In the process, creating tangible value with cutting-edge technology is the only way – Schneider Electric is further breaking down the barriers between IT and OT, penetrating into the entire life cycle of enterprises from design, construction to operation and maintenance, and putting AI technology into practice.

In the early R&D and design stages, Schneider Electric is using AI technology to innovate the traditional software development methods, such as using large models to assist in generating basic code and help check code integrity, saving engineers a lot of repetitive work, and injecting more vitality into the development of new technologies and new functions. In the key production and manufacturing stages, AI technology is used to help factories improve quality and efficiency, such as using AI intelligent decision-making to help coordinate multiple factors and formulate accurate production plans; using AI visual inspection to efficiently identify product defects and improve product quality. In the process of operation and maintenance management, Schneider Electric is using AI algorithms and machine learning to help companies efficiently manage assets and equipment, improve operational efficiency, optimize energy use, and help companies improve the efficiency and resilience of operation and maintenance.

It can be seen that whether it is visual recognition, machine learning, large language models, or generative AI, they have now penetrated into all aspects of the industrial production process. So what is the key to maximizing the value of AI scenarios?

In-depth scenarios, deep integration of technology and applications

The key to unleashing the potential of AI technology lies in promoting the integration and innovation of AI technology and actual application scenarios. As a “practitioner” and “enabler” of AI scenario applications, Schneider Electric is committed to deeply integrating AI technology with a series of vertical industry scenarios to enable production quality and efficiency:

Process optimization: Schneider Electric uses AI algorithms to formulate intelligent control strategies and provides a disruptive production line optimization solution for a beer manufacturer. By aggregating, analyzing, and sensitively monitoring the working conditions of the entire production data, and predicting and fine-tuning the optimal control strategy, it helps customers achieve 20% material savings and 15% production efficiency improvement while achieving safe and high-quality production.
Industrial full-process carbon reduction: In an application example of a chemical company, Schneider Electric deployed a customized machine learning model to monitor six carbon emission sources in a vacuum distillation unit. The model uses the AVEVA PI System operational big data management platform to analyze data streams every 5 minutes, providing timely feedback on potential deviations in CO2 emissions. This enables operators to respond quickly, investigate root causes, and make targeted adjustments to optimize processes and minimize CO2 emissions. The model is not only applicable to vacuum distillation units, but can also be migrated to different industrial processes.
Refined management of energy consumption: Schneider Electric provides a semiconductor company with an ice machine cooling capacity prediction solution. Based on AI algorithms, it accurately predicts the cooling capacity on the demand side based on the historical data of ice machine operation. Refined management of energy consumption is achieved through more accurate control of energy demand. Actual measured data shows that the solution has an energy saving effect of 3-5%. If hardware modification is provided, a comprehensive energy saving of 5-10% can be achieved.
Improved energy efficiency of air compressors: Schneider Electric uses AI intelligent algorithms to achieve optimized control and intelligent management of air compressor stations, helping companies significantly improve energy efficiency. In a station management system project of a new energy vehicle company, through data collection, modeling and analysis, the optimal operating parameter suggestions are provided for the factory’s integrated station air compressor station control system and HVAC control system, achieving control logic optimization and energy saving and efficiency improvement, so that the company can achieve twice the result with half the effort on the road to building an efficient, energy-saving modern and green factory.
Dynamic refrigeration efficiency improvement: In a HVAC energy-saving renovation project of a data center, Schneider Electric injected AI modeling and data analysis algorithms into traditional PID closed-loop control. Through modeling and data collection, accurate prediction, optimization solution and strategy output, the terminal precision air conditioner in the computer room is optimized to dynamically output refrigeration according to actual needs. At the same time, the cold station control system is globally optimized to achieve 31% power saving of the terminal air conditioning system, and the cold station refrigeration efficiency is expected to increase by 20%.
Predictive maintenance: The equipment fault prediction and diagnosis system based on vibration mechanism + mathematical model, combined with the process mathematical model fault diagnosis tool, can not only help users diagnose mechanical aging and wear problems, but also diagnose equipment failures caused by electrical faults or process changes for users. Schneider Electric’s Xiamen factory has deployed an AI-based predictive maintenance solution for vacuum furnace equipment, enabling real-time data monitoring of equipment status 24 hours a day, 7 days a week throughout the year, and scheduling equipment maintenance according to prediction curves, saving approximately RMB 1.2 million in maintenance costs each year.With the rapid development of digital technologies such as artificial intelligence, the global industry is undergoing major changes. Schneider Electric will continue to be innovation-driven, promote the deep integration of AI technology with specific application scenarios in more industries, and work with more partners to create industry influence and move towards a smarter, innovative and sustainable future industry.
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Enhanced NINVA™ TSP341-N is the first non-invasive temperature sensor with SIL2 certification

To meet customer demand for simpler and safer temperature measurement, NINVA™ reduces the installation and maintenance costs of the protection tube without affecting the measurement performance

Quick installation and no protection tube testing means savings of up to 75% and lower operating costs

ABB has launched the enhanced NINVA™ TSP341-N non-invasive temperature sensor, providing safer and simpler temperature measurement for chemical, oil and gas industry applications. The new NINVA is the first non-invasive temperature sensor with SIL2 certification and is the safest non-invasive temperature measurement sensor on the market.

ABB’s innovative non-intrusive temperature sensor technology, with proven performance over the past few years, offers an alternative solution to traditional invasive thermowells, greatly simplifying temperature measurement.

Using the surface temperature of the pipe to infer the process temperature, NINVA™ offers the same accuracy and performance as invasive measurement devices, while avoiding the risks and lifecycle costs associated with design, installation and maintenance.

The SIL2 certified NINVA TSP341-N is based on the proven TTH300 temperature transmitter and offers a range of new features based on customer feedback. These include a unique split-mount capability, enhanced vibration resistance, optional configuration for applications up to 550°C (1022°F), and simpler calibration and maintenance through a removable sensor measuring insert with independent sensor calibration and zeroing capabilities. Without sacrificing accuracy and performance, NINVA™ eliminates complex thermowell designs for small pipes by adopting a precise clamping method for different pipe diameters from 40mm to 2500mm and less than 40mm.

“The market for non-intrusive process measurement is being driven by customers,” said Dr. Guruprasad Sosale, ABB’s Measurement & Analytics business unit. “As the need for sensing continues to grow, professionals in the global chemical, oil and gas industries see the value of this technology, which provides them with more detailed information about their processes without having to stop the process or cut the pipe. By turning a section of process pipe into a temperature measurement point, NINVA™ gives plant operators a simpler and safer way to measure process temperature without compromising performance – no downtime, no holes to cut, and significantly simplifies engineering.”

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