Refactory

Refactory: How to Rebuild American Manufacturing by Refactoring ERP Legacy Code Using AI

In the digital age, the term “refactory” emerges as a powerful portmanteau, blending “refactor”—the software engineering practice of restructuring code to improve its internal structure without altering external behavior—with “factory,” the heart of manufacturing. This concept encapsulates a transformative approach: just as developers refactor tangled, outdated code to make it more efficient and maintainable, American manufacturers can “refactor” their legacy Enterprise Resource Planning (ERP) systems using artificial intelligence (AI). By doing so, they break down rigid, inefficient old manufacturing models, paving the way for agile, resilient, and innovative production ecosystems. This isn’t just a tech upgrade; it’s a blueprint for revitalizing U.S. manufacturing amid global challenges.

The State of American Manufacturing: A Legacy of Challenges

American manufacturing stands at a crossroads in 2025, grappling with a confluence of economic, technological, and geopolitical pressures. According to Deloitte’s 2025 Manufacturing Industry Outlook, manufacturers face higher costs, supply chain uncertainties, and a volatile business climate. The NIST highlights transformations driven by AI and automation, yet persistent issues like material shortages, shipping cost increases, and geopolitical tensions are expected to linger. Key challenges include:

• Supply Chain Disruptions: Ongoing global events and tariffs exacerbate vulnerabilities, with manufacturers prioritizing resiliency through reshoring and diversified sourcing.
• Labor Shortages and Workforce Development: Nearly 13 million manufacturing workers were employed in July 2025, but job losses and skills gaps persist, hindering growth.
• Technological Lag and Industry 4.0 Adoption: Many factories resist modernization, risking competitiveness, as noted in reports on cybersecurity, digital agility, and regulatory compliance.
• Economic Pressures: Inflation, higher input costs, and talent shortages could impede the sector’s projected growth, with some analyses forecasting job transitions amid reshoring efforts.

These issues mirror the brittleness of outdated systems, much like legacy code in software that accumulates “technical debt”—inefficiencies that compound over time.

Legacy ERP Systems: The Tangled Code of Manufacturing

ERP systems, designed to integrate core business processes like inventory, production, and supply chain management, have been the backbone of manufacturing for decades. However, many are now legacy relics, built on obsolete architectures that struggle with modern demands. Problems abound:

• Hidden Costs and Inefficiencies: Legacy ERPs lead to reduced efficiency, slow performance, and clunky workflows, frustrating users and inflating operational expenses.
• Scalability and Integration Issues: They fail to integrate with emerging technologies like IoT or cloud services, limiting adaptability in hybrid workflows.
• Maintenance Burdens: Outdated support and manual data entry decrease productivity, while rigidity hampers response to market changes.

In manufacturing, these systems perpetuate old models: siloed operations, reactive maintenance, and over-reliance on manual processes. Just as spaghetti code in software is hard to debug and scale, legacy ERPs create bottlenecks, delaying production and eroding competitiveness.

Refactoring Code: A Software Parallel to Manufacturing Overhaul

In software development, refactoring involves restructuring existing code to enhance readability, reduce complexity, and improve performance—without changing what the code does. Techniques include renaming variables for clarity, breaking down monolithic functions into modular ones, and eliminating redundancies. This process pays down technical debt, making systems easier to maintain and extend.

Apply this to manufacturing: Old models, akin to legacy code, are monolithic and inflexible—think rigid assembly lines optimized for mass production but ill-suited for customization or rapid pivots. “Refactoring” them means deconstructing these models into modular, data-driven processes. For instance, breaking down siloed departments into integrated workflows mirrors splitting a bloated codebase into microservices.

AI as the Refactoring Tool: Automating the Transformation

AI is the catalyst for this refactory revolution, particularly in refactoring ERP legacy code. Generative AI and machine learning can analyze vast codebases, identify patterns, and automate improvements, shaving 20-30% off refactoring time. Tools like GitHub Copilot and Amazon CodeWhisperer suggest structural enhancements while preserving functionality.

In ERP contexts:

• Code Analysis and Modernization: AI agents scan millions of lines of legacy code, detecting “code smells” (inefficiencies) and executing systematic refactoring.
• Migration to Modern Architectures: From monolithic ERPs to cloud-native systems, AI facilitates safe transitions, reducing risks like security vulnerabilities in outdated code.
• Enterprise Examples: Companies like JetRuby have used AI to slash manual effort by 70% in legacy code transformation, while McKinsey reports up to 45% savings in net new code writing.

Extending to manufacturing, AI-integrated ERPs enable predictive outcomes. For example, AI forecasts demand using historical data, optimizes inventory, and automates workflows—cutting downtime and boosting productivity. Siemens employs AI for real-time quality analysis, reducing defects by 50%, while Toyota uses it for supply chain tracing. Praxie’s AI-powered scheduling integrates with ERPs to prevent production halts, managing operations with minimal staff.

Benefits: Rebuilding a Competitive American Manufacturing Sector

By refactoring legacy ERPs with AI, manufacturers dismantle outdated models, fostering:

Aspect	Old Model (Legacy ERP)	Refactored Model (AI-Enabled)
Efficiency	Manual processes, delays	Automated workflows, real-time optimization
Scalability	Rigid, hard to adapt	Modular, cloud-integrated systems
Innovation	Siloed data, reactive maintenance	Predictive analytics, prescriptive actions
Cost Savings	High maintenance, hidden costs	20-50% reduction in downtime and effort
Competitiveness	Vulnerable to disruptions	Resilient supply chains, agile production

This shift addresses core challenges: 82% of manufacturers are increasing budgets for AI-ready ERPs in 2025, anchoring investments in seamless data flow. AI robotics predict faults, increasing productivity by 61% and driving $50B in savings by 2030. As one X post notes, AI and Industrial IoT enable prescriptive maintenance and edge computing, turning data into strategic assets.

The Path Forward: Embracing the Refactory Mindset

Refactory isn’t a one-time fix; it’s a continuous evolution. Manufacturers must invest in AI talent, secure data infrastructures, and partner with tools that embed intelligence into workflows. Enterprise leaders recognize this—many F500 companies are hiring AI heads but need guidance on differentiation beyond cost-cutting. The opportunity is immense: agentic AI systems that learn and adapt, as seen in platforms like ORO AI, could redefine operations.

In rebuilding American manufacturing, refactoring legacy ERP code with AI is the key to unlocking efficiency, innovation, and global leadership. It’s time to refactor the factory floor—before the code (and competition) leaves us behind.