The product lifecycle, including design, engineering, production, and operation, will be covered by the partnership.
As part of the partnership, the companies are integrating Siemens’ Teamcenter product lifecycle management (PLM) software with Microsoft Teams and the language models in Azure OpenAI Service, and other Azure AI features.
Among many other use cases, the tie-up is said to facilitate increased collaboration, for instance, by allowing staff to document and report product design or quality concerns in a natural speech via mobile devices.
Azure OpenAI Service can process that informal speech data into a report, which is then transferred to the concerned team.
Workers can also record data in their preferred language, which can be translated into the official company language with Microsoft Azure AI.
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Additionally, Siemens and Microsoft are working together to assist software developers and automation experts speed up the development of code for Programmable Logic Controllers (PLCs).
PLCs are industrial computers that control most of the machinery in factories across the world.
At Hannover Messe exhibition between 17 and 21 April, the companies will demonstrate how engineering teams can dramatically cut down on time and the likelihood of errors by creating PLC code through natural language inputs.
Siemens member of the managing board and CEO of digital industries Cedrik Neike said: “Siemens and Microsoft are coming together to deploy tools like ChatGPT so we can empower workers at enterprises of all sizes to collaborate and innovate in new ways.”
Microsoft executive vice president of Cloud + AI Scott Guthrie said: “With Siemens, we are bringing the power of AI to more industrial organisations, enabling them to simplify workflows, overcome silos and collaborate in more inclusive ways to accelerate customer-centric innovation.”
They will also showcase how photos and videos can be analysed using Siemens’ Industrial Edge and Microsoft Azure Machine Learning.
This processed data can then be used to design, implement, run, and monitor AI vision models to detect defects.