The US accounts for about 30% of the world’s consumption, roughly equal to the combined consumption of China and the EU. The country’s consumer spending surged to $16.7 trillion in 2025, over twice China’s $7.8 trillion that year. It also remains one of the highest margin markets in the world, with certain sectors giving nearly 2X the margins seen in Europe. Historically, these superior margins were achieved through a simple formula: manufacture in low-cost regions and sell into the high-demand U.S. market.
This model is now being challenged by a reshuffling of trade patterns, set in motion by the current US administration’s push to revive domestic manufacturing. Several global conglomerates such as Apple, Micron, and GE have doubled down their domestic investments in the last year. New investments run into trillions across verticals spanning EV, semiconductors, and clean energy.
However, many manufacturers remain skeptical, concerned that their high margins would erode with reshoring. They are forced to rethink where their margins truly come from. Instead of relying solely on geographic cost advantages, many are turning inward, optimizing the fundamental drivers of manufacturing efficiency: machines, manpower, materials, methods, and measurement, commonly known as the 5Ms of manufacturing. This shift is increasingly being driven by AI in manufacturing, enabling smarter and more adaptive operations.
| As tariffs rise and reshoring debates intensify, manufacturers can no longer rely on geography for margins. The real opportunity lies within—unlocking productivity by reimagining the 5Ms (man, machines, materials, methods, and measurement) with intelligence, automation, and AI. |
Is Reshoring Really Happening?
Reshoring is gaining momentum, but the shift is far from uniform across the manufacturing landscape. The last decade has seen a steady flow of large manufacturers bringing parts of their production back to the US. But that cannot be said of mid-market and boutique players for whom the calculus is far more complex. Their boardroom conversations currently do not hint at immediate reshoring, though their offshore bases are exposed to higher tariffs that discourage imports into the country.
Weakening global demand adds another layer of uncertainty. Recent Purchase Producer Index Data (PPI) trends show declining price momentum across most regions, with the exception of North America: a strong indicator that many markets are unwilling to absorb higher prices.
As a result, most mid-market enterprises find themselves in a strategic bind. While they are reluctant to reshore immediately, they still want to tap into the high-demand US market at the lowest possible cost.
Many are therefore focused on extracting greater productivity and efficiency from their existing assets—machines, workforce, sourcing strategy, and workflows.
Rethinking 5Ms: Challenges & Opportunities Ahead
Man: Labor remains one of the most pressing constraints for U.S. manufacturers. While there has been a regulatory push to ramp up domestic production, many existing plants run shorthanded constantly. Even for large companies, skilled roles such as machinists, welders, and technicians don’t come at a competitive price.
Recent estimates suggest that roughly half a million manufacturing jobs remain unfilled across the country.. If current trends continue, that gap could widen dramatically by 2030. The labor crisis is real and is compounded by an aging demographic —45% of the workforce is already over the age of fifty, signaling an approaching wave of retirements that could further strain talent pipelines.
Given this situation, how can manufacturers attract and sustain skilled labor? Part of the solution lies in automation that could help eliminate unnecessary labor. A shift towards robotics and AI could rebrand them as high-tech industries attracting the GenZ talent. The real answer however lies not just in reducing the dependence on labor, but in fundamentally redefining the nature of work on the shop floor. Automation and AI can take over repetitive, low-value tasks, allowing human workers to focus on higher skill, decision-oriented roles. For example, prebuilt modular solutions for order management, inventory tracking, and fulfillment can automate routine workflows that traditionally require significant manual effort. These lightweight, plug-and-play modules reduce dependency on spreadsheets and fragmented systems while improving accuracy and speed. As a result, fewer people are needed for operational coordination, and existing teams can focus on higher-value activities—easing labor pressure while making roles more efficient and engaging.
Machines
If labor shortages are forcing manufacturers to rethink the human side of production, the next frontier lies in the machines themselves. Across many mid-market factories, machines installed decades ago continue to be mechanically solid, but they are digitally blind, largely disconnected from modern data systems.
Manufacturers are now exploring ways to modernize these equipment without rip and replace. Their key question being how to bring intelligence to these brownfield investments without triggering heavy CAPEX commitments. Fortunately, they have three pragmatic options in front of them: Retrofit, Bolt-on, and Phased Modernization.
- While retrofitting machines may cost only 5-10% of a full replacement, it still involves deep engineering. For example, replacing a CNC machine’s control systems with a modern PLC requires a complete reconfiguration and integration with existing workflows.
- In contrast, bolt-ons are lightweight modular enhancements that can be added or removed with ease. A common example is automating product quality checks using computer vision, implemented in as little as 15 days.
- Phased modernization sits at the other end of the spectrum from big bang transformation. Instead of replacing entire systems at once, it involves gradual transitioning from monolithic setups to prebuilt modular and composable architectures, minimizing disruption to everyday operations.
Perhaps the most transformative benefit of modernization is visibility. The moment sensors and energy meters are layered onto machines, they begin generating clean, unbiased data on the machine health. The reactive, calendar-based rework is essentially replaced by a proactive approach that helps anticipate and fix defects before they occur. When implemented effectively, it gives real-time visibility across production flows, operational efficiency, and supply chains, which is often the need for many mid-market manufacturers.
Materials
Material sourcing has emerged as another pressure point for manufacturers navigating an uncertain trade and demand environment. Over the past two years, there have been significant factory closures on account of sluggish demand. In some cases, tariff increases have directly triggered operational decisions. For example, the 25% duty on upholstered furniture instigated the sudden closure of Ashley homestore’s Texas facility, resulting in the loss of 250 jobs.
Research shows that companies have been scrambling to absorb as much as 30% of the costs, while passing the remaining to the consumers. This dynamic unfolds alongside a period of extended inflation, which further escalates the final product prices and dampens demand.
At the same time, the industrial policy incentives aimed at strengthening domestic manufacturing have triggered strong investment in new factories. Construction spending on factories have remained relatively high. The big OEMs in sectors such as batteries, chips, EV systems, electrification infrastructure and advanced materials are steadily reshoring production.
But the fundamental question is: who will supply these expanding manufacturing ecosystems? There has been an aggressive push to locate Tier 2, Tier 3 and Tier 4 suppliers in each of these verticals.
A manufacturer of industrial equipment imports its 75-horsepower diesel engines from Germany, as building them domestically might require nearly $25 million investment. Given the state of tariffs, its reversals and rollbacks, would Tier 2 suppliers take the risk to set up such a plant? There is already a loud call to relax tariffs for raw materials, parts, and components that cannot yet be produced domestically at scale.
But some businesses are benefiting from the evolving landscape. Contract manufacturers, for instance, are seeing significant interest from companies seeking alternatives to existing offshore production hubs. American and European companies with offshore units in China and Mexico are signing long-term contracts to bypass tariffs.
Domestic suppliers are another clear beneficiary. As long as their supply chains are within the US, they are well poised to serve the big OEMs reshoring in the country.
For the consumer-facing small and medium manufacturers (SMMs), however, the situation is particularly complex. Tariff volatility leaves them clueless with regard to sourcing decisions. Even if their supply chain networks operate within the country, upstream suppliers may still depend on imported components. For the SMMs, every option seems like a no-go. Building factories is off the cards, while raising prices risks losing customers to bigger brands. Pausing production might as well lead them to shut down factories.
The only plausible way is to exercise maximum control in sourcing. Some manufacturers absorb part of the additional costs, while carefully passing the remainder to customers. Others are diversifying their supplier networks in low-tariff countries to reduce exposure to trade fluctuations. In certain cases, companies rewrite the origins (origin engineering) and reclassify their raw materials to navigate tariff classifications more effectively.
But the most effective hedge against tariff is to prime the sourcing with intelligence. Supply chain AI assistants give manufacturers full visibility into everything from sourcing and inventory to fulfillment and recall. These AI assistants scan the external environment, track tax and tariff fluctuations, run multiple what-if scenarios and model potential outcomes, before recommending the most optimal course of action.
For manufacturers seeking stability in volatile trade conditions, material intelligence is quickly becoming a critical capability.
Method and Measurement
If labor, machines, and materials shape the physical side of manufacturing, method and measurement determine how effectively those resources are used. In today’s environment, rising material costs, longer lead times, labor shortages, expensive equipment upgrades are the primary external factors amplifying the cost of goods. While manufacturers have limited control over these external variables, they look to control the internal cost amplification factors: machine breakdowns, stockouts, overstocking, production defects, and manual error-prone workflows.
By keeping in check the internal, often hidden drivers of operational cost, they can still profitably serve one of the world’s largest consumer markets. The most effective way out is to extract greater productivity and efficiency from existing assets. Rapid technology advances spurred by AI and Agentic AI offer a path to drive productivity and efficiency at scale. However, AI adoption is often held back by traditional methods, outdated metrics, and fragmented systems – barriers that prevent them from becoming truly data-driven or intelligent. This makes it equally critical to rethink measurement—moving beyond static, lagging KPIs to real-time, contextual metrics that continuously track efficiency, quality, and throughput across operations.
Meanwhile, the large OEMs are steadily building a responsive, integrated, and intelligent ecosystem that seamlessly connects the suppliers, shopfloors, warehouses, and end customers. For small and medium manufacturers, the key question is how to seamlessly make the intelligent shift?
The answer lies in priming the 5Ms with intelligence. Developing predictive and agentic applications that don’t just stay isolated, but easily scale across the enterprise. In the last year alone, around 85% of pilots have failed. While this statistic cuts across sectors, the underlying issue remains the same: AI initiatives collapse when they are not validated against real business workflows, data realities, and operational constraints.
This is where platforms such as Trigent ArkOS play a critical role. ArkOS is an operator-grade AI execution engine that helps enterprises validate, deploy, and scale AI solutions with measurable proof before making irreversible investments. It transforms fragmented pilots into production-ready systems by grounding AI in real workflows, ensuring explainability, performance visibility, and full control. With ArkOS, manufacturers can move from experimentation to execution confidently, securely, and at scale.
Ultimately, when methods and measurement become intelligent and data-driven, the other 3Ms – man, machines, and materials – begin to perform at their full potential.
Tough times, Bold Measures
In all likelihood, 2026 may not be rosy for manufacturers navigating tariff pressures and supply chain shifts. Yet, it offers immense opportunity to unlock productivity and efficiency at scale, and rethink the economics of production itself.
Yes, the year may test the fundamentals of our business models and challenge the existing operational assumptions. But, it also provides an opportunity to nurture resilience from within by rethinking the 5Ms and making bold moves that harness the transformative potential of automation and AI.
If reshoring is to succeed at scale, it will depend not merely on shifting geography, but on building smarter, data-driven manufacturing systems that can deliver productivity, agility, and margin in equal measure.
In Technology we Trust!
