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Malaysian Reserve
4 days ago
- Business
- Malaysian Reserve
For a Fourth Consecutive Year, LeanDNA Earns Place on the Inc. 5000 List
This recognition follows previous acknowledgements from Inc., including being named an Inc. Best Workplace and an Inc. Regional honoree. NEW YORK and AUSTIN, Texas, Aug. 12, 2025 /PRNewswire/ — Inc. today announced that LeanDNA, the leading supply planning and inventory optimization platform, is on the Inc. 5000 list, the most prestigious ranking of the fastest-growing private companies in America for the fourth year in a row. The list is a data-driven look at the most successful companies within the economy's most dynamic segment—its independent, entrepreneurial businesses. 'LeanDNA offers a powerful solution for the supply side of the shortage-excess inventory paradox plaguing discrete manufacturers, which is an often-underserved area for the supply chain,' said Andy Ellenthal, CEO of LeanDNA. 'Our platform helps teams unlock the full potential of their operations by optimizing inventory, reducing waste, and uncovering growth opportunities.' LeanDNA improves on-time delivery and working capital by providing a single source of truth for inventory optimization and production readiness. Over the past twelve months, the company has continued to achieve significant milestones, including: Hosting its first Manufacturing Excellence Summit. Being named a 'Leader' in the Enterprise Grid® Report for Inventory Control and a 'Momentum Leader' for Inventory Control and Predictive Analytics in the Spring G2 reports. Earning a Top Supply Chain Project award and a Partner in Collaboration Manufacturing Leadership Council Award. 'Making the Inc. 5000 is always a remarkable achievement, but earning a spot this year speaks volumes about a company's tenacity and clarity of vision,' says Mike Hofman, editor-in-chief of Inc. 'These businesses have thrived amid rising costs, shifting global dynamics, and constant change. They didn't just weather the storm—they grew through it, and their stories are a powerful reminder that the entrepreneurial spirit is the engine of the U.S. economy.' For the full list, company profiles, and a searchable database by industry and location, visit: Methodology Companies on the 2025 Inc. 5000 are ranked according to percentage revenue growth from 2021 to 2024. To qualify, companies must have been founded and generating revenue by March 31, 2021. They must be U.S.-based, privately held, for-profit, and independent—not subsidiaries or divisions of other companies—as of December 31, 2024. (Since then, some on the list may have gone public or been acquired.) The minimum revenue required for 2021 is $100,000; the minimum for 2024 is $2 million. As always, Inc. reserves the right to decline applicants for subjective reasons. Related: LeanDNA Inc. Company Profile LeanDNA Makes the Inc. 5000 Fastest Growing Companies List for the Third Consecutive Year LeanDNA Earns Spot on Inc. 5000 2023 List Two Years in a Row LeanDNA Earns Spot on 2022 Inc. 5000 Annual List About Inc. Inc. is the leading media brand and playbook for the entrepreneurs and business leaders shaping our future. Through its journalism, Inc. aims to inform, educate, and elevate the profile of its community: the risk-takers, the innovators, and the ultra-driven go-getters who are creating the future of business. Inc. is published by Mansueto Ventures LLC, along with fellow leading business publication Fast Company. For more information, visit About LeanDNALeanDNA is a supply planning and inventory optimization platform that enables supply chain teams with a single source of truth for inventory management and production readiness. This cloud-based platform synchronizes execution across the supply chain, empowering manufacturers to prioritize and collaborate to resolve critical material shortages and excesses. With LeanDNA, manufacturers improve on-time delivery and working capital levels by gaining visibility into current and incoming materials, actions based on inventory criticality, real-time collaboration with suppliers, and the ability to track progress toward inventory optimization goals. Learn more at
Forbes
25-06-2025
- Business
- Forbes
AI And Digital Twins Are Transforming Supply Chain Execution
Richard Lebovitz is the Founder of LeanDNA, a technology company focused on using AI to optimize manufacturing and supply chain performance. It's 3 a.m. in a manufacturing plant. A critical component shortage threatens to halt production of 2,000 units. The procurement team won't arrive for hours. In yesterday's world, this meant costly downtime and scrambled recovery efforts. In today's AI-powered supply chain, the digital twin has already rerouted inventory from another facility, notified suppliers of the urgency and adjusted production schedules to minimize impact, all while the team slept. This isn't science fiction. It's the new reality of supply chain execution, and it's transforming how leading manufacturers operate. For years, the idea of a "digital twin" has captured the imagination of manufacturing and supply chain leaders—a virtual replica of a complex physical system that can simulate, predict and optimize outcomes. It's an elegant concept: Plug your supply chain into a digital mirror and unlock unprecedented agility. But for most organizations, that mirror has remained foggy. Despite growing investment in dashboards, data lakes and simulation tools, the digital twin has struggled to escape the realm of buzzwords. That's about to change. Thanks to rapid advances in artificial intelligence, particularly in machine learning, simulation modeling and large language models, we're entering a new phase. A digital twin is no longer just a reflective model. It's becoming an intelligent, operational assistant. And it's this shift that is ushering in a new supply chain era that I call "optimized execution." From Mirror To Machine: The Limitations Of Yesterday's Digital Twins The traditional digital twin vision focused on building a mirror image of a physical system—a manufacturing line, a warehouse or a global supply network. These representations were useful for visibility and planning but lacked one critical function: execution. They could diagnose but not act. Simulate, but not respond. Even the most advanced models remained siloed from the daily grind of supply chain operations. They didn't factor in live execution data like late supplier commits, real-time part shortages or shifting production constraints. They didn't help procurement teams prioritize their workload or support planners in navigating "clear-to-build" challenges, the critical process of ensuring all components are available before production begins. What was missing? A living system that optimizes, executes and learns every single day. Optimized Execution The digital twin of the future is a decision making engine that connects optimization with real-world execution through three interconnected phases. First, it optimizes strategically by establishing a plan-for-every-part, determining optimal ordering and inventory policies for every component based on demand variability, lead time and risk. It sets dynamic inventory targets that balance service goals with working capital constraints at the individual part level, where real decisions happen. Second, it executes intelligently. The system provides daily, prioritized inventory actions, highlighting what needs expediting, delaying or canceling. It delivers clear-to-build analytics so planners understand exactly which components block production and why. It enables direct supplier collaboration within the system while maintaining bidirectional synchronization with ERP platforms. Third, it learns continuously. The system captures execution outcomes, including actual shortages, late commits and missed opportunities. These feed into AI models using reinforcement learning, where the system improves by learning from its own recommendations. The Architecture Of A Thinking Supply Chain Building this requires four architectural layers. The foundation is unified data synchronization, integrating real-time information from fragmented systems into a normalized model. Digital modeling and simulation capabilities build virtual representations, including part hierarchies, lead times and supplier networks. This enables real-time scenario simulation using Monte Carlo techniques to model uncertainty. Prescriptive analytics and execution tools use AI to recommend specific actions like adjusting reorder points or reallocating materials. They provide role-specific workbenches for buyers, planners and suppliers. The closed-loop learning engine measures execution effectiveness and continuously refines optimization logic through machine learning, progressively closing the gap between model and reality. When these layers work together, you have a supply chain that adapts. Transforming Daily Operations The shift from manual triage to intelligent prioritization is transformative. In typical factories today, planners and buyers spend hours buried in Excel files, manually identifying shortages and updating spreadsheets. Even basic tasks, like understanding which orders are late due to missing parts, are painfully inefficient. With optimized execution, planners log in to see prioritized lists of parts blocking the build. Buyers receive data-driven recommendations on which orders to expedite based on inventory targets and downstream demand. Supplier collaboration becomes structured and real-time, replacing endless email chains. According to our data, early adopters in automotive, electronics and pharmaceuticals report 20% to 30% reductions in excess inventory, 40% improvements in on-time delivery and hours saved daily on routine tasks. Overcoming Implementation Challenges Implementation isn't without challenges. Data quality and integration complexity remain hurdles as organizations struggle with inconsistent data across legacy systems. Change management is critical; success requires new technology and new ways of working. Initial investments can be substantial, though ROI typically justifies expense within 12 to 18 months. Start with focused use cases where impact is measurable. Begin with high-value product lines where improved execution drives immediate results. Build proof points, then expand systematically. A Strategic Imperative In a world defined by disruption—geopolitical risk, supplier shortages, labor constraints—supply chains must be intelligently proactive. Organizations embracing this shift will move faster and operate leaner because their supply chains won't just reflect reality; they'll shape it. Competitive advantages compound over time. As AI systems learn from more execution cycles, recommendations become increasingly sophisticated. Early adopters can capture disproportionate value, while laggards risk permanent disadvantages as the gap between AI-powered and traditional supply chains widens. Looking forward, emerging capabilities promise greater transformation. IoT integration will provide real-time visibility. Predictive maintenance will prevent disruptions before they occur. Natural language interfaces will make systems more accessible. For supply chain leaders, the question isn't whether to implement an AI-powered digital twin; it's how quickly to begin. Start by auditing current systems, identifying high-impact use cases and partnering with providers who understand both technology and operational realities. The future isn't just visibility. It's intelligence. And that future is already here for those ready to embrace it. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
23-05-2025
- Business
- Forbes
AI's Manufacturing Renaissance: Why The Future Of Value Creation Will Return To Making Things
Richard Lebovitz is the Founder of LeanDNA, a technology company focused on using AI to optimize manufacturing and supply chain performance. getty In the rapidly evolving landscape of artificial intelligence, most conversations center on how AI will transform knowledge work. As someone who has spent over 30 years bringing advanced technology to manufacturing and supply chain operations, I see a different revolution on the horizon: AI won't just transform knowledge work—it will fundamentally shift our economic focus back to manufacturing. For decades, advanced economies have shifted away from manufacturing things toward providing services. The United States, once the manufacturing powerhouse of the world, now sees nearly 80% of its GDP derived from services. This shift created tremendous economic value and millions of jobs. But we now face a dramatic reversal. As a technology leader, I've witnessed AI's transformative power firsthand. We are experiencing dramatic efficiency gains in knowledge work—and this is just the beginning. A recent World Economic Forum report anticipates significant productivity enhancements from generative AI, with gains potentially compounding to transformative levels by 2030. While hard to imagine, experts predict up to 30% of U.S. work hours could be automated by 2030—primarily in knowledge-based roles—due to advances in generative AI. The shift is revolutionary, yet most individuals don't see it coming. AI systems increasingly streamline knowledge work. In manufacturing, companies like BMW have adopted AI-driven quality control systems. For instance, implementing AI-powered cameras at one of BMW's European plants cut defects by 30% in a year by spotting recurring issues and guiding engineers to address root causes. While these services won't disappear, they will require significantly fewer workers. According to one report, 69 million new jobs are projected to be created, particularly in areas like AI and machine learning, while 83 million positions may be eliminated, indicating a major transformation in the global labor market. At the same time, 42% of physical and manual tasks are expected to be automated by 2027, reshaping the demand for human labor across industries. This raises the question: Where will economic value shift? My answer: back to manufacturing. At its core, it's a return to fundamental value creation. People need manufactured goods to live—food, shelter, transportation, medical devices, energy systems. While we use lawyers, accountants and IT support out of necessity, we don't inherently desire these services. They're means to ends. In contrast, people genuinely want and need the tangible products that manufacturing delivers. These products make life possible and enjoyable. As AI reduces the human capital required for knowledge work, we have an unprecedented opportunity to reallocate talent toward making better things. The great economic transformations that built modern prosperity weren't driven by services but by industrial revolutions. Britain's rise to global power came through manufacturing innovations. America's economic dominance in the 20th century was built on its manufacturing might. China's economic rise has been powered primarily by manufacturing, lifting hundreds of millions from poverty. The lesson is clear: Making things—real, tangible products—has been the most reliable path to broad-based economic growth throughout modern history. This isn't a call to return to yesterday's manufacturing. AI-enhanced manufacturing will unleash a revolution where human ingenuity, amplified by machine intelligence, will transform how we create everything from microchips to automobiles, merging the cognitive power of knowledge work with the tangible impact of physical production. Modern manufacturing already relies on robotics, computer vision, predictive maintenance and sophisticated supply chain optimization. The next generation will integrate these technologies with generative AI to create intelligent production systems capable of unmatched customization, quality and efficiency. This evolution will require diverse talent—data scientists, engineers, designers and creative problem-solvers working in unprecedented collaboration. These will be among the most valuable roles of the decade. For those of us in manufacturing and supply chain operations, the need for these advanced skills is already apparent. Yet top talent has been consistently lured away by the allure and higher salaries of "sexy" tech jobs in social media and consumer apps, where pay often exceeded manufacturing by 50% to 100%. This perception gap represents a massive opportunity. As AI commoditizes many aspects of traditional knowledge work, manufacturing roles will increase in relative value and status. The renaissance in manufacturing will create a new generation of fulfilling careers that combine technological sophistication with tangible impact on the products that shape our daily lives. Nations that recognize this shift early will gain tremendous advantages. Rebuilding manufacturing will require smart policy and investment. For businesses heavily invested in knowledge services, the imperative is clear: understand how AI will transform your operations and consider how your resources might be redirected toward tangible product innovation. The AI revolution will transform all aspects of work, but its most profound impact may be this great reallocation of human talent—away from information manipulation and back toward tangible value creation through advanced manufacturing. For those who've spent careers in manufacturing, as I have, this shift represents a long-awaited validation. We've always understood the fundamental importance of making things that improve human lives. Now, as AI reshapes knowledge work, that understanding will spread. Forward-thinking business leaders must recognize this inflection point and act decisively. Companies investing primarily in traditional knowledge work infrastructure will increasingly compete directly with AI systems—a losing proposition. The true advantage will come from blending AI with reimagined manufacturing processes that create novel physical products addressing humanity's greatest challenges. For investors, the implications are equally significant. The coming decade will likely see companies focused on reimagining manufacturing and supply chain operations outperform pure software and services plays. Those who recognize this pattern shift early will position themselves at the forefront of the next great wave of value creation. The next generation's most exciting opportunities won't be found in shuffling digital information but in reimagining the physical world. Those who recognize this shift early will help shape a future where technology enhances our capacity to create tangible value—making things that genuinely improve lives while creating meaningful work for millions. The AI revolution isn't just about smarter software—it's about redirecting our collective intelligence toward making a better world, quite literally. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
