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Automotive OEMs and
How do leading automotive organizations utilize Retrocausal?
The 2020s will be a decade of transition for the automotive industry. The legacy supply base spent much of the last decade adjusting to this new future. Most acquired new technology firms or were acquired, making use of scales of economy to carve out market share in spaces like electric harnesses, electric motors and a host of other now critical parts for autonomous and electric vehicles.

Battery-powered cars have just more than a dozen moving parts, compared with hundreds in one powered by a traditional internal combustion engine. So while those suppliers have spent years investing in electric-vehicle technology, components for gas-powered cars continue to make up the bulk of their businesses.

We are seeing the need for rapid adaptability and extremely fast iterations in process design and improvement in these uncertain times. As Elon Musk noted [1], excessive automation in the midst of such agile development cycles is a mistake. Organizations that are able to adapt teams of human operators and train and equip them with the right tools will succeed.

Our focus is on augmenting human workers with AI-based tools for training, live guidance and support, as well as on manual process analytics. Whether you need to perform continuous time and motion studies on a new process, trace mistakes through a line, visually "mistake-proof" complex assembly processes for leaner quality control, or get temporary workers rapidly up to speed, Pathfinder Apollo is the right platform for you.

We are working with Fortune 500 vehicle and part manufacturers, and giving them the opportunity to save millions on individual lines, without requiring them to stop their operations. Leading automotive VPs have called our manual process monitoring solutions a core strategic priority.

Let's get on a call, and discuss how we can help you reduce costs and build incredibly agile processes that will stand the test of the 2020s.

[1] Elon Musk says 'humans are underrated,' calls Tesla's 'excessive automation' a 'mistake'. TechCrunch (link)
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