Q&A: Physical AI's role in supply chain digital transformation

Automation device part supplier MISUMI Group Inc. last week announced the launch of MISUMI Americas, a specification-driven global manufacturing and supply chain company that integrates the manufacturing platform Fictiv with MISUMI's industrial experience.

MISUMI Americas aims to combine standard, configurable and custom fabricated parts into one platform for engineers and manufacturers, joining Japanese operational precision with American digital innovation, according to MISUMI.

Aloha, digital transformation: Hawaii DOT saves in maintenance costs through AI analytics, data, machine vision

Our group of brands wanted to dive a little deeper, so here find a brief Q&A with Dave Evans, MISUMI Americas president and CEO, that explores smarter supply chains that are boosting U.S. manufacturing competitiveness, and the emerging applications of physical AI. The following is our Q&A with the MISUMI president and CEO:

Smart Industry: Big picture … how is AI transforming static supply chains

Dave Evans: For decades, supply chains were built to be rigid linear tracks, optimized purely for predictability and low cost under perfectly stable conditions. But if the last few years have taught us anything, it’s that stable conditions no longer exist. Legacy systems rely heavily on manual intervention, fragmented data, and siloed communication, which inevitably lead to severe bottlenecks whenever market volatility strikes.

AI is transforming those old, static tracks into dynamic, interconnected networks. Instead of a supply chain that breaks when a tariff changes or a factory goes down, AI acts as an intelligent operating system.

We can use AI to analyze thousands of data points instantly—from automatically evaluating complex 3D CAD files for manufacturability (DFM), to instantly pricing custom components and routing production to the optimal factory based on real-time capacity and global logistics.

We are moving away from manual, spreadsheet-driven logistics and moving toward an agile network that automatically heals and re-routes itself in real time. This shift changes the entire manufacturing paradigm—from reactive crisis management to proactive, automated orchestration.

SI: How enthusiastically is this shift being embraced?

DE: The enthusiasm isn’t just high; it’s become an absolute mandate. In our latest State of Manufacturing & Supply Chain Report, 95% of manufacturing leaders stated that implementing AI into their supply chain operations is vital to their company’s future success. This overwhelming consensus proves that digital transformation is no longer a luxury for early adopters, but a baseline requirement for survival.

See also: Why industrial AI requires a data ops foundation to scale

The industry is embracing this because the old way of doing things has simply become too slow, too opaque, and too costly. More than 80% of leaders told us that traditional supplier sourcing takes up far too much time. Manufacturers are tired of spending weeks waiting for quotes, chasing down status updates, and dealing with prototype delays.

They are embracing AI enthusiastically because it eliminates the dark corners of fragmented data, it bridges skilled-labor shortages, and AI enables their engineers to focus on what they do best: designing incredible products, rather than chasing down parts.

SI: And how are autonomous production systems changing as a result?

DE: The ultimate appeal boils down to one word: velocity. When I was the first hire at Ford’s Silicon Valley Innovation Lab, it could take up to 12 weeks just to get a single prototype part back. That lag kills innovation, drains capital, and stalls time-to-market.

Autonomous production systems bridge the gap between digital design and physical reality, shrinking those development cycles from months to mere days. An autonomous production system doesn't mean a lights-out factory with no humans; it means a seamless, frictionless digital workflow.

A designer uploads a file, AI instantly analyzes it for manufacturing flaws, the global digital platform automatically sources the materials, and automated precision systems execute the build.

The appeal is that it gives hardware companies the agility of a software deployment but in the physical world. It unlocks massive product customization, drastically reduces operational overhead, and guarantees total predictability in an otherwise unpredictable global market.

See also: With new software team-up, AVEVA and IFS think they’ve closed the gap between AI insight and action

SI: What does this mean for U.S. manufacturing competitiveness?

DE: It’s an absolute game-changer. For a long time, the dominant narrative around U.S. manufacturing was constrained by labor shortages and the constant temptation to outsource production offshore. AI completely turns that narrative on its head by redefining how value and efficiency are created on the factory floor.

By unifying standard components and custom digital manufacturing onto a single AI-powered platform, we are leveling the global playing field. AI helps automate the repetitive, administrative friction of sourcing, compliance and quality tracking.

This empowers mid-sized and local US manufacturers to scale up their operations, drive incredible internal efficiency, and confidently lead a massive wave of reshored, local production.

We are entering a new era of industrial strength in the Americas where our competitiveness isn’t defined by cheap labor, but by digital speed, high-precision engineering, and unmatched supply chain agility.

See also: Siemens entry aims to make agentic AI more affordable for SMBs

SI: Let’s explore that notion of “physical AI.” How do you define “physical AI” and how does it come into play here?

DE: I define physical AI as the intelligence that allows digital software to accurately perceive, reason and interact with the physical world. While standard generative AI models are great at processing text or pixels on a digital screen, physical AI understands things like material tolerances, friction, mechanical forces, and spatial geometry. It bridges the gap between abstract code and concrete manufacturing reality.

In our world, physical AI is the essential connective tissue. It’s what allows an automated robotic gripper to safely handle a delicate component, or a digital platform to instantly know how a specific tool path will affect a block of raw aluminum.

By combining the massive digital datasets we process daily with world-class physical manufacturing, we are moving past theoretical, screen-bound AI.
Physical AI is bringing intelligence directly to the factory floor, enabling the next generation of hardware—from advanced humanoid robotics to breakthroughs in climate tech and aerospace—to be built faster and more reliably than ever before.