In this exclusive op-ed for The Manufacturer, Bristlecone’s Jennifer Chew explores how artificial intelligence (AI) can help organizations reshore/nearshore their operations, bolstering their supply chains in the process.
As of October 2024, 62% of U.S. firms have embraced nearshoring, split-shoring, or reshoring, emphasizing domestic production to strengthen supply chain resilience. This marks a significant departure from the globalization wave that once defined business strategy.
Several factors are driving this shift, including geopolitical tensions, transportation bottlenecks, and environmental concerns. However, the transition is not just a matter of shifting operations—it requires a strategic approach that leverages AI and emerging technologies to optimize supply chain resiliency. Organizations seeking to future-proof their operations must integrate AI-enabled solutions to navigate this transformation effectively.
What’s driving the transition?
Over the past year, various economic, political, and environmental forces have pushed organizations to reconsider their reliance on international production. Geopolitical instability has been a persistent challenge, but recent years have seen an escalation in trade conflicts, sanctions, and policy shifts. With trade wars already here, companies are increasingly motivated to reduce their dependency on international suppliers. By nearshoring or reshoring, businesses can mitigate risks associated with fluctuating tariffs, import restrictions, and regulatory changes.
Supply chain disruptions due to transportation delays have also become a persistent challenge. The farther a company’s suppliers are, the more vulnerable it becomes to logistical breakdowns. A McKinsey study spanning a decade found that transportation disruptions cost organizations an average of 45% of their annual profits—an impact that intensifies with distance. Nearshoring offers a way to minimize these vulnerabilities by shortening supply chains and increasing agility.
At the same time, sustainability is becoming a critical priority for businesses. Carbon emissions from international shipping grew from 494 million metric tons in 2002 to a record high of 710 million in 2022. Companies seeking to reduce their carbon footprint are turning to nearshoring as a strategy to lower emissions and align with corporate sustainability goals. Organizations can significantly decrease their environmental impact by localizing production while improving supply chain resilience.
Given these pressing concerns, nearshoring presents itself as a more reliable, sustainable, and cost-effective solution for businesses navigating an unpredictable global landscape.
Leveraging AI for a successful transition
While the benefits of nearshoring are evident, making the shift is far from straightforward. To ensure a seamless transition, organizations must adopt localization strategies and harness the power of AI and predictive analytics. AI excels at synthesizing vast amounts of data, making it a critical tool in evaluating nearshoring strategies. By analyzing historical supply chain data alongside real-time market conditions, AI can assess the cost-effectiveness and sustainability benefits of moving operations closer to home. This data-driven approach enables companies to make informed decisions about supplier selection, inventory management, and logistics optimization.
Beyond data analysis, AI plays a crucial role in risk management. AI-powered systems can function as early warning mechanisms, identifying potential disruptions before they occur. By assessing risks such as weather events, geopolitical instability, or tariff changes, AI helps businesses proactively adjust their strategies to avoid costly setbacks. For example, AI-driven predictive models can estimate shipment value-at-risk, allowing companies to anticipate potential losses and make contingency plans. AI-powered scenario analysis tools can also simulate thousands of possible disruptions, equipping businesses with actionable insights to enhance supply chain resilience.
AI-driven automation also enhances efficiency across localized supply networks. From demand forecasting to warehouse management, AI optimizes every stage of the supply chain. Machine learning algorithms can analyze purchasing patterns to anticipate demand fluctuations, reduce waste, and ensure that just-in-time delivery models operate smoothly. Additionally, AI-powered robotics and automation in manufacturing facilities can streamline production processes, making reshoring or nearshoring more cost-effective. These advancements help businesses maintain competitiveness while reducing reliance on low-cost offshore labor.
AI as a catalyst for localization
Technology, particularly AI, was once the driving force behind rapid globalization. Today, it is equally pivotal in enabling businesses to pivot toward localization. By harnessing AI’s capabilities in data analysis, risk assessment, and operational optimization, companies can confidently navigate the complexities of nearshoring.
To fully leverage AI’s potential, companies must prioritize education and training for their teams. Understanding AI’s capabilities and limitations will be essential in making informed decisions, improving operational efficiency, and fostering innovation. Organizations should encourage continuous learning and provide resources to help employees adapt to AI-driven workflows. Additionally, recognizing that AI implementation can be complex, businesses should not hesitate to seek external expertise when needed. Whether through partnerships, consultants, or specialized AI firms, asking for help ensures a smoother transition and maximizes the benefits of AI in supply chain optimization.
As organizations must continue to adapt to a rapidly changing global landscape, those that effectively integrate AI into their supply chain strategies will be best positioned to thrive. The future of supply chain resilience lies not in distant production hubs but in smarter, AI-driven localization strategies that balance efficiency, sustainability, and risk mitigation.
About the author
Jennifer Chew is VP of Solutions and Consulting at Bristlecone.
As a seasoned leader and strategist, Jennifer brings extensive experience in advising global multinationals and fast-growing start-ups, specializing in supply chain, manufacturing finance, marketing and branding, digital/enterprise technology, talent, customer experience, and employee engagement. In her current role as Vice President of Solutions and Consulting at Bristlecone, a Mahindra Group Company, Jennifer is driving the company’s shift to become a consulting-led organization. Drawing from her diverse background, insights from discrete manufacturing, and experience in growing a consulting practice within an India-based organization, Jennifer leads in a transformative way at Bristlecone.
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