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It starts with genAI interpreting material requirements in a timely manner and activating a dedicated AI agent that then orchestrates HMND 01 Alpha( along with other autonomous mobile robots( AMRs) to collect, transport, and deliver materials to production lines. Along the way, agents inform and guide the process with automated prerequisite checks for releasing production orders, including material, capacity, and scheduling availability. They flag material shortages and suggest workarounds, including alternative components and scheduling adjustments. This is exactly the type of use case manufacturers should be pursuing to maximize their AI investments, according to a recent report from Deloitte, the findings of which are based on a survey of 140 + manufacturers. The report asserts that it’ s time for manufacturers to prioritize
‘ combining AI technologies deliberately,’ adding,‘ The question is no longer who has the most AI pilots. The question is who can replicate successful use cases across lines, plants, and regions with consistent performance, governance, and user adoption.’
Early results at Martur Fompak support such a strategy. They show increased throughput, fewer errors and an AI-driven intralogistics model that appears readily scalable. The company is now targeting up to five times reduction in manual logistics coordination in the future state.
The business case for multi-AI deployments
Martur Fompak is among a range of manufacturers to successfully pilot multi-AI use cases. Here are two more examples:
■ In Vodafone Germany’ s warehouse in Duisburg, humanoid robots autonomously executed various visual inspection tasks across the facility. They detected misplaced or damaged products, assessed pallet stacking and weight distribution, highlighted unused storage space, and identified potential hazards such as obstacles in aisles or misaligned pallets. They then reported their findings and recommendations directly into a warehouse system for real-time visibility and better-informed, timelier decision-making.
■ Mahindra & Mahindra, a global automotive and industrial manufacturer, transformed an error-prone manual vehicle identity
Physical AI vs. Embodied AI vs. Embedded AI
▶ Physical AI refers to all forms of artificial intelligence focused on the physical world, including specialized models for perception, navigation, manipulation, and more
▶ Embodied AI leverages physical AI combined with AI agents
▶ Embedded AI focuses on digital systems, enabling smarter decisions inside business applications
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