In the race to electrify transportation and scale next-generation manufacturing, few challenges are as pressing as building reliable, cost-effective, and sustainable battery production systems. At the center of this effort is Kartik Gurav, a manufacturing engineering leader who has spent his career designing and launching complex production lines across high-speed consumer goods and electric vehicles.
With more than a decade of experience, Gurav has overseen global equipment ramp-ups that unlocked gigawatt-hours of new capacity while saving hundreds of millions in costs. He holds a Master’s degree in Product Development Engineering from the University of Southern California and brings certified expertise in Six Sigma, Project Management, and Smart Manufacturing.
We sat down with Gurav to discuss what smart manufacturing looks like on the factory floor today, the role of automation in scaling without sacrificing quality, and the innovations that will define Industry 4.0 in the decade ahead.
Smart manufacturing has become a buzzword in recent years. How do you define it in practical terms, and what does it look like on a factory floor today?
Smart manufacturing is about distinguishing valuable data from non-valuable data, capturing it accurately at the source, and integrating it into a closed-loop feedback system. This enables systems to self-correct and overcome day-to-day variabilities. In today’s world, a standalone automation system is no longer enough. If equipment has a PLC, data can be captured and fed into secondary systems for analysis. When this is scaled across hundreds of interconnected systems, manufacturers gain large datasets that can be synthesized into actionable insights for strategy and daily operations.
The foundation of smart manufacturing lies in automation systems equipped with instrumentation that can collect critical information about process variability, equipment health, and human interaction. On the factory floor, this translates into practical implementations such as:
- ETL pipelines linking profilometer or vision systems with downstream processes to maintain accuracy through individual part scans.
- SCADA dashboards that go beyond simple blocked or starved condition reporting to provide early warnings about non-optimal conditions, like low pallet counts or throughput risks.
You’ve overseen major global equipment ramp-ups. What role does automation play in scaling production lines quickly without sacrificing quality?
Scaling production at high volumes demands continuous process flows with minimal interruptions. Cycle time becomes the most critical factor, and automation is essential for achieving the lowest possible cycle time while maintaining a first-pass yield above 99%. This requires thoughtful automation design that incorporates smart process flows, robust PFMEA, and cutting-edge technologies.
Automated quality control plays a central role here. Inline vision systems, paired with rejection mechanisms, ensure that products can be tested and filtered without line stoppages. These vision systems are often integrated with testing architectures such as hipot tests, leak tests, and end-of-line functional checks. Together, they provide closed-loop feedback to upstream processes, enabling corrective action or initiating automated shutdowns in cases of major yield fallout.
Can you share examples of how IoT, AI, or machine learning have delivered measurable improvements in large-scale manufacturing environments?
Data capture through equipment instrumentation, such as sensors, flowmeters, and pressure transducers, feeds machine learning models that continuously track process capability. These models can detect gradual process shifts and raise alerts before they cause unplanned downtime or yield loss.
Advanced imaging systems are another example. Techniques like thermal pulse imaging identify electrical joining defects that would otherwise go undetected by conventional visual or camera inspection. This reduces field risk while ensuring performance targets are met.
Data is often described as the new oil. In manufacturing, what are the most valuable data points to capture, and how can they drive better decision-making?
Real-time insights come from data such as RFID tags on carriers, which provide visibility into blocked or starved conditions. Cycle times at each station, including manual ones, help identify bottlenecks and drive staffing or training adjustments to meet takt time requirements.
Process parameters like adhesive dispense pressures, flows, temperatures, bond pull strength, and cure times are tracked and mapped to each manufactured unit. This ensures that yield targets are met and supports the rapid implementation of countermeasures for optimization.
Data aggregation tools like Splunk loggers, SCADA systems, and yield dashboards further classify downtime, track utilization losses, and highlight areas for yield improvement.
What are the biggest challenges manufacturers face when trying to integrate Industry 4.0 technologies into legacy production systems?
Legacy systems often pose integration challenges because they rely on proprietary controls and hardcoded architectures. When production lines use equipment from multiple vendors, interoperability becomes even more difficult, especially if APIs are unavailable. As a result, manufacturers often resort to manual data transfers from CSV files or PLC logs, which complicates the creation of robust ETL pipelines.
Retrofitting legacy systems also requires significant downtime with limited ROI, making companies hesitant to undertake large-scale upgrades. Feeding profilometer scan data into laser cleaning stations, for example, can be particularly difficult without re-engineering the system.
You’ve worked across both high-speed consumer goods and next-gen EV platforms. What lessons from one industry transfer effectively to another when it comes to automation and digital strategy?
In the consumer goods industry, volume is everything. Line speeds can reach thousands of units per second, and automation is optimized for continuous motion and razor-thin margins. This industry has decades of maturity, while battery manufacturing is still evolving, yet it stands to gain much by adopting proven automation strategies.
Battery manufacturing requires processes that are both predictable and repeatable, even though architectures are far more complex. Lessons from consumer goods that transfer effectively include:
- Designing continuous automation with inline vision that captures images while products are in motion.
- Building flexible, high-speed conveyance systems that maintain flow while removing non-conforming products.
- Selecting processes with microsecond-level cycle times to meet demand.
- Driving digital strategy through integrated SCADA systems for accurate MTBF and MTTR reporting, ensuring uptime remains the ultimate goal.
How do you see sustainability goals intersecting with smart manufacturing? Can automation and data-driven systems actually reduce energy use and waste at scale?
Scrap reduction is critical in battery manufacturing, where each scrapped pack represents wasted material and energy in a supply-constrained environment. Closed-loop systems powered by automation and data tracking enable root cause analysis and dynamic process adjustments to minimize scrap.
Energy use is another area where data delivers impact. By monitoring power consumption across processes, manufacturers can identify areas for savings and activate smart modes, such as sleep and wake cycles during blocked conditions. Adjustments like warm-up and cool-down modes for large ovens reduce kilowatt-hours per pack, cutting both waste and cost.
Looking ahead, what innovations in Industry 4.0 excite you most, and how do you see them shaping the future of global production over the next decade?
The next decade of Industry 4.0 will be defined by flexibility, intelligence, and global integration. Manufacturing lines capable of handling varied form factors and different cell types will create immense value. Humanoid robotics could take on repetitive yet difficult-to-automate tasks, ensuring quality while reducing reliance on manual labor.
Closed-loop feedback will become even more advanced, with systems dynamically adjusting process parameters based on root-cause analysis of real-time data. On a global scale, interconnected manufacturing networks could share learnings, balance production loads, and optimize processes across borders.
