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Schneider Electric (digital transformation of energy management and automation) and Teradata, (big data analysis), are working together to contribute to the development of the plant of the future in France.
Announced on March 27 at the Global Industry trade show in Paris, this partnership responds to industry's growth demands with the focus of creating value and optimizing operating budgets (TCO) by making the most of investments, enabling a 360-degree view of customers, increasing the interoperability of systems while ensuring better production quality, productivity and greater agility.
A full offer is needed to make the most of Analyticssolutions. On the one hand Schneider Electric brings its know-how in industrial solutions, on the other Teradata brings the quality of its Analyticssolutions.
Teradata brings its expertise in the fields of Data Management, Data Integration, Data Model and Analytics, expertise recently recognized by the election of the firm as leader of the Magic Quadrant for the 16th consecutive year on Data Eat and Analyticssolutions.
This guarantees customers a personalized offer and a complete knowledge of the value chain of the production systems and management of the Supply Chain and allows to benefit from a thorough knowledge of the levers of performance.
"We are very pleased to conclude this partnership marking the advent of new industrial and technological solutions for the benefit of our customers in the industry. The industry of the future draws on the expertise of the best players in the market, including Schneider Electric and Teradata," said Marc Fromager, vice president of Schneider Electric's industry division. "We are now looking to the future to bring our customers the offer that will allow them to fully enter the 21st century industry with us."
"Today, the association with Schneider Electric (...) ushers in a new era in the capacity for integration with industrial solutions, allowing us to bring strong industrial expertise and strong analytical expertise in a highly competitive sector," said Christophe Gendre, Director of Teradata France
Through this association, the two players are able to design and then test complete solutions for industrialists, based on new uses, drawing in particular on their complementary know-how in the fields of Machine Learning,industrial performance and connected objects.
The two players plan to constantly improve customer satisfaction through a more relevant use of customer data.
Continuous optimization and compliance with new technologies enable growth to be developed in high-growth environments, thus optimizing structural costs and machine management.
Improving costs also requires the care taken in logistics and maintenance, a central element of heavy capital costs, by anticipating all malfunctions and reducing, if they occur, downtime from productive activities. Predictive analytics has already proven its worth, particularly in the rail sector, where the generalization of the technology has prevented 75% of derailments by some companies and even improved their image and profitability.
A European industrialist, thanks to predictive maintenance, has also enabled 99% of trains to arrive on time, to save money and to comply with European directives in this area. One electricity supplier saved more than $60 million through fewer outages and better monitoring of consumption.
The joint approaches of Schneider Electric and Teradata also enable the safety and quality of services provided through the adoption of faster and smoother decision-making processes to be worked on. The data expertise of the two firms also offers interesting technological possibilities, notably in the use of hybrid technologies (on-site or in hybrid cloud), to value intelligence in all its forms, know-how necessary for integration in an industrial world with disparate and constantly evolving technologies.
All these approaches enable growth by offering products and services with high added values, notably through the resonance of operational data (OT) with high value-added business data (IT).
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