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Business Wire
7 days ago
- Business
- Business Wire
Data & Digital: TotalEnergies and Emerson Sign a Strategic Collaboration to Boost the Value of Industrial Data
PARIS--(BUSINESS WIRE)-- TotalEnergies (Paris:TTE) (LSE:TTE) (NYSE:TTE) and Emerson's Aspen Technology business have announced a strategic collaboration to deploy advanced digital technologies for the continuous, real-time collection of data from TotalEnergies' industrial sites. The objective is to harness the value of that data to enhance decision-making, specifically through the use of artificial intelligence (AI), and optimize operational efficiency, energy use and environmental performance at TotalEnergies sites worldwide, thereby continuing to supply more reliable and sustainable energy. Leveraging data in real time… Under the terms of the deal, TotalEnergies will roll out Emerson's AspenTech Inmation™ across its industrial sites worldwide. This industrial data fabric will continuously collect and centralize millions of real-time data points from TotalEnergies' facilities, providing secure and unified access to data across the organization. The digital infrastructure, which also includes Emerson's advanced process control solutions, will subsequently allow TotalEnergies to deploy AI use cases. ...to improve industrial performance This rollout is planned over a two-year period and will ultimately enable TotalEnergies to extract more value from its data by: Accelerating the detection of anomalies and performance degradation; Optimizing energy consumption; Enhancing operational safety; Speeding up the integration of AI into industrial processes. 'At TotalEnergies, digital technology is a key enabler of our transformation toward a more sustainable and efficient energy future. Our collaboration with Emerson demonstrates how advanced technologies such as Inmation help us optimize operations, reduce emissions, and generate long-term value. This collaboration is a sign of our intention to turn data and digital tech into the hallmarks of our facilities' industrial excellence,' said Namita Shah, President of OneTech at TotalEnergies. 'Emerson's Aspen Technology business has worked with TotalEnergies for almost 30 years, and we're excited to continue our collaboration by supporting their operational and sustainability objectives with our digital technologies. The powerful combination of AI and our industrial data fabric solution will serve to accelerate TotalEnergies' mission,' said Vincent Servello, President of Emerson's Aspen Technology business. *** About Emerson Emerson (NYSE: EMR) is a global industrial technology leader that provides advanced automation. With an unmatched portfolio of intelligent devices, control systems and industrial software, Emerson delivers solutions that automate and optimize business performance. Headquartered in St. Louis, Missouri, Emerson combines innovative technology with proven operational excellence to power the future of automation. For more information, visit Emerson Global Media Contacts About TotalEnergies TotalEnergies is a global integrated energy company that produces and markets energies: oil and biofuels, natural gas and green gases, renewables and electricity. Our more than 100,000 employees are committed to provide as many people as possible with energy that is more reliable, more affordable and more sustainable. Active in about 120 countries, TotalEnergies places sustainability at the heart of its strategy, its projects and its operations. @TotalEnergies TotalEnergies TotalEnergies TotalEnergies Cautionary Note The terms 'TotalEnergies', 'TotalEnergies company' or 'Company' in this document are used to designate TotalEnergies SE and the consolidated entities that are directly or indirectly controlled by TotalEnergies SE. Likewise, the words 'we', 'us' and 'our' may also be used to refer to these entities or to their employees. The entities in which TotalEnergies SE directly or indirectly owns a shareholding are separate legal entities. This document may contain forward-looking information and statements that are based on a number of economic data and assumptions made in a given economic, competitive and regulatory environment. They may prove to be inaccurate in the future and are subject to a number of risk factors. Neither TotalEnergies SE nor any of its subsidiaries assumes any obligation to update publicly any forward-looking information or statement, objectives or trends contained in this document whether as a result of new information, future events or otherwise. Information concerning risk factors, that may affect TotalEnergies' financial results or activities is provided in the most recent Universal Registration Document, the French-language version of which is filed by TotalEnergies SE with the French securities regulator Autorité des Marchés Financiers (AMF), and in the Form 20-F filed with the United States Securities and Exchange Commission (SEC). TotalEnergies Media Relations: +33 (0)1 47 44 46 99 l presse@ l @TotalEnergiesPR Investor Relations: +33 (0)1 47 44 46 46 l ir@
Forbes
10-06-2025
- Business
- Forbes
Unpacking The Link Between Data And Industrial AI
Heiko Claussen is Chief Technologist at Emerson's Aspen Technology business, leading its AI research and technology strategy. In most conversations, data and AI are inextricably linked. The narrative tends to be that organizations are not using AI well if they don't have quality data from the field feeding into AI models. While this may be true in many industries and organizational contexts, it's far from universal. In fact, in industrial contexts, purpose-built industrial AI can be highly effective based solely on first principles and simulation models. But while purpose-built industrial AI designed for industries like oil and gas as well as chemicals may not require data from the field, that's not to say data is unimportant. Ensuring engineers, plant personnel and IT/OT leaders have access to centrally managed, contextual data is critical to deriving data-driven insights across the business. With the proliferation of modern digital tools and sensors, industries are collecting more data than ever before. As the volume of industrial data continues to grow, data management tools are fast becoming a key technology, helping companies sort through vast pools of data to better understand what information they have—all of which translates to higher levels of operational excellence. Forward-looking industrial organizations understand the growing data landscape, how well-managed data can improve their business and even the nuanced relationship between data and industrial AI. For decades, companies have collected data in one form or another, beginning with paper records collected by hand for sensors and integrated systems. What was once a trickle of information has become a deluge in recent years. Estimates from the World Economic Forum suggest that industries generated as much as 130 zettabytes of data in 2023. Despite collecting huge amounts of data, surveys show relatively little data actually gets used. A Forrester Research study found that companies use just 12% of their data for analysis and less than 30% of companies say they can translate the data into action. One reason why data is not used as well as it could be can be traced back to the fact that data is often highly siloed. In industrial environments, it's collected and stored across different plants and systems with different formats, tags and protocols, making it difficult to coordinate the use effectively. One way organizations are mitigating siloes and simplifying the process of aggregating vast amounts of data is through agnostic centralized data management tools with fewer connectors and extractors across various systems. With the ability to be hosted anywhere, ingest data from anything and feed data into anything, such tools help make data available to users when, where and how they need it. But advanced data management tools' arguably most important capability is adding crucial context to raw data. Context offers companies a more holistic understanding of their data—where it comes from, what sensors and units of measure it represents, when it was collected and more. Today's industrial use cases require not just a stream of binary values, but also a lot of metadata, such as specifications, plans, schedules and work orders. All this data needs to be stored in the right context for it to be useful. Data contextualization can be automated with an industrial data management tool. Because these tools inherently know the data's location and associated context, industrial organizations can eliminate the challenging, manual task of moving data and applying context afterward. By eliminating this step, industrial organizations can more quickly achieve efficiencies and fuel digital transformation initiatives that help them overcome increasingly complex technological and environmental challenges. AI is one example of a digital transformation initiative that has risen to the top of organizations' priority lists. Purpose-built industrial AI solutions can be applied to a host of operational challenges, from monitoring and analyzing emissions to automating mundane tasks to aiding engineers' decision making. As previously described, much of this can be achieved without field data, thanks to first principles, simulation models and deep industry domain expertise that enables guardrails that keep AI results safely within real-world constraints. In fact, an asset shouldn't be deployed in the field without such an approach. Industrial organizations eager to begin reaping the benefits of AI often don't have the luxury of waiting as field data is collected over time before training a model and controlling the asset. Despite industrial AI's effectiveness as is, asset data can help take AI results to the next level. By refining an AI model with in-context operating data from an industrial data management platform, organizations are beginning to close the simulation-reality gap and help predict future outcomes based on past observations. For instance, AI applications for predictive maintenance can be improved by identifying asset abnormalities and building models based on both normal and abnormal operations. The powerful combination of industrial AI and the right data from the field can effectively supercharge companies' operational excellence initiatives, making it easier to create and sustain meaningful AI models that give companies a significant competitive edge. Whether it's helping to optimize existing processes, identifying ways to increase efficiency or informing the design of new processes, there should be little doubt that data is—and will continue to be—a critical resource for industry. While data's benefits for AI and other business improvements may be known, the question that lies ahead is how companies will make the transition to more effective industrial data management tools. Replacing a web of siloed, global data connections that were likely set up by different personnel years earlier can feel overwhelming. Among other challenges, companies must maintain business continuity and avoid disrupting established processes that require traceable product information, test results and more. By initially configuring a new data management tool as a real-time backup or redundant control system alongside current connectivity architecture, then removing previous connectivity incrementally after a validation period, industrial organizations can safely implement centralized data management tools. Ultimately, this approach is faster, easier and more secure than setting up many peer-to-peer connections With the right data management tool and strategy in place, organizations can effectively use their data, enhance AI applications and fuel ever-more advanced use cases to help them remain ahead in an uncertain macro-environment. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
10-03-2025
- Business
- Yahoo
Albertsons Companies Set to Join S&P MidCap 400
NEW YORK, March 4, 2025 /PRNewswire/ -- Albertsons Companies Inc. (NYSE: ACI) will replace Aspen Technology Inc. (NASD: AZPN) in the S&P MidCap 400 effective prior to the opening of trading on Tuesday, March 11. S&P 500 constituent Emerson Electric Co. (NYSE: EMR) is acquiring Aspen Technology in a deal expected to be completed soon, pending final closing conditions. Following is a summary of the changes that will take place prior to the open of trading on the effective date: Effective Date Index Name Action Company Name Ticker GICS Sector March 11, 2025 S&P MidCap 400 Addition Albertsons Companies ACI Consumer Staples March 11, 2025 S&P MidCap 400 Deletion Aspen Technology AZPN Information Technology For more information about S&P Dow Jones Indices, please visit ABOUT S&P DOW JONES INDICES S&P Dow Jones Indices is the largest global resource for essential index-based concepts, data and research, and home to iconic financial market indicators, such as the S&P 500® and the Dow Jones Industrial Average®. More assets are invested in products based on our indices than products based on indices from any other provider in the world. Since Charles Dow invented the first index in 1884, S&P DJI has been innovating and developing indices across the spectrum of asset classes helping to define the way investors measure and trade the markets. S&P Dow Jones Indices is a division of S&P Global (NYSE: SPGI), which provides essential intelligence for individuals, companies, and governments to make decisions with confidence. For more information, visit FOR MORE INFORMATION: S&P Dow Jones Indicesindex_services@ Media View original content: SOURCE S&P Dow Jones Indices Sign in to access your portfolio
