At the intersection of technology and manufacturing, innovation often comes from those who dare to challenge long-accepted inefficiencies. Yuvraj Shidhaye, Founder and Director of TreadBinary, is one such disruptor. With a deep understanding of industrial pain points and a passion for tech-driven transformation, Shidhaye has been instrumental in shaping smarter, leaner, and more adaptive manufacturing systems. One of TreadBinary’s standout innovations, the patented Beaverz-hac, is redefining material utilization by slashing scrap rates that have plagued manufacturers for decades. In this exclusive interview, Shidhaye shares insights on how technologies like AI, ERP, and IoT are reshaping the factory floor, why upskilling is as critical as automation, and how Indian manufacturers are uniquely positioned to lead the smart manufacturing revolution.
What inspired the development of Beaverz-hac? What gap in the manufacturing industry does it solve?
Beaverz-hac is one of our patented products, developed to address a long-standing inefficiency in manufacturing: excessive scrap during material cutting. During an aerospace implementation project focused on optimising production and supply chain processes, we noticed that manufacturers routinely factor 10% scrap into their cost estimates for materials like aluminium, copper, or titanium. This level of waste often exceeded the typical profit margin of 2 to 3%, yet it was accepted without question. We built Beaverz-hac to change this mindset. Through intelligent Material Requirements Planning (MRP) and layout optimisation, the solution has reduced scrap to as low as 0.85-1% in some trials, with average reductions exceeding 60%. This improves the material usage efficiency of our clients, resulting in significant cost savings.
In your view, what are the biggest manufacturing pain points today, and how can technology help resolve them?
Manufacturing today faces three core challenges, each influencing the other and compounding overall complexity. The first is demand variability—frequent design changes and shifting customer preferences have made forecasting unreliable, often resulting in overproduction or stockouts. This unpredictability puts added pressure on operations, the second major challenge. High labour costs, attrition, and long training cycles disrupt continuity, while traditional automation remains rigid and capital-intensive. These internal hurdles are further complicated by external forces—the third challenge, such as evolving trade policies, tariffs, and import restrictions that reshape sourcing and production strategies.
To address these interconnected issues, manufacturers are turning to technology. AI and machine learning improve demand planning accuracy, while ERP-integrated tools streamline operations and reduce manual dependence. Modular automation offers flexibility with lower capital risk. Meanwhile, digital systems support compliance and enable material substitution, enhancing adaptability in a volatile environment.
How do you view the balance between automation and workforce upskilling in the modern factory?
Balancing automation and workforce upskilling in modern factories depends largely on product complexity. Fully automated plants work well for standardised, unchanging products. However, for factories producing varied models, such as EV batteries with different sizes and specifications, automation must be flexible. This requires upgrading machines and integrating smart conveyors to manage quality control and sequence changes autonomously.
Such adaptable systems demand a skilled workforce to oversee and operate them effectively. Many sectors still rely on labour-intensive tasks where automation has limits. Hence, upskilling becomes critical. To tie it all together, standardised processes and transparent data governance through ERP systems help identify faults and dependencies early. Ultimately, a blend of agile automation, trained workers, and structured systems ensures both efficiency and adaptability.
How critical is real-time data in modern manufacturing, and what challenges still exist in implementing data-driven decision-making?
Real-time data is essential in modern manufacturing, enabling timely, accurate decisions that enhance efficiency and responsiveness. It begins with reliable master data, such as product specifications, forming a stable base, while transactional data captures live production activities. However, challenges arise in maintaining data accuracy—manual entries after shifts often introduce errors that compromise real-time insights. To overcome this, IoT-enabled automation offers continuous data flow, though integrating diverse systems into a single source of truth remains complex. Beyond collection, interpreting the data is equally critical. Data scientists must align models with specific operational goals, like just-in-time inventory, to avoid overstocking or delays. Finally, translating this data into clear dashboards ensures management can act swiftly, driving informed and agile decision-making.
How are Indian manufacturing firms adapting differently to smart manufacturing trends compared to their global counterparts?
Indian manufacturing firms are carving a unique path by leveraging a strong talent pool and adopting smart technologies, rather than relying solely on economies of scale like global leaders such as China. While other nations focus on high-volume production to drive down costs, Indian manufacturers are embracing automation, robotics, cloud computing, and digital governance to boost shop floor efficiency, reduce defects, and lower per-unit costs. This marks a clear shift from low-capex, margin-compromising models toward smart manufacturing investments that enhance competitiveness. The entry of large multinational companies is further accelerating this transformation, pushing Indian firms to adopt professional, process-driven practices and data-led decision-making. As a result, they’re achieving higher product quality and throughput, competing globally through innovation and operational excellence, not just production scale.
Do you think AI and predictive maintenance are overhyped in manufacturing — or are we just scratching the surface?
We’re only beginning to explore what AI and predictive maintenance can truly offer manufacturing. Although widely discussed, their real impact depends on an organisation’s readiness to implement them effectively. The core issue isn’t the technology itself, but the lack of clean, structured, and consistent data. Many manufacturers still operate without robust ERPs or process-driven systems, making it difficult to gather the quality data AI requires. Without this foundation, most AI-led efforts fall short of expectations. Predictive maintenance, for instance, has immense potential to reduce costly breakdowns and unnecessary maintenance. But for it to deliver results, companies must first streamline data collection and governance. With the right infrastructure in place, AI can evolve from hype to a powerful driver of operational efficiency.
With sustainability becoming central to manufacturing strategies, how are plants measuring environmental performance more intelligently?
Manufacturing plants are becoming more intelligent in measuring environmental performance by focusing on waste reduction across the entire value chain. The traditional acceptance of scrap is being challenged, with companies reassessing processes to cut down on material loss, energy usage, and logistics overheads. Waste is now seen more holistically—not just as rejected output, but also as hidden inefficiencies like excessive heat treatments, unnecessary transport, or repeated quality checks.
To gain real-time visibility, many manufacturers are deploying IoT-enabled sensors to track emissions, energy consumption, and material efficiency. In sectors like chemical manufacturing, there’s a growing shift toward sustainable raw materials and innovative reuse of by-products. Simulation tools further support these efforts by identifying process alternatives with lower environmental impact. Together, these tools enable data-driven, proactive strategies that drive smarter, sustainable manufacturing.