Bringing a new product to life is rarely a straight line. It takes more than a clever prototype or a promising idea to make something durable, scalable, and commercially successful. Few people know this better than Nareen Karnati, a multidisciplinary engineering leader whose career has stretched from factory floors to robotics labs to global SAP rollouts. Over the past decade, he’s helped companies streamline supply chains, reduce costs without cutting quality, and bring structure to chaotic product launches. In this interview, Nareen pulls back the curtain on the realities of scaling innovation, sharing lessons from manufacturing, enterprise technology, and even bioinspired robotics on what it really takes to get from concept to market-ready.
You’ve worked across prototype design, manufacturing, and enterprise systems. In your experience, what are the most common gaps companies face when moving from a working prototype to scalable production, and how can they be closed early?
In my experience, the most common gaps companies face when moving from a working prototype to scalable production include insufficient planning for mass production, human or machine errors during manufacturing, high machine maintenance and labor costs, and shipping and handling challenges.
Prototypes are typically developed in a cost-effective manner using tools like 3D CAD modeling programs, such as SolidWorks, and rapid prototyping methods, like 3D printing. These early-stage versions often rely on inexpensive materials such as ABS plastic or aluminum before transitioning to production-grade materials. Closing these gaps early requires involving experienced engineers from the beginning, reviewing previous research and industry benchmarks, and allocating more time to design validation and manufacturability studies. Taking this structured approach not only reduces errors and costs but also ensures a smoother transition to scalable and reliable production.
Cutting costs often seems at odds with maintaining quality. What strategies have you found most effective for ensuring manufacturing excellence while still achieving measurable cost reductions?
Cutting costs in manufacturing does not have to mean sacrificing quality. The most effective strategies I have found focus on creating efficiencies, strengthening internal capabilities, and optimizing labor management, all while keeping quality at the forefront.
- Improve Operational Efficiency: Identify inefficiencies and reduce waste by introducing new machinery, automation, or advanced technologies. Streamlined operations lower costs without compromising quality.
- Develop In-House Capabilities: Reduce dependency on external suppliers by building in-house processes for critical products. This not only cuts costs but also improves control, flexibility, and planning.
- Address Labor Inefficiencies: Since labor is a major expense in manufacturing, minimizing inefficiencies is crucial. Enhancing training, improving workflows, and boosting productivity help reduce costs while maintaining quality standards.
You’ve led large-scale SAP implementations and data migrations. How does integrating ERP systems into product development cycles speed up time-to-market compared to running them separately?
Advanced ERP systems integrate key modules such as purchasing, inventory, and sales into a single platform. By consolidating data into one source of truth, they eliminate manual handoffs, reduce duplication, and enable real-time visibility across the product development life cycle. This allows teams to move seamlessly from procurement to production to sales, while also streamlining financial reconciliation of profit and loss. As a result, organizations not only accelerate time-to-market but also enhance accuracy, efficiency, and decision-making within a single unified system.
Your background spans robotics research, medical device innovation, and enterprise technology. How has working across such diverse fields shaped your approach to designing and launching products?
Although my work spans robotics, medical devices, and enterprise systems, the foundation of my approach comes from core principles in mechanical engineering. This background allows me to apply the same systematic thinking, focusing on design efficiency, reliability, and scalability while adapting methods to suit each field. By combining these fundamentals with domain-specific knowledge, I’m able to develop solutions that are both technically sound and practically viable across different industries.
You’ve driven process standardization initiatives on a global scale. What lessons from harmonizing procurement and supply chain operations can be applied to fast-moving product launch environments?
Harmonizing procurement and supply chain operations taught me the value of standardized frameworks that still allow for local flexibility. Standardization reduces complexity, improves supplier collaboration, and speeds up decision-making by ensuring processes are consistent across regions. In fast-moving product launch environments, these same principles help teams avoid bottlenecks, align resources quickly, and scale launches more efficiently without sacrificing quality or compliance.
When launching a new product, what role should data, both from ERP systems and from quality control feedback loops, play in guiding decisions from day one?
From day one, ERP data provides clear visibility into supply chain readiness, costs, and resource availability. At the same time, quality control feedback loops validate design choices in real-world conditions. When these two data streams are combined, companies can detect risks earlier, adjust production schedules, and strengthen product reliability. This evidence-based approach minimizes guesswork and helps teams move faster and with greater confidence.
Your work includes bioinspired robotics. Are there insights from robotics or biomimicry that can be applied to streamlining manufacturing systems or improving product reliability?
Robotics brings precision and repeatability, both of which are essential for scaling consistent manufacturing. Biomimicry builds on this by taking cues from nature’s adaptability and self-correction. For instance, bioinspired systems can be designed to reconfigure production lines based on demand shifts or to detect and correct anomalies in real time. When combined, these approaches enable manufacturing systems that are not only efficient but also resilient and sustainable at scale.
With technology cycles compressing and customer expectations rising, what do you see as the next big shift in how engineering leaders must think about launching products, especially in aligning development, quality, and enterprise systems?
Integrating product development life cycle & enterprise systems with AI is the next big shift for engineering leaders. It doesn’t take away the limelight from humans. Rather, it works as creative technology, allowing teams to focus on better outcomes; it isn’t here to replace human expertise, but to augment it. In the context of PDLC and enterprise systems, AI can handle repetitive tasks such as predictive scheduling, demand forecasting, or anomaly detection in high-quality data. This allows engineers and leaders to focus on higher-value activities like innovation, customer alignment, and strategic problem-solving. By shifting routine workload to AI, teams can achieve faster cycles and more creative, impactful outcomes.
