NAKUL DINKAR KURKUTE

• Increased production throughput by 10% through the implementation of production scheduling, inventory management on SAP
• Conducted Capacity-Demand Analysis to balance machine utilization, workforce planning, and material flow, ensuring demand fluctuations were met efficiently, led a team of 5 technicians in RCA and PFMEA to identify and mitigate process bottlenecks, enhanced OEE tracking, ensuring TEEP improvement and process compliance.
• Conducted ergonomic assessments, man/machine analysis and 5S implementation to enhance chassis closure assembly efficiency; optimized tooling layouts, workstation ergonomics, and robotic weld cell integration, collaborating with crossfunctional teams to reduce waste using Value Stream Mapping (VSM), achieving an 8% improvement in cycle time consistency.
• Reduced assembly line downtime by 13% through Kaizen initiatives, process mapping, and Single Minute Exchange of Dies
• Implemented TPM strategies, integrating predictive and preventive maintenance to enhance reliability and optimize MTTR
• Led New Product Introduction proposing design changes, aligning them with assembly processes for manufacturing feasibility
• Utilized Creo for tooling design, managed BOMs for different product families on PTC Windchill PLM, prepared RFQs, validated new equipment, and ensured alignment with IQ/OQ/PQ protocols to meet scalability and quality objectives.
• Integrated a FANUC robotic weld cell with Siemens S7 PLC controls to enable quick-change fixture swaps for chassis subframe
• Applied SMED to reduce mechanical and programming changeover time by 28%, adapting high-mix, low-volume production
• Assisted in drafting CAPEX justifications and budget preparation for workstation improvements and equipment upgrades

➡️ Simulated hub-level flows for 600,000 truck arrivals in AnyLogic, comparing plug-in charging vs. battery swapping, and optimized throughput to reduce lead time by 18%. Created Power BI dashboards to track trip times, charger delays, and swap efficiency across 113 locations.

➡️ Used Python and SQL to analyze 30 million SKUs across 600,000 truck movements, estimating 1.77 billion kWh/year in charging needs. Optimized charger allocation, truck arrival scheduling, and dock labor shifts based on spatial demand clustering at 113 logistics hubs.

➡️ Designed optimal storage layouts by conducting method studies on truck unloading, charging wait time, and energy demand buildup. Applied R-based statistical analysis to balance grid loads, reducing inventory buffer overruns and improving space utilization by 22%.

➡️ Trained ML models to forecast hub-wise hourly energy demand from simulated trips and SOC-based charging behavior, enabling predictive staffing and charger deployment. Improved forecast accuracy and established engineered standards to increase resource efficiency by 25%.

• Identified Improvement targets by root cause analysis, optimizing cleaning intervals and water treatment to resolve fouling issues
• Standardized cleaning protocols and revised SOPs, boosting utilization of boilers by 11% and reducing unplanned shutdowns

• Utilized Proplanner MES to eliminate NVA tasks, aligning high volume injection molding workflows with IATF 16949 standards
• Reduced defect rates by 4% and improved operational efficiency through workflow streamlining and standardized work.