For manufacturers, supply chain strategy was once largely an exercise in efficiency. Find the right suppliers, control costs, keep inventory moving and deliver products on time.
That equation has become considerably more complicated.
Years of disruption, geopolitical uncertainty, changing trade conditions and rapid technological advancement have forced manufacturers to reconsider how their supply networks are built. Cost remains important, but resilience, visibility and adaptability have become equally critical measures of a successful supply chain.
The scale of that disruption is difficult to overstate. The Federal Reserve Bank of New York’s Global Supply Chain Pressure Index, which tracks transportation costs and manufacturing indicators across major economies, reached 4.3 standard deviations above its historical average at the end of 2021. By June 2026, global supply chain pressures were falling again, but the experience leaves manufacturers with a lasting focus on resilience, supplier visibility and risk.
Those challenges were at the center of Automation Alley’s Integr8 Roundtable, “Automation in the Real World — Aligning Supply Chain and Strategy,” which brought together manufacturers, technology providers and educators to explore how automation, artificial intelligence and deeper supplier collaboration are reshaping the modern supply chain.
The event opened with a fireside chat between Patric Stadtfeld, chief procurement officer for Siemens in the Americas, and Automation Alley CEO Tom Kelly before the roundtable and panel discussion. Stadtfeld oversees approximately $8 billion in annual Siemens spending across the Americas as part of a global procurement volume of about $35 billion. With 25 years in procurement and supply chain roles, he has watched the operating environment change considerably.
“Disruptions are the new normal,” Stadtfeld said. “What I tell my team is, expect the unexpected every day.”
CHALLENGES
Managing the unexpected
Supply chain strategies that once remained viable for 12 to 18 months may now need to change rapidly as companies respond to geopolitical developments, tariffs, transportation disruptions, extreme weather and other pressures.
For Siemens, those conditions have accelerated a decade-long effort to better understand the interconnectivity of its supply network.
“Think about a supply chain as an ecosystem,” Stadtfeld said.
That ecosystem was once managed primarily around cost. Stadtfeld said decades of sourcing strategies pushed significant production toward lower-cost countries at a time when disruptions were less frequent. Today, manufacturers must consider risk, lead times, regulatory requirements and geographic dependencies alongside cost.
Nearshoring and onshoring have consequently become important parts of supply chain strategy, but that does not mean global supply networks are disappearing.
U.S. goods imports reached a record $3.44 trillion in 2025, according to the U.S. Bureau of Economic Analysis and U.S. Census Bureau. Imports from Mexico alone reached approximately $535 billion.
The challenge is therefore not simply deciding whether a supply chain should be global or domestic. Manufacturers increasingly need to understand where vulnerabilities exist throughout the ecosystem and determine which risks they are willing to carry.
Seeing the entire supplier ecosystem
Visibility becomes particularly difficult as the number of companies within that ecosystem grows.
Siemens has approximately 17,000 suppliers in the United States, with more than half of that supplier base consisting of small and midsize enterprises. Finding the right supplier within a network of that scale requires more than knowing that manufacturing capacity exists.
“The number one challenge any small or medium-sized manufacturer has, whether here or anywhere else, is visibility,” Stadtfeld said.
The problem works in both directions. SMEs need larger customers to understand their capabilities, while large manufacturers need effective ways to identify specialized suppliers when demand, capacity requirements or sourcing strategies change.
Digital supplier marketplaces and manufacturing networks can help close that gap, but visibility also depends on manufacturers clearly communicating where they excel.
Stadtfeld identified customization, short lead times, technical expertise and high quality as areas where U.S. SMEs can be particularly competitive.
AI meets a fragmented supply chain
Artificial intelligence introduces another layer to the visibility challenge.
Supply chains generate enormous amounts of information across procurement, production, inventory, logistics, suppliers and customers. AI creates new opportunities to connect those signals, identify risk and help manufacturers make decisions while there is still time to act.
Adoption, however, remains ahead of integration.
A 2026 Gartner survey of 140 senior supply chain leaders found that only 17% of supply chain organizations were pursuing immediate AI-driven transformation of processes and workflows. The remaining 83% were applying AI incrementally to individual use cases or gradually scaling it into integrated processes.
Separate Gartner research found that 56% of chief supply chain officers identified integrating AI with legacy systems and processes as a major challenge, while 50% cited limited internal expertise or talent to implement and manage the technology.
“We still have a lot of hype versus what the reality is all about,” Stadtfeld said.
Siemens already uses AI within procurement for tasks such as evaluating sourcing information and supplier responses. But procurement, sales, forecasting and production can still operate within separate digital environments. The longer-term opportunity is connecting those systems and eventually orchestrating AI agents across functions.
Paying the complexity tax
George Stasiw, senior account executive at Samsung SDS, described another obstacle: the “complexity tax” successful companies accumulate as they grow.
Manufacturers can have numerous enterprise systems across facilities and acquired businesses, with different systems identifying the same materials or products in different ways. The larger and more complex the organization becomes, the more difficult it can be to connect that information.
AI can help analyze information across those environments, compare suppliers and parts, evaluate risk and turn fragmented information into actionable supply chain intelligence.
“That’s the beauty of AI,” Stasiw said.
But technology cannot compensate indefinitely for disconnected systems. Stasiw emphasized the need for an architecture and data strategy capable of connecting operational information with broader corporate objectives around workflow, risk and security.
The potential becomes greater when that connectivity extends to suppliers, but doing so requires organizations to confront another challenge: trust.
Data without context
More data does not automatically produce better decisions.
Rebekah McCarter, lead supplier scout at the Michigan Manufacturing Technology Center, said manufacturers still need experience and context to interpret what AI produces.
“We understand data, and data has to be clean, it has to be good,” McCarter said. “But there’s still that experience that you bring to the table to interpret that data.”
Demand signals remain particularly important in supply chain management. AI can help collect and organize the information surrounding those signals, but people still need to determine what that information means for the business.
Dr. Henry Aigbedo, professor of operations and supply chain management at Oakland University, sees a related challenge emerging in education.
Companies increasingly expect graduates to be AI literate, while educators must ensure students continue developing the critical-thinking and problem-solving abilities necessary to evaluate what those systems produce.
“The critical thinking aspect will remain important,” Aigbedo said.
According to Aigbedo, Oakland University faculty members are establishing AI policies for their courses defining whether and to what extent students may use the technology. The question is no longer whether students will encounter AI, but how to incorporate it without allowing the technology to substitute for learning.
Yuji Fujii, director of emerging technology initiatives at Washtenaw Community College, said the speed of change adds another complication.
“Technology is moving so fast,” Fujii said. “We do have to keep up with what’s needed in the industry.”
Those challenges are significant, but they also point toward where some of the greatest opportunities are emerging. The same pressures forcing manufacturers to rethink their supply chains are creating new openings for regional suppliers, new applications for AI and automation, and new models for collaboration across the manufacturing ecosystem.
OPPORTUNITIES
Nearshoring opens doors for SMEs
For smaller manufacturers, the rebalancing of global supply networks represents a substantial opportunity.
Siemens’ U.S. spending with SMEs has grown from a little more than $300 million in 2016 to nearly $2 billion in 2026, according to Stadtfeld. The shift was part of a decade-long strategy focused not only on reducing risk, but also identifying the quality, innovation and specialized capabilities available from smaller suppliers.
“They’re sometimes even more cost competitive than the big companies because they have much less overhead,” Stadtfeld said.
Their advantages can extend beyond cost. SMEs can bring specialized expertise, shorter lead times and flexibility to customer relationships. Stadtfeld said smaller manufacturers can also be more willing to engage in deeper conversations around innovation.
For Michigan manufacturers, the opportunity is to specialize and make those capabilities visible to companies actively reconsidering where and how they source.
Bringing suppliers into innovation earlier
The changing supply chain also creates an opportunity to redefine the relationship between manufacturers and suppliers.
Traditionally, procurement teams may receive a bill of materials after engineering decisions have already been made, leaving little opportunity to identify alternatives or recommend changes.
Stadtfeld said Siemens is working to involve procurement and suppliers earlier in the design process.
“One of the things that we at Siemens have not done a great job is utilizing the supplier innovation to help us design our products,” Stadtfeld said.
Suppliers often possess specialized knowledge of materials and production processes that can influence a product before specifications are locked in. Siemens has created channels through which suppliers can respond to technical challenges and propose ideas that can be evaluated with engineering teams.
That approach turns the supply chain from a purchasing network into a potential innovation network.
Stasiw sees AI creating opportunities to extend that collaboration further. Manufacturers capable of integrating information across their operations can potentially open portions of that ecosystem to trusted suppliers, allowing companies to identify alternatives and solve problems collaboratively.
That, Stasiw said, creates “the ability to scale at a level that we haven’t seen before thanks to AI.”
From knowing what happened to understanding why
John Gnotek, AI strategy advisor at Gnotek AI, said many manufacturers already possess significant amounts of production and traceability data. The opportunity is to make that information useful fast enough to influence a decision.
“What AI is good at is taking that data and helping decision-makers make real-time decisions,” Gnotek said.
A traditional report might tell a manufacturer that sales declined or production fell. AI can examine surrounding information to identify contributing factors, such as a late supplier, production downtime or an external disruption affecting another part of the world.
The distinction is critical for supply chain resilience. Knowing something went wrong after the fact is considerably less useful than understanding why it is happening while alternatives remain available.
Gnotek also emphasized standardization and traceability. Manufacturing systems can already associate process data with individual components and operations. AI creates an opportunity to synthesize that information across a larger environment instead of requiring people to manually piece together signals from multiple reports.
Building an AI-ready supply chain
Taking advantage of those capabilities requires manufacturers to get the fundamentals right.
Stadtfeld described data as potentially one of a manufacturer’s most important assets in the AI era.
“If you’re going to use AI, you have to have data,” Stadtfeld said.
That data must also be clean and reliable. Companies need to map and understand the processes generating information instead of expecting technology to correct poor processes downstream.
Gnotek’s emphasis on standardization points toward the same conclusion. Traceability becomes more valuable when information from suppliers, components and manufacturing processes can be analyzed together.
McCarter recommends beginning with a specific business problem rather than the technology itself. Manufacturers can identify a pain point, determine what information is necessary to address it, implement a practical solution and apply what they learn to the next challenge.
That approach can keep AI implementation grounded in measurable supply chain needs instead of adopting technology simply because it is available.
Turning trust into scale
For SMEs, collaboration may offer another advantage.
Large companies have scale, but they also carry the complexity Stasiw described. Smaller organizations can often move more quickly and build closer relationships with customers and suppliers.
McCarter said trust will determine how far those relationships can go.
“The trust factor to open up your information to your suppliers, that’s huge,” McCarter said.
For SMEs capable of developing that trust, sharing the right information can allow manufacturers and suppliers to use AI collectively to solve problems.
Preparing people alongside technology
The opportunity created by AI also depends on whether people know how to use it.
Aigbedo said the strongest results are likely to come from treating human and artificial intelligence as complementary rather than competing capabilities.
“What we are seeing is that the best results come from combining AI with human intelligence,” Aigbedo said.
AI can analyze complex information and identify patterns at a speed people cannot match. Human workers bring context, judgment and critical thinking that remain difficult to replicate.
Washtenaw Community College is putting that principle into practice through workforce training.
Fujii said the college has introduced noncredit AI workshops aimed at familiarizing people with the tools and reducing apprehension around their use. It has also purchased AI vision systems and added them to robotic training systems, giving students hands-on experience setting up and operating technologies they may encounter in manufacturing.
Fujii said training will likely require a tiered approach. Workers need practical familiarity with AI tools, but deeper knowledge of data quality, standardization and how AI reaches conclusions can become increasingly valuable as applications grow more sophisticated.
Stadtfeld framed the workforce challenge another way. Manufacturers now have multiple generations working alongside one another, each with different experiences with technology. Companies must preserve institutional knowledge while developing the digital skills required to work effectively with new systems.
“Don’t be afraid of the AI,” Stadtfeld said. “Be afraid of the people who know how to use AI more effectively.”
Building the next supply chain
The conversations throughout the Integr8 Roundtable ultimately pointed toward a broader definition of supply chain resilience.
Technology is part of it. AI and automation can provide greater visibility and help manufacturers make faster decisions. Nearshoring and supplier diversification can reduce exposure to disruption. Better data can reveal problems earlier.
But resilience also depends on connection.
Manufacturers need to understand the capabilities within their supplier networks. Suppliers need opportunities to participate earlier in innovation. Data needs to move across functions and organizations without losing reliability or context. And the people using these systems need the skills and judgment to turn information into action.
For Michigan, that creates an opportunity to build on one of its greatest existing strengths: a dense ecosystem of manufacturers, suppliers, technology companies and educational institutions. The competitive advantage may come not simply from adding more technology, but from connecting those capabilities more effectively.
For McCarter, SMEs have a particularly important role to play.
“We should be leveraging this democratized capability that’s now been given to us and the data that we have and the flexibility and ingenuity that we have and build something,” McCarter said.