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2026 POWERED BY:

Automation in the Real World: Aligning Supply Chain & Strategy An Implementation Playbook for Industry, Academia & Government • September 2026 Sponsored By:

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FOREWORD Resilience by Design: Rewiring the Modern Supply Chain

upply chains have always been about movement: materials, components, information and finished goods flowing across companies, borders and continents. But in 2026, the conversation has expanded far beyond moving products efficiently from one point to another. After years of disruption, geopolitical uncertainty, shifting trade policy and rapid technological change, supply chain strategy has become inseparable from business strategy.

For manufacturers, the lesson of the past several years is clear. Efficiency alone is no longer enough. The supply chains best positioned for the future will be those capable of adapting when conditions change, identifying risks before they become disruptions and maintaining operations when the unexpected occurs. That requires a different kind of visibility and a different approach to resilience. Digital technologies are increasingly providing both. Artificial intelligence, automation, advanced analytics and connected systems are giving manufacturers new ways to understand their supply networks, anticipate demand, manage inventory and respond to disruptions. At the same time, companies are reassessing sourcing strategies, supplier relationships and geographic dependencies as they navigate an increasingly complex global operating environment.

So what does a resilient supply chain look like in 2026? And how can manufacturers build one without sacrificing the competitiveness that made global supply networks so valuable in the first place? These questions sit at the center of this Integr8 playbook on automation and the supply chain. Industry leaders, technology experts and manufacturing professionals came together to examine how organizations are responding to today’s pressures while preparing for the challenges still ahead. From supplier visibility and data integration to automation, artificial intelligence and risk management, this playbook reflects a broad spectrum of perspectives. It is a curated collection of insights from Automation Alley’s network of industry, academia and government partners, all working toward stronger, smarter and more resilient manufacturing supply chains in the United States. Automation Alley extends its gratitude to all who have contributed to the Integr8 series throughout the year. The continued engagement of its members and partners makes this work possible and ensures that these conversations remain grounded, relevant and forward-looking.

Yet technology cannot eliminate uncertainty. A more connected supply chain can provide better information, but manufacturers must still determine how to act on it. Resilience requires difficult decisions about where to invest, where to build redundancy, when to diversify suppliers and how to balance cost against risk.

Tom Kelly

Executive Director & Chief Executive Officer Automation Alley 2

TABLE OF CONTENTS

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Attendee List: Sponsors, Supporters and Roundtable Participants

Expert Insights: Oakland Univeristy Effective Supply Chain Risk Management: An Imperative for Today’s Executives and Managers

Expert Insights: Washtenaw Community College Driving the Future Supply Chain: Building Michigan’s Automotive Digital Workforce

32 36 40 44

Recommendations: Industry

Industry Insight: Gnotek AI From Visibility to Understanding: Aligning Supply Chain Strategy in the Age of AI

Industry Insight: Samsung The Complexity Tax: Why Hidden Supply Chain Complexity is Becoming Manufacturing’s Largest Unseen Cost

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Key Takeaways: Main points from the Integr8 Roundtable Discussion

Main Feature: How AI is Reshaping the Manufacturing Supply Chain Key Insights from Industry Leaders

Recommendations: Academia Recommendations: Government Industry Insight: Siemens From Pilot to Practice: How Manufacturers can Build AI That Actually Lasts

Sources 3

SPONSORS, SUPPORTERS & ROUNDTABLE PARTICIPANTS SPONSORED BY: Siemens, Gnotek AI, Oakland University, Samsung and Washtenaw Community College SUPPORTED BY: Michigan Economic Development Corporation, Michigan Manufacturering Technology Center, and US Center for Advanced Manufacturing ADDITIONAL LEADERS IN INDUSTRY, ACADEMIA AND GOVERNMENT IN ATTENDANCE: Tom Kelly - Executive Director & Chief Executive Officer, Automation Alley Henry Aigbedo - Professor of Operations and Supply Chain Management, Oakland University Mohamed Arfaoui - VP Relationship Manager, PNC Financial Services Group Brad Baumgartner - VP of Sales, Reliant Industries Jeremiah Beebe - Sales Manager, DSV Mark Brucki - Associate Vice President, Economic Development, Lawrence Technological University Bella Brykalski - Sr. Search Consultant, Contract Professionals, Inc. Toussaint Carpenter - President & Fractional CIO CMIT Solutions of Northwest Metro Detroit Donovan Cotter - Founder, Cotter Automations Ron Crabtree - CEO, MetaOps Inc Yuji Fujii - Director of Emerging Technologies, Washtenaw Community College Ron Gillingham - Founder, Weigeaux Mobility Technologies John Gnotek - Strategic AI Advisor, Gnotek Ai George Greenough - Founder Principal, G2 Create Wesam Iwas - Managing Director, Ankercloud Inc. Kevin Kalb - Chief of Staff, TriOrb USA Al Khavari - VP Business Development, Reliant Industries Soren Knudsen - Director of AI Strategy, Solidigm Mark Kruckeberg - Managing Partner, Soltec Inc. Sanjay Kumar - Principal/Owner, OTai - IOT and Digital Al Lecz - Advanced Transportation Center Director, Washtenaw Community College Hendrik Lojek - Founder & Principal , ForgeShift Advisory Eric Lynch - Director of Business Development, build/create studios Barb Maclachlan - President, QSG Joe Marin - Sales Director, KMS Technology Rebekah McCarter - Lead Supplier Scout/CONNEX Administrator, Michigan Manufacturing Technology Center Patrick Mondro - SVP - Group Manager, PNC Financial Services Group Murat Muftari - President, Strategize Group Troy Neva - Director of Sales – Automotive Vertical Market, Siemens Digital Industries Julie Oldham - Business Growth Consultant, Michigan Small Business Development Center Gregg Ozga - Shareholder, Butzel Bruce Pittman - Tech, LABORATORIO ELETTROFISICO USA, Inc. Philip Scangas - Senior Project Manager, Quality Support Group, Inc. Steve Schultz - Director of R&D and Advanced Manufacturing, Dakkota Integrated Systems, L.L.C. James Semerad - CEO, JMSI Vision LLC Terry Senish - Business Development Manager, Wolverine Bronze Company Hazaa Shahit - Sr. Business Developer, Wipfli Sree Sivasankaran - Director of Sales and Product Strategy, MiNDCAN Inc. Rene Sommers - K12 Career Readiness Consultant, Oakland Schools Patric Stadtfeld - Regional Chief Procurement Officer - Americas, Siemens Digital Industries George Stasiw - Senior Account Executive, Samsung Ryan Szymanski - Director of Strategic Partnerships & Growth, mCubed Staffing Atsushi Tanase - Business Development Manager, OKAYA USA Mitch Tucker - Regional Sales Consultant , HID Global Jeff Watt - Senior People & Leadership Advisor | Founder, REAL WORLD HR SOLUTIONS Deanna Waun - Principal Consultant, MTB Consulting advisor for Solidigm

How AI Is Reshaping the Manufacturing Supply Chain 6

or 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.” 7

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.

“The number one challenge any small or medium-sized manufacturer has, whether here or anywhere else, is visibility.” Patric Stadtfeld Chief Procurement Officer, Americas Siemens

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. 9

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. 11

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. 12

“We understand data, and data has to be clean, it has to be good, but there’s still that experience that you bring to the table to interpret that data.” Rebekah McCarter Lead Supplier Scout Michigan Manufacturing Technology Center

“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. 14

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.” 15

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.

“What AI is good at is taking data and helping decision-makers make real-time decisions.”

John Gnotek AI Strategy Advisor Gnotek AI

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. 17

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. 18

“What we are seeing is that the best results come from combing AI with human intelligence.”

Henry Aigbedo

Professor of Operations and Supply Chain Management, Oakland University

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.

EXPERT INSIGHTS

Effective Supply Chain Risk Management: An Imperative for Today’s Executives and Managers

Henry Aigbedo, Ph.D.,

Professor of Operations and Supply Chain Management, Oakland University

Organizations face numerous challenges which must be skillfully addressed in order to achieve the desired success. Their risk exposures are based on several factors such as: size, the nature of the organization, the industry affiliation, and global footprint, among others.

Classifying Supply Chain Risks Risks can be classified in several ways. One of such classifications comprise of 4 elements: (1) Strategic risks such as threat of new entrants who could become competitors or threats due to new technology; (2) Financial risks such as those resulting from tariffs or exchange rate fluctuations; (3) Hazard risks such as pandemics or natural disasters, including tornedos and earthquakes; and (4) Operations risks such as those arising from poor product quality from suppliers, system disablement from computer virus/hacking, or demand/supply risk due to inadequate demand/supply. 22

Since all organizations are part of supply chains, their risks are often propagated through multiple organizations. For example, when a customer faces financial distress or becomes bankrupt, this affects its suppliers. This was the case when General Motors and Chrysler (now Stellantis) were at the brink of bankruptcy during the 2008-2009 global financial crises. Many suppliers experienced the same fate since they depended largely on these 2 automotive behemoths. Fortunately, the United States (US) Government rescued General Motors and Chrysler through major restructuring initiatives that were enabled by then US President Barack Obama’s Administration. Risk management has always been important to executives and managers. However, it is safe to say that it has recently become even more important. For example, consider the fact that many firms are yet to fully recover from the devastation caused by the COVID-19 pandemic. There is also the changing business landscape brought about by Artificial Intelligence (AI) and active wars across the globe such as the Russia-Ukraine war and the United States-Iran war. Furthermore, there are other tension spots around the world that have the potential of resulting in wars. Added to these challenges are natural disasters over which we have no control. In the remaining part of this article, I will briefly share some of my thoughts on actions we need to take under the prevailing circumstances. They are based on my more than 25 years of experience teaching supply chain management (SCM); conducting research on many SCM topics – including supply chain risk management, participation in Harvard Business School’s webinars, some of which relate to this topic; my direct interactions with company executives; as well as my own industry work experience. 23

Risks Typically have Extensive and Significant Impacts The intercontinental and extensive structure of organizations’ supply chains, which has major impacts on risks and how they should be effectively managed, is often underestimated by executives. Let us examine the following example to buttress my point. The ongoing war between the US and Iran has led to closure of the Strait of Hormuz, the pathway for a significant proportion of oil used around the world. Specifically, about 20% of global oil and Liquified Natural Gas (LNG) needs and 30% of global fertilizer needs go through this route. CBS News recently reported related developments in the Red Sea area of the Middle East. The resulting increase in global oil prices has a snowball effect on all sectors of the world economy, both in direct and indirect ways. Aside from refined petroleum which powers cars and trucks, crude oil byproducts are essential inputs for many manufactured items; including plastics, which are component parts of many industrial products such as automobiles and robots. In fact, over 6,000 products that people use daily; including bags, shoes, tires, some clothing, are made from byproducts of crude oil. Furthermore, since transportation is a key supply chain process, increased fuel prices lead to increases in prices of most products on store shelves, as well as cost of operations for businesses, including restaurants and hair salons. Similarly, a blockage caused by a 1312-foot-long boat in the Suez Canal (nestled between Egypt and Saudi Arabia) for about 7 days in April 2021 led to an estimated loss of $10 billion in global business. This is because about 12% of global trade goes through the Suez Canal every day.

What Should We Do? What are some key things executives and managers should do?

Understand Your Supply Chain

Oftentimes, we limit our interactions and knowledge to our immediate partners such as direct suppliers or customers. Clearly, this is inadequate when seeking to manage risks effectively as the source of the risk may not be from the immediate partners. Let us consider, for instance, poor quality product from an automotive Original Equipment Manufacturer (OEM). While the processes of the OEM and its direct suppliers may be functioning well, the real quality problem may be from a Tier 2 or Tier 3 supplier. Clear documentation of the suppliers’ processes at multiple tiers facilitates traceability. Also, it is noteworthy that while consolidation within supply chains has several benefits, it can also pose traceability challenges as demonstrated in the July 2026 outbreak of the Cyclospora parasite in the US that is linked to iceberg lettuce (Cyclospora outbreak shows risks of consolidated food supply). 25

Identify, Understand, and Manage Your Risks

One traditional way of managing risks is by assessing their associated vulnerabilities. This involves estimating both the likelihood of occurrence of an event and its impact – including financial impact – on the organization. The highest vulnerability risks are those that are most likely to occur and that have very severe impact on the organization. This information for the various risks is used to prioritize causes of action. More recent research by Massachusetts Institute of Technology’s (MIT’s) Dr. Simchi-Levi indicates that the above approach may be inadequate in some cases. Thus, he has introduced the concepts of Risk Exposure Index (REI) and Time to Recovery (TtR) for nodes in the supply chain. Outsourcing is a way firms use to reduce their direct risk exposure. However, Delta Airlines is currently reaping the benefits of the opposite – vertical integration (owning its own refinery) – during this period of the US-Iran war, where there is significant increase in the price of aviation fuel (Delta’s Ace in the Hole for Surging Jet Fuel Costs: Its Own Refinery - WSJ). Also, circumstances may require having some excess capacity as a way to manage risks (build resilience) even though it may be more costly in the short term.

Diversify Your Operations

This is basically in line with the adage: “Do not put all your eggs in one basket”. Diverse portfolios are generally known to do better. Diversification can be a combination of various forms: Business diversification could mean serving clients in multiple industries; Spatial diversification could mean having operations in different regions or countries; and Partner diversification could mean having multiple suppliers for a given product or part.

Invest in Your Personnel and in Appropriate Technology

Technology can be leveraged to assess, forecast, and analyze a firm’s risk exposure by incorporating information from its supply chain. AI is beginning to play a crucial role in this regard. The human element is no less important as the combination of human intelligence and AI has the potential to accomplish more than either one can do by itself. Therefore, companies should not only invest in technology but they should invest in training their employees adequately.

Oakland University is a nationally recognized doctoral university of high research activity located on 1,443 acres of scenic land in the cities of Rochester Hills and Auburn Hills in Oakland County, Michigan. The University has 142 bachelor’s degree programs and 138 graduate degree and certificate programs. Academics include programs in the College of Arts and Sciences, School of Business Administration, School of Education and Human Services, School of Engineering and Computer Science, School of Health Sciences, School of Medicine and School of Nursing. As an anchor institution in southeastern Michigan, Oakland University is committed to building ongoing and collaborative relationships with the surrounding communities. Community and civic engagement enhances the lives of our students and has a positive impact on our broader community.

EXPERT INSIGHTS

Driving the Future Supply Chain: Building Michigan’s Automotive Digital Workforce

Yuji Fujii

Director of Emerging Technologies, Washtenaw Community College

Michigan remains one of the world’s most important automotive and mobility ecosystems, but long-standing strengths in product engineering, manufacturing, and supplier execution are being tested by rapid technological change. As the industry accelerates toward electrification, software-defined vehicles, connected technologies, and Industry 4.0 manufacturing, companies across the state must prepare workers for a technical environment that is significantly different from the one that has supported traditional vehicle production. Michigan’s automotive supply chain is facing a critical workforce development moment. Digital technologies, semiconductors, microelectronics, and power electronics are becoming central to vehicle systems, including vehicle control units, advanced driver assistance systems, battery management systems, electric drives, and control software. At the same time, Industry 4.0 technologies are transforming prototyping and production processes through connected machines, robotics, digital twins, and data analytics. These innovations enable software-defined automation, predictive maintenance, digital work instructions, and smart production scheduling. 28

Supply chain dynamics often resemble rush-hour traffic, where a single disruption can slow or even halt the movement of the entire system. Modern vehicle development requires a coordinated flow of knowledge, data, and goods across OEMs, suppliers, engineering teams, and manufacturing operations. Workforce capabilities must evolve to support robust product design, rapid digital prototyping, cybersecurity awareness, and highly automated production processes with minimal downtime. Emerging technologies are increasing demand for workers who understand both traditional automotive processes and new digital capabilities, including software, data-driven systems, electronics hardware, and artificial intelligence (AI). The rapid advancement and broad accessibility of AI tools deserve particular attention because these technologies are increasingly being integrated into engineering, manufacturing, and business operations. Michigan has an opportunity to strengthen its competitiveness by building practical training pathways for digitally skilled technicians, technologists, engineers, and business professionals. There is a greater need than ever to bring together automotive employers, Tier 1, Tier 2, and Tier 3 suppliers, educators, workforce agencies, economic development partners, technology providers, and industry leaders to address the digital

talent challenges created by the rapid integration of emerging technologies. OEMs and large Tier 1 suppliers often possess deeper technical resources, internal academies, specialized laboratories, and dedicated experts in vehicle engineering, digital prototyping, systems engineering, advanced manufacturing, and digital business management. Many have experimented with AI technologies for years and now successfully apply sophisticated AI tools across engineering and business functions. Smaller firms may not have these advantages, yet they must still respond to business customer expectations for automation, quality, cost, delivery, and technology readiness. For these companies, digital workforce development must be practical, affordable, modular, and directly connected to business operations, plant-floor activities, and product development needs. Short-term workshops, vendor-led training, community college programs, employer collaboratives, and targeted grant opportunities can all play an important role in helping organizations build capabilities without disrupting productivity. Another central question for Michigan industry is whether companies should hire new talent or reskill incumbent workers. The answer is likely both. Experienced technicians bring valuable process knowledge, equipment familiarity, quality awareness, troubleshooting expertise, and op-

erational business experience that cannot be easily replaced. With the right training, many can transition into hybrid roles involving automation, mechatronics, digital manufacturing, EV safety, electronic systems support, and AI-enabled business management. At the same time, companies may need to selectively recruit specialists in Industry 4.0 automation, semiconductor integration, power electronics testing, and cybersecurity. Data specialists are particularly critical when AI is used to support business decision-making because expertise in neural network models, data preparation, and training methods is often necessary to interpret predictive model behavior. The challenge for Michigan’s supply chain is not simply preparing more workers for automotive careers. It is ensuring that workforce capabilities evolve alongside the technologies reshaping the industry. Expanding digital, electronic, semiconductor, and advanced technology capabilities, while building on Michigan’s existing engineering, manufacturing, and business expertise, will be critical to maintaining the state’s global automotive leadership.

Washtenaw Community College was established in 1965, when Washtenaw County voters passed a millage to provide financial support for a countywide community college. WCC opened with a 6-member Board of Trustees, a newly-appointed president, and a parcel of land in Ann Arbor and Superior Townships. Classes started in September 1966 on a temporary campus at Willow Run, with 1200 students enrolled in 30 occupational programs and a transfer curriculum. From this modest beginning, WCC has grown into one of the nation’s premier community colleges with an annual enrollment of more than 18,000 students in credit classes each year, and thousands more in Economic and College Development classes. Our diverse student body includes more than 1,000 students from 100+ foreign countries. Our 285-acre campus, operating resources, technology-enabled facilities, and instructional quality are the envy of post-secondary education institutions near and far. 31

Recommendations for

INDUSTRY

upply chains are vulnerable to disruptions, slowdowns and other complications caused by more than tariffs and international political uncertainty. Manufacturers face challenges related to workforce readiness, data availability, system integration, and adopting the latest technology.

Developing a strategic approach utilizing AI, automation, advanced analytics, and digital technologies will modernize supply chain operations, improve overall performance, and enable adaptive responses to evolving market demands. The following recommendations highlight actions to better equip industry leaders to improve decision-making that will improve long-term outcomes. Start AI adoption with focused supply chain use cases. Begin AI integration with specific, measurable applications such as demand forecasting, inventory optimization, supplier risk scoring, or production scheduling. Narrow use cases allow companies to test data quality, validate ROI, and build internal confidence before scaling across the entire manufacturing enterprise. This approach reduces the risk of workflow disruption and aids teams in identifying where AI creates value. Consider data readiness as the essential starting point for supply chains utilizing AI. Before investing heavily in AI tools, assess whether supply chain data is accurate, accessible, standardized, and connected across the entire manufacturing system. When data quality is poor, forecasting, inventory management, and automated decision-making suffer. Companies should prioritize cleaning, integrating, and governing data across ERP, MES, supplier, logistics, and inventory systems.

Build real-time visibility across supplier, inventory, and logistics networks. Invest resources in tools that provide real-time insights into inventory levels, supplier status, logistics delays, and production capacity. Increased visibility enables organizations to transition from reactive choices to proactive supply chain control. AI-driven forecasting can elevate inventory precision and curb stock shortages. Leverage AI forecasting tools to examine demand, supplier performance, lead times, and market instability. The objective is to reduce overstocking, prevent stock shortages, and enhance working capital efficiency. Use AI to assist, but not replace, human supply chain expertise in areas where judgment and context remain essential. Create cross-functional supply chain technology teams. Don’t view supply chain AI and automation as separate IT initiatives. Successful implementation requires collaboration among operations, procurement, logistics, finance, production, cybersecurity, and frontline employees. Cross-functional teams help ensure that new tools solve operational problems and improve existing workflows. Strategically phased in automation projects minimize downtime and workforce resistance. Manufacturers ought to implement automation gradually, starting with repetitive, high-friction, or data-intensive tasks where disruption is minimal and value is straightforward to assess. Phased implementation lets employees adjust, managers improve workflows, and leadership assess performance before expanding. Hybrid models that combine human oversight with automated systems can reduce resistance on the factory floor.

Increased visibility enables organizations to transition from reactive choices to proactive supply chain control.

Invest in workforce training before scaling AI and automation. Training employees to use and supervise AI supply chain tools is crucial for manufacturers. Workforce readiness remains one of the major obstacles to adoption, especially when employees fear automation will disrupt roles or eliminate jobs. Training needs to focus on hands-on abilities like data literacy, digital workflow, predictive analysis, and working with machines. Leverage supply chain control towers or dashboards for accelerated decision-making. Manufacturers should explore dashboards or control tower models that integrate data from procurement, production, inventory, transportation, and suppliers into one unified decision-making platform. Leaders can use these tools to detect disruptions sooner, evaluate different scenarios, and organize departmental actions. Enhance cybersecurity and oversight for supply chain data. Strengthening cybersecurity and data governance is essential for manufacturers as they connect additional suppliers, machines, software platforms, and AI systems. AI-enabled supply chain tools depend on trusted data, secure access, and well-defined policies for how information is shared and used. Thorough cybersecurity risk assessments are a critical component every supply chain visibility or automation project. Measure AI and automation projects with operational KPIs. Setting clear success metrics is crucial for manufacturers prior to AI or automation implementation. Examples of valuable KPIs are forecast accuracy, inventory turnover, supplier punctuality, shorter lead times, prevented downtime, efficient labor, and customer order fulfillment. Clear metrics enable companies to determine if a pilot should continue, be changed, or be halted.

Recommendations for

ACADEMIA

anufacturers are increasingly adopting AI, advanced analytics, real-time visibility tools to navigate the complex and turbulent supply chain environment. Academia has an important role in preparing students to develop innovations to combat such challenges through research, skills development, and collaboration with industry and government to define specific needs and potential furniture workforce shortfalls.

Academic research can help bridge the gap between theoretical capabilities and real-world applications.

Academic institutions and the private sector working together will bridge the gap between emerging technologies and practical implementation by developing new knowledge, validating solutions, and providing access to industry experts, testing environments and applied research that will reduce adoption risk. The following recommendations will help prepare future supply chain specialists. Expand applied research on AI-driven supply chain decision-making. Increase applied research efforts focused on AI-powered demand forecasting, inventory optimization, supplier risk management, and production planning. Many AI technologies have shown potential, yet manufacturers continue to need practical direction on implementation, scalability, and ROI. Academic research can help bridge the gap between theoretical capabilities and real-world applications. Initiate industry-academic partnerships focused on supply chain data sharing. Collaboration between academic bodies and industry players like manufacturers, logistics experts, and tech businesses is crucial for developing research spaces that enable secure data sharing and enhance supply chain visibility. Real-world operational data enhances research quality and aids industry partners in evaluating emerging technologies. These partnerships can also identify common barriers to visibility and interoperability.

Integrate AI and supply chain analytics into the curriculum. Expand coursework that combines supply chain management, data analytics, artificial intelligence, and industrial operations. As manufacturers adopt more predictive analytics and AI planning tools, graduates need to have both technical and operational skills. Cross-disciplinary education aids in preparing students for upcoming positions in digital supply chain management. Establish labs for supply chain innovation and demonstration. Create demonstration environments where students, researchers, and manufacturers can evaluate AI, automation, digital twin, and visibility technologies providing practical testing environments that reduce adoption risk while accelerating technology transfer. Demonstration labs expose students to real-world manufacturing preparing them for challenges and solutions. Explore strategies for human-centered automation. Expand research into phased and hybrid automation approaches that minimize disruption while improving operational performance. Manufacturers often point to staff pushback and workflow interruptions when discussing automation adoption challenges. Academic studies can identify best practices for integrating human expertise and automated systems. Expand workforce research on AI adoption barriers. Examine the organizational, cultural, and workforce hurdles hindering AI and automation adoption in manufacturing supply chains. Addressing skill gaps, building trust, establishing governance, and clarifying job roles will aid organizations in adopting new technologies efficiently. Future workforce development and change-management strategies can be informed by research findings.

Crossdisciplinary education aids in preparing students for upcoming positions in digital supply chain management.

Explore cutting-edge research in supply chain resilience and predictive analytics. Continue developing predictive analytics models that help manufacturers anticipate disruptions, identify supply risks, and improve inventory planning. Proactive risk management has become crucial due to recent supply chain disruptions. Academic research offers manufacturers innovative tools and methods for boosting resilience and operational agility. Foster cross-disciplinary studies involving engineering, business, and data science. Multiple disciplines such as engineering, operations management, computer science, cybersecurity, and business strategy are now essential for supply chain modernization. Universities should encourage collaborative research efforts that address both the technical and organizational dimensions of digital transformation. Interdisciplinary approaches are more likely to generate practical solutions. Enhance the study of supply chain cybersecurity and data governance. As manufacturers increase connectivity across suppliers, logistics networks, and AI systems, academic institutions should prioritize research on cybersecurity, governance, and trusted data environments. To gain better visibility and protect intellectual property and operational data, secure information-sharing frameworks are essential. Organizations can use research to achieve a balance between transparency and security needs. Measure and publish best practices for AI and automation adoption. Collaboration between universities and manufacturers is needed to document AI and automation implementation results and develop evidence-based best practices to aid organizations that remain uncertain about where to begin or how to evaluate success. Companies can benefit from academic case studies and benchmarking research when modernizing their supply chains.

Recommendations for

GOVERNMENT

G By working together, public and private entities can build dependable structures for governing data and ensuring its seamless exchange.

overnment agencies play a vital role in strengthening supply chain resilience, accelerating technology adoption, and supporting the long-term competitiveness of manufacturers.

Public-sector policies, investments, and partnerships can help the manufacturing industry overcome barriers related to AI adoption, data sharing, workforce readiness, and automation implementation while reducing risk and encouraging innovation and adoption of the latest technology. The following are recommendations for the government to foster supply chain modernization, improve operational visibility, and promote the responsible integration of AI and automation technologies. Increase state-sponsored AI initiatives for supply chain strategy. Expand grant and technical assistance programs that help manufacturers pilot AI applications for demand forecasting, inventory optimization, and supply chain risk management. Despite acknowledging AI’s benefits, companies encounter obstacles such as high costs, an unprepared workforce, and poor data quality. Government-funded trials can decrease risk and deliver measurable financial returns before large-scale use. Fast-track the creation of regional supply chain data-sharing networks. Government agencies should support secure regional data-sharing ecosystems that enable manufacturers, suppliers, logistics providers, and distributors to exchange operational information in near real time. By improving visibility among supplier tiers, organizations can achieve quicker responses to disruptions, shortages, and demand changes, leading to greater resilience. By working together, public and private entities can build dependable structures for governing data and ensuring its seamless exchange.

Encourage the adoption of standardized supply chain data models. Federal agencies should foster the adoption of shared data standards for better integration of ERP, MES, and supply chain platforms. Data standardization overcomes silos, resulting in enhanced forecasting, more efficient inventory management, and timely decision-making. Consistent data structures are foundational for effective AI deployment across supply chains. Allocate more funds towards supply chain visibility solutions. To bolster supply chain efficiency, governments should offer more incentives for technologies that grant instant awareness of stock, vendor reliability, shipment progress, and manufacturing capabilities. Manufacturers consistently find that a lack of real-time information hinders their ability to make effective decisions. Enhancing digital supply chain technologies can boost responsiveness and lower operational risk. Support hybrid automation implementation models. Economic development agencies ought to encourage phased automation strategies that integrate human knowledge with automated workflows, instead of attempting wholesale replacements upfront. Hybrid approaches reduce workforce resistance, minimize operational disruption, and provide opportunities for continuous improvement. Incremental implementation can improve adoption rates and accelerate organizational learning. Develop more skilled workers for AI-integrated supply chains. Investing in workforce development is crucial for governments to equip employees for AI, robotics, and advanced automation. The most common hurdle for adoption continues to be workforce preparedness. Training programs focused on digital literacy, analytics, and automation management can help manufacturers achieve greater success with modernization efforts.

To bolster supply chain efficiency, governments should offer more incentives for technologies that grant instant awareness of stock, vendor reliability, shipment progress, and manufacturing capabilities.

Establish demonstration hubs for intelligent supply chain solutions. Support demonstration facilities where manufacturers can evaluate AI, digital twins, automation, and supply chain visibility technologies before making major investments. Practical experience can reduce uncertainty and help organizations identify practical use cases that align with their operational needs. Demonstration centers can also help manufacturers share what they know. Encourage the adoption of predictive analytics for supply chain risk management. To anticipate disruptions, government programs should enable manufacturers to harness predictive analytics. By using AI for forecasting, businesses can better plan demand, optimize inventory, and assess supplier risks, all while reacting more quickly to market shifts. The importance of these skills is rising within volatile international supply chains. Bolster data governance and cybersecurity mandates. To support organizations in growing AI-driven supply chains, governments need to offer guidance and incentives that improve cybersecurity, data governance, and supply chain transparency. To fully leverage AI benefits and reduce operational/compliance risks, secure and trustworthy data environments are essential. Enhanced governance can boost trust in automated decision-making. Incentivize incremental digital transformation strategies. Government programs should focus funding on approaches that encourage gradual modernization instead of mandating extensive digital transformation. To boost their success, many manufacturers introduce AI, automation, and visibility technologies step-by-step, without disrupting operations. By taking small steps, we can lessen downtime, boost employee support, and test outcomes before expanding our financial commitments.

INDUSTRY INSIGHT

From Pilot to Practice: How Manufacturers Can Build AI That Actually Lasts Artificial intelligence (AI) isn’t a conversation for the future of procurement; it’s already helping teams big and small work faster, make smarter decisions, and compete with greater confidence in a rapidly changing supply chain environment. The question is not whether to engage with AI, but how to do it in a way that is structured, sustainable, and centered on the people who will use it every day. For small and medium-sized manufacturers, the AI conversation can feel like something that’s only happening in large multinationals, not on the shop floor or in the purchasing department. And to be fair, the reality of AI has not always matched the hype. Implementations that promised transformation have sometimes delivered frustration, and organizations of every size have learned that AI is not a switch you flip. It’s a capability you build. But that is precisely why the moment is right for manufacturers who are willing to approach it with clear eyes and realistic expectations. The gap between organizations that are building AI fluency now and those waiting for the “right moment” is widening every quarter. 44

Two years ago, as AI began transforming business functions across industries, Siemens recognized that the greatest opportunity was not simply in deploying AI tools but in building an adoption model capable of scaling innovation across the organization. The company created a framework that combined experimentation, enterprise data governance, employee development, and change management to accelerate AI adoption while ensuring long-term business value. Recognizing that procurement challenges often differ by geography, business unit, and supply market, regional teams were given the freedom to pilot AI solutions that addressed local business needs. Over an 18-to-24-month proofof-concept period, teams explored various applications, including supplier risk monitoring, contract intelligence, spend analytics, negotiation preparation, market intelligence, and process automation.

Every pilot was structured as a genuine learning opportunity, with clear business objectives, defined success metrics, and documented lessons that could travel beyond the region where they originated. By encouraging experimentation close to the business while maintaining transparency across the organization, Siemens created an environment where innovation could flourish without losing strategic alignment. A formal process was created to evaluate what worked, what didn’t, and what was worth scaling, creating a continuous loop between regional innovation and enterprise adoption. Solutions that demonstrated measurable value were integrated into the broader procurement ecosystem; insights from initiatives that fell short helped prevent duplicated effort and sharpen future decision-making. The result: within the next year, Siemens will deploy a scaled, enterprise-wide multi-agent solution designed to assist across a range of core procurement functions, including sourcing, pricing, supplier risk monitoring, spend analytics, contract intelligence, and negotiation preparation.

One of the most important lessons to emerge from the proof-of-concept period was that meaningful AI adoption requires real investment, but that doesn’t necessarily mean deep pockets. Most small and medium-sized manufacturers aren’t in a position to fund large-scale innovation programs, and they don’t need to be. What matters is intentionality. Innovation stalls when it depends entirely on enthusiasm and spare capacity, regardless of company size. Dedicating the right resources is what separates a successful pilot from a project that never gets off the ground. That can look like a modest budget for one well-chosen tool, protected time for training, or a single internal champion with the authority to drive change. Treat AI adoption as a business investment, not a side project, and hold it to the same standard you would any other, with a clear problem to solve, a realistic sense of the value it could create, and a plan for what success looks like. Sustainability must also be built in from the start. A tool that solves a problem in one corner of the business doesn’t always translate to another. Siemens learned early to evaluate every initiative on not just whether it worked, but whether it could grow with the organization, integrating cleanly with existing systems, adapting across different functions, and continuing to deliver value over time. The question shifted from “does this work?” to “will this last?” That change in perspective is one any organization can apply, regardless of size. Something else to keep in mind is that AI is only as good as the data and processes behind it. Before any tool can be useful, an organization needs a clear understanding of how its processes actually work. Where are decisions made, where does information live, and where are the real friction points? AI cannot improve a process 47

that hasn’t been mapped, and it cannot automate a workflow that no one has clearly defined. Siemens used the AI adoption process as an opportunity to strengthen data governance and improve the quality and consistency of its procurement information. Better data and cleaner processes made the entire organization sharper. But the technology and data are only part of the story. After all, technology is only as good as the people who use it. It’s one thing to deploy a powerful tool; it’s an entirely different challenge to train the workforce to use it well. From the outset, Siemens invested in developing AI literacy, not just among technical teams or early adopters. Every employee, regardless of role or tenure, needs a foundational understanding of what AI can and cannot do, how to evaluate its outputs critically, and how to apply it responsibly. Equally important is what AI cannot replace. As procurement teams are freed from repetitive, data-heavy tasks, the skills that have always defined great procurement professionals — negotiation, supplier relationship management, ethical judgment, creative problem-solving, and the ability to influence across complex organizations — become more valuable, not less. The goal is not a workforce that defers to AI, but one that uses it as a force multiplier for distinctly human strengths. For every organization looking to incorporate AI into their processes, the path forward doesn’t require the resources of a global enterprise. It requires the same things Siemens committed to: a willingness to experiment, the discipline to learn from what doesn’t work, a serious investment in data and people, and the conviction that AI is not a destination but a capability you build over time. The companies that get this right won’t just be more efficient; they’ll be more resilient, more strategic, and better positioned to compete in a supply chain environment that will keep changing whether they’re ready or not. 48

Patric Stadtfeld Regional CPO, Americas

Siemens Corporation is a U.S. subsidiary of Siemens AG, a leading technology company focused on industry, infrastructure, transport, and healthcare. The company’s purpose is to create technology to transform the everyday, for everyone. By combining the real and the digital worlds, Siemens empowers customers to accelerate their digital and sustainability transformations, making factories more efficient, cities more livable, and transportation more sustainable. A leader in industrial AI, Siemens leverages its deep domain know-how to apply AI – including generative AI – to real-world applications, making AI accessible and impactful for customers across diverse industries. Siemens also owns a majority stake in the publicly listed company Siemens Healthineers, a leading global medical technology provider pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably. In fiscal year 2025, which ended on September 30, 2025, the Siemens Group USA generated revenue of $24.427 billion with 25 manufacturing sites across the U.S. and more than 50,000 employees serving customers in all 50 states and Puerto Rico.

INDUSTRY INSIGHT

From Visibility to Understanding: Aligning Supply Chain Strategy in the Age of AI

Automation is advancing rapidly across manufacturing. Robotics, sensors, ERP systems, MES platforms, dashboards, supplier portals, and analytics tools have given manufacturers more information than ever before. Yet despite this progress, many supply chains remain difficult to coordinate, especially across OEMs, Tier 1s, and smaller suppliers. The issue is not simply that companies lack technology. In many cases, they already have more systems, reports, and data than their 50

teams can reasonably interpret. The deeper challenge is alignment. Information exists, but it is fragmented across departments, companies, systems, customers, and suppliers. A planner may see one version of demand. Purchasing may see another version of supplier risk. Operations may understand capacity constraints that are not visible upstream or downstream. Leadership may receive summaries after the moment for action has already passed. The next step for supply chain competitiveness is not merely more automation. It is better operational understanding.

Visibility Is Not the Same as Understanding Manufacturers have invested heavily in visibility. Dashboards show inventory levels, delivery dates, production status, quality performance, and supplier activity. These tools are valuable. They help people see what is happening. But seeing what is happening is not the same as understanding what it means. A dashboard may show that inventory is below target, a supplier shipment is late, a work center is constrained, or a customer forecast has changed. But the decision-maker still has to answer harder questions: • Why did this happen? • Which commitments are now at risk? • What options are available? • What trade-offs does each option create? • Who needs to act, and when?

Visibility tells us what is happening. Understanding tells us why it matters. That distinction is especially important in supply chain work because most disruptions are not isolated events. A late shipment may affect production scheduling, customer commitments, labor planning, transportation costs, and cash flow. A forecast change may require adjustments across purchasing, staffing, production, and supplier communication.

The challenge is not only getting the data. The challenge is connecting data to the decisions people must make while there is still time to act. Not after the fact.

The Context Gap Across the Supply Chain The context gap becomes even more difficult across company boundaries. OEMs, Tier 1s, and smaller suppliers rarely operate from a single shared picture. Forecasts may change upstream before smaller suppliers fully understand the implications. Customer portals 52

may require different formats, update cycles, and reporting expectations. Suppliers may hesitate to share too much operational detail because of competitive, contractual, or intellectual property concerns. At the same time, OEMs and Tier 1s may not always see the real constraints forming deeper in the supply base until they become late deliveries, shortages, premium freight, or production disruption. The result is not simply a lack of data sharing. It is a lack of shared operational context. This is especially difficult for small and mid-sized manufacturers. Many are expected to respond with the speed and discipline of larger enterprises, but they operate with lean teams, limited IT resources, and a practical mix of ERP systems, spreadsheets, email, tribal knowledge, and customer portals. For these companies, the key question is: Where are we repeatedly making important supply chain decisions with incomplete, delayed, or fragmented information?

Where AI Actually Helps Artificial intelligence is frequently discussed as if its main purpose is automation. That framing is too narrow. In supply chain environments, AI’s near-term value is not replacing planners, buyers, schedulers, operators, or managers. Its value is helping those people understand complex situations faster and make better decisions. AI can assist by summarizing communications, comparing forecast changes against capacity, surfacing inventory risks, explaining exceptions, and organizing scattered operational knowledge. Organizational situational awareness matters: AI becomes more useful when it can connect signals across functions, systems, and decision points. Consider a common automotive supply chain scenario. A smaller supplier receives a forecast change through a customer portal. A related engineering clarification is buried in email. A material constraint is known only to purchasing. The Tier 1 sees risk as a delivery issue. The supplier sees it as a capacity and material issue. The OEM may not see the problem at all until it threatens production timing. An AI-supported workflow would not “solve” the supply chain. But it could help assemble the scattered context: what changed, which parts and orders are affected, what communications already occurred, what constraints exist, and what escalation options are available. The human still makes the decision. But the decision is made with better orientation.

Judgment Before Automation At Gnotek Ai, I describe the right adoption sequence as judgment before automation. Many organizations begin with the wrong question: “Where can we use AI?” That question encourages tool-first thinking. It can lead companies to chase software before they have defined the operational problem, the decision being supported, the data required, the risk involved, or the accountability needed. A better starting point is: “What decision are we trying to improve?” That question matters because automation can amplify weakness as easily as it can improve performance. If a process is poorly understood, automating it may simply make confusion move faster. If data definitions are inconsistent, automation may create false confidence. If accountability is unclear, AI-generated recommendations may introduce new risk instead of reducing it.

A Practical Path Forward For manufacturers looking to align automation, supply chain strategy, and AI adoption, a practical path can begin with five steps. First, map recurring decisions. Identify where teams repeatedly make decisions under time pressure. Second, identify missing context. What information is hard to obtain, delayed, or trapped in someone’s inbox, spreadsheet, or undocumented process knowledge? Third, start with decision support. Use AI to summarize, explain, compare, and recommend before asking it to automate. Fourth, govern the risk. Define what data can be used, what tools are approved, who reviews AI outputs, and who remains accountable. Fifth, automate gradually. Once a workflow is understood and trusted, begin with repeatable, lower-risk steps before expanding into deeper integration. Supply chain transformation is not only technical. It is organizational. People need to understand why a system is being introduced, how it supports their work, and what risks it creates.

The Leadership Challenge GroupSupply chain alignment requires more than connected systems. It requires connected understanding across organizations. For OEMs, Tier 1s, and smaller suppliers, the opportunity is to move beyond fragmented visibility toward shared operational understanding. That means:

Connecting data to context > context to decisions > decisions to coordinated action

AI can help make that possible. But only if it is introduced with discipline. The future of supply chain competitiveness will not belong simply to the companies that automate the most. It will belong to the companies that understand their operations clearly enough to know what should be automated, what should be guided, and where human judgment must remain central. Automation should not be the starting point. Better judgment should be.

John Gnotek Strategic AI Advisor Gnotek Ai helps manufacturers and business leaders adopt artificial intelligence with clarity, discipline, and practical judgment. Rather than starting with tools or automation for its own sake, Gnotek Ai helps organizations identify where AI can improve understanding, decision-making, operational performance, and responsible innovation. Its work focuses on AI strategy, opportunity mapping, governance, executive advisory, and practical adoption pathways for small and mid-sized manufacturers. Grounded in the principle of judgment before automation, Gnotek Ai helps companies move from AI curiosity or scattered experimentation toward purposeful, governed use. Services include AI readiness assessments, use-case discovery, implementation guidance, responsible AI frameworks, and leadership support. The goal is not to replace human expertise, but to help teams better interpret information, reduce uncertainty, and act with greater confidence. Gnotek Ai works at the intersection of manufacturing, operational intelligence, AI governance, and human-centered technology adoption. 57

INDUSTRY INSIGHT

The Complexity Tax: Why Hidden Supply Chain Complexity is Becoming Manufacturing’s Largest Unseen Cost Complexity Was Never the Problem Manufacturing has never been more technologically capable. Engineering teams collaborate seamlessly across continents. Artificial intelligence is accelerating design decisions while global supply chains connect thousands of suppliers through increasingly sophisticated digital platforms. By almost every measure, manufacturers have more tools, more data, and more computing power than at any point in history. Yet many of the world’s most successful manufacturers continue to wrestle with delayed product launches, fragmented engineering decisions, duplicate parts, inconsistent supplier information, and rising operational costs. Why? Despite billions of dollars invested in ERP systems, PLM platforms, procurement applications, and digital transformation initiatives, those problems continue to persist. What if the issue was never a lack of technology? What if the real challenge is the accumulated cost of complexity?

Over the past several years, I’ve had countless conversations with executives at some of the world’s largest manufacturers. Increasingly, they’re arriving at the same conclusion: complexity has become one of the biggest obstacles to improving supply chain performance. Nowhere is this more apparent than in the automotive industry, where established manufacturers compete against EV startups capable of bringing new products to market in months rather than years. Those startups don’t necessarily have better technology or larger budgets. They simply have far less organizational complexity. One of the ironies of manufacturing is that success today often creates the very conditions that slow future success. As companies grow, they acquire competitors, introduce new product lines, expand into new markets, build additional plants, and onboard thousands of new suppliers. Every one of those decisions is strategically sound. Collectively, however, they create more systems, more processes, more data, and more opportunities for information to become fragmented across the enterprise.

It dawned on me that there is a Complexity Tax being levied against large successful manufacturers, where an accumulated economic cost emerges. This Complexity Tax manifests when an organization’s growth outpaces its ability to unify products, suppliers, processes, and information. Although seemingly ‘hidden’, its intuitively obvious how easily this complexity ‘happens’ in successful organizations, as represented in the following graphic.

Unlike most business costs, the Complexity Tax rarely appears on a financial statement. It isn’t tracked as a KPI or highlighted in a board presentation. Instead, it quietly surfaces as slower innovation, longer product development cycles, delayed sourcing decisions, duplicate engineering effort, inconsistent supplier data, missed market opportunities, and a reduced ability to respond to change. Over time, what begins as operational friction becomes a measurable competitive disadvantage. 60

Complexity Tax in Action It usually begins as a routine design review. Engineering finishes the latest design, but procurement discovers a few preferred components are no longer available. Manufacturing raises questions about buildability while finance updates commodity assumptions. Quality asks for additional supplier validation, and logistics identifies a new tariff risk. These issues aren’t unusual, and each makes perfect sense on its own, but any one of them can cause the launch date to slip 2 to 4 weeks. That’s the Complexity Tax in action. In a recent conversation with leaders at a large automotive OEM, I explained how Samsung’s Caidentia Design to Source platform could create a common view of the bill of materials across functions, plants, and systems. They immediately saw the value, but one executive summed up the challenge simply: “This is extremely difficult to take on given our 30 plants, 12 ERP systems and the 40 countries we operate in.” That comment captures the Complexity Tax perfectly. The organization had become so complex that even addressing the problem felt overwhelming. But it doesn’t have to be solved all at once. Manufacturers can start with a product, business unit, or specific use case, prove the value, and expand over time.

The Invisible Factory A second factory has quietly emerged inside every organization: the one where information moves. Engineering hands work to procurement, procurement collaborates with suppliers, and information flows from manufacturing through logistics. Every product passes through this Invisible Factory. Yet unlike the physical factory, it is rarely mapped, measured, or managed with the same discipline. As complexity grows, information moves more slowly, decisions take longer, and the impact often isn’t visible until a launch date begins to slip.

Where the Complexity Tax Comes From As companies succeed, they acquire other businesses, expand into new markets, launch additional product lines, build new plants, and add suppliers around the world. Each of those decisions is the right one for the business. Over time, however, they also introduce more systems, more processes, and more disconnected information. That’s how the Complexity Tax begins to accumulate. The instinct is to consolidate everything onto one global system, which can cost hundreds of million dollars. The more useful question isn’t “how do we get everyone onto one system?” It’s “what has to be unified even while multiple systems remain?” Having too many systems is the most visible symptom. The bigger problem, however, is that those systems don’t agree on the basics. A part may carry different identifiers depending on the plant, supplier, division, or region. An engineering bill of materials may not align with the manufacturing, sourcing, or service version of the same product. Even supplier names can vary by legal entity, while “cost” might mean standard cost, purchase price, landed cost, or quoted cost depending on who’s in the conversation.

The Invisible Factory connects the flow of information across engineering, sourcing, suppliers, and manufacturing around the bill of materials—the natural point where these functions come together and where Samsung has helped many clients create a unified view.

Organizational Speed Is the Real Advantage When people look at manufacturers that consistently out-execute their competitors, they often point to labor costs, scale, or government support. I think the bigger advantage is organizational speed: how quickly can a company turn a product decision into coordinated action across engineering, sourcing, suppliers, and manufacturing? That’s where many established manufacturers, weighed down by decades of accumulated systems and processes, struggle the most. 63

The BOM is where it All Comes Together The answer isn’t to replace every ERP or PLM system with a single platform. That may be the long-term vision, but most manufacturers can’t wait years to make better product decisions. A more practical approach is to connect the systems they already have around the bill of materials—the place where engineering, sourcing, suppliers, and manufacturing naturally come together. When those teams are working from the same product context, decisions happen faster, collaboration gets easier, and the Complexity Tax starts to come down. Complexity isn’t going away. It comes with the scale and growth of a successful manufacturing business. The opportunity is to manage it better, so it no longer determines how quickly the business can move.

George Stasiw Senior Account Executive Author bio: George Stasiw is a recognized thought leader in manufacturing, supply chain, and technology innovation. With a foundation built at IBM, advanced AI studies at MIT, and experience driving Industry 4.0 initiatives at Automation Alley, he is now a voice for design-to-source solutions at Samsung SDS, helping manufacturers with complex supply chains modernize and compete globally. George thrives when brainstorming with organizations looking to enhance procurement processes, optimize BOM management, and connect their design and sourcing teams. Start a conversation with him at g.stasiw@samsung.com. About Samsung SDS America Samsung SDS America (SDSA) is the North American subsidiary of Samsung SDS, a global leader in cloud and digital transformation. We help organizations boost productivity, strengthen security, and make smarter decisions with enterprise-grade technologies, including secure mobility, generative AI, cloud services, and procurement solutions with full design-to-source-to-pay capabilities through platforms like Caidentia. Serving industries from government, financial services, retail, and healthcare to manufacturing and technology, SDSA uses data-driven insights and automation to help businesses thrive in today’s connected world. As part of Samsung SDS, a globally recognized ICT leader, we deliver innovative solutions that shape the future of business across North America. About Caidentia Caidentia is Samsung SDS’s AI-driven procurement platform covering the full design-to-source-to-pay process. It connects engineering, sourcing, and supplier teams to streamline collaboration and decision-making. The platform delivers real-time cost analysis, alternative part options, and supplier insights while using AI to automate sourcing tasks, forecast pricing, and improve contract compliance. Designed for manufacturing, high tech, automotive, and consumer electronics, Caidentia helps organizations make faster, more cost-effective sourcing decisions and strengthen supply chain resilience.

Key Takeaways on 1. Supply chain resilience now matters as much as cost efficiency:

2. Supply chains should be managed as interconnected ecosystems:

Manufacturers must build networks capable of adapting to geopolitical shifts, transportation disruptions, changing trade conditions and other unexpected events.

Manufacturers need visibility across suppliers, operations, data and partners rather than optimizing individual links in isolation.

4. Supplier visibility and earlier collaboration can drive innovation: 3. Nearshoring is creating new opportunities for SMEs: Small and midsize manufacturers can compete through specialization, flexibility, shorter lead times, technical expertise and the ability to collaborate closely with customers.

Manufacturers benefit when they can identify specialized supplier capabilities and bring those suppliers into product and process development before major decisions are finalized.

Supply Chain & Strategy 5. AI can transform supply chain visibility into faster decision-making: Its greatest potential lies in connecting information across procurement, production, logistics and suppliers to help manufacturers understand why disruptions are occurring and respond while options remain available.

6. AI is only as effective as the data and systems supporting it: Clean data, standardized processes, connected technology environments and strong governance are essential foundations for meaningful AI adoption.

7. Technology cannot replace human expertise and trusted relationships: The strongest supply chains will combine AI and digital tools with critical thinking, institutional knowledge, workforce development and collaboration between manufacturers and suppliers.

SOURCE LIST Federal Reserve Bank of New York - Global Supply Chain Pressure Index https://www.newyorkfed.org/research/policy/gscpi#/interactive U.S. Bureau of Economic Analysis and U.S. Census Bureau - U.S. International Trade in Goods and Services https://www.bea.gov/data/intl-trade-investment/international-trade-goods-and-services

National Institute of Standards and Technology (NIST) Manufacturing Extension Partnership - Manufacturing Extension Partnership Resources https://www.nist.gov/mep Council of Supply Chain Management Professionals (CSCMP) - Supply Chain Management Resources https://cscmp.org/

Gartner Survey Shows AI is Not Driving Supply Chain Operating Model Transformation https://www.gartner.com/en/newsroom/press-releases/2026-05-06-gartnersurvey-shows-ai-is-not-driving-supply-chain-operating-model-transformation

Frontiers in Artificial Intelligence / National Library of Medicine - Examining the Integration of Artificial Intelligence in Supply Chain Management from Industry 4.0 to 6.0: A Systematic Literature Review https://pmc.ncbi.nlm.nih.gov/articles/PMC11788849/

Gartner Survey Finds Technology Integration and Talent Perceived as Key Roadblocks to Scaling AI in Supply Chain https://www.gartner.com/en/newsroom/press-releases/2026-04-29-gartnersurvey-finds-technology-integration-and-talent-perceived-as-key-roadblocks-toscaling-ai-in-supply-chain

MDPI – Information - Human-Centered and Hybrid Automation Research https://www.mdpi.com/2078-2489/16/1/26

National Institute of Standards and Technology (NIST) - Application of Artificial Intelligence in Supply Chain Management – Opportunities and Challenges, a Workshop Report https://www.nist.gov/publications/application-artificial-intelligence-supply-chain-management-opportunities-and National Institute of Standards and Technology (NIST) Manufacturing Extension Partnership - The Rise of Artificial Intelligence (AI) in U.S. Manufacturing https://www.nist.gov/mep/rise-artificial-intelligence-ai-us-manufacturing-text-only

arXiv - Identifying the Supply Chain of AI for Trustworthiness and Risk Management in Critical Applications https://arxiv.org/abs/2511.15763 National Institute of Standards and Technology (NIST) - Towards Resilient Manufacturing Ecosystems Through Artificial Intelligence: Symposium Report https://www.nist.gov/publications/towards-resilient-manufacturing-ecosystems-through-artificial-intelligence-symposium National Institute of Standards and Technology (NIST) - Artificial Intelligence Risk Management Framework 1.0 https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf

National Institute of Standards and Technology (NIST) Manufacturing Extension Partnership - Supply Chain Resources https://www.nist.gov/mep/supply-chain

World Economic Forum, in collaboration with Boston Consulting Group The Data-Driven Journey Towards Manufacturing Excellence https://www.weforum.org/publications/the-data-driven-journey-towards-manufacturing-excellence/

National Institute of Standards and Technology (NIST) - Cybersecurity Supply Chain Risk Management https://www.nist.gov/itl/executive-order-improving-nations-cybersecurity/cybersecurity-supply-chain-risk-management

World Economic Forum, in collaboration with Accenture - Shared Intelligence for Resilient Supply Systems https://www.weforum.org/publications/ shared-intelligence-for-resilient-supply-systems/

National Academies of Sciences, Engineering, and Medicine - Options for a National Plan for Smart Manufacturing https://www.nationalacademies.org/read/27260

McKinsey & Company - Capturing the True Value of Industry 4.0 https://www.mckinsey.com/capabilities/operations/our-insights/capturing-thetrue-value-of-industry-four-point-zero

Preprints.org - Barriers to AI Adoption in Supply Chain Management: Perspectives from Industry Leaders https://www.preprints.org/manuscript/202504.0581

Richard Beckhard and Reuben Harris - Organizational Transitions: Managing Complex Change, Addison-Wesley, 1977.

MHI and Deloitte - 2025 MHI Annual Industry Report: The Digital Supply Chain Ecosystem: Orchestrating End-to-End Solutions https://www.mhi.org/annual-industry-reports

MIT News - “MIT Professor David Simchi-Levi’s Risk Exposure Index (REI) used by the UN Office for Disaster Risk Reduction” https://news.mit.edu/2014/mit-professor-risk-exposure-index-used-un-disaster-risk-reduction

Business Wire - New MHI and Deloitte Report Focuses on Orchestrating Endto-End Digital Supply Chain Solutions https://www.businesswire.com/news/ home/20250319048739/en/New-MHI-and-Deloitte-Report-Focuses-on-Orchestrating-End-to-End-Digital-Supply-Chain-Solutions Al-Kindi Center for Research and Development - AI-Driven Inventory Optimization in Supply Chains: A Comprehensive Review on Reducing Stockouts and Mitigating Overstock Risks https://al-kindipublishers.org/index.php/jcsts/article/view/10163 Supply Chain Management Review - How AI Is Shifting Global Supply Chains from Reactive to Predictive https://www.scmr.com/article/how-ai-is-shifting-global-supply-chains-from-reactive-to-predictive/AI World Economic Forum - AI and Supply Chains https://www.weforum.org/stories/supply-chains-and-transportation/ai-supplychains/ National Science Foundation (NSF) - Research and Funding Resources https://www.nsf.gov/ MIT Center for Transportation & Logistics - Supply Chain Research Resources https://ctl.mit.edu/ Association for Supply Chain Management (ASCM) - Supply Chain Resources https://www.ascm.org/ Manufacturing USA - Manufacturing Innovation Resources https://www.manufacturingusa.com/

CNBC - “Consolidated food supply may be worsening cyclospora outbreaks, experts say” https://www.cnbc.com/2026/07/27/cyclospora-outbreak-food-industry-consolidation.html BBC News - “The cost of the Suez Canal blockage” https://www.bbc.com/news/business-56559073 U.S. Department of Energy, Office of Fossil Energy - “Products Made from Oil and Natural Gas” (infographic) https://www.energy.gov/sites/prod/files/2019/11/f68/Products%20Made%20 From%20Oil%20and%20Natural%20Gas%20Infographic.pdf CBS News / MoneyWatch, hosted on MSN - “Global oil prices hit $100 a barrel amid attacks in Red Sea” https://www.msn.com/en-us/money/economy/global-oil-prices-hit-100-a-barrelamid-attacks-in-red-sea/ar-AA28xncz https://www.cbsnews.com/news/global-oil-prices-red-sea-attacks-rates/ BBC News - “Why the Strait of Hormuz matters so much in the Iran war” https://www.bbc.com/news/articles/c78n6p09pzno Knowledge at Wharton - “The Auto Bailout 10 Years Later: Was It the Right Call?” https://knowledge.wharton.upenn.edu/podcast/knowledge-at-wharton-podcast/ auto-bailout-ten-years-later-right-call/

ABOUT:

Automation Alley is a nonprofit technology business association and Digital Transformation Insight Center focused on driving the growth and success of businesses in Michigan and beyond through innovation and automation. With a global outlook and a regional focus, we foster a vibrant community of manufacturing and technology innovators, entrepreneurs, and business leaders through opportunities for collaboration and learning. Our programs and services help businesses develop the skills and expertise needed to effectively jumpstart or accelerate digital transformation. By bringing together industry, academia, and government, we aim to create a dynamic ecosystem that drives innovation and growth across Michigan.

Automation Alley, our mission is to help businesses thrive in the rapidly changing digital economy. MISSION: At We equip them with the knowledge, insights, and tools to develop a software-first mindset that leverages the power of automation, AI, and other cognitive technologies. We believe that by working together, we can build a stronger, more innovative, and more competitive economy for the future.

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Wealth, prosperity and equality through technology.

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