NAKUL DINKAR KURKUTE

➡️ 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%.

• Improved production throughput by 10% by implementing SAP-based production scheduling and refining inventory management to reduce part shortages and rescheduling loops
• Balanced capacity and demand through hands-on analysis of machine utilization and shift labor, during high-mix weeks.
• Improved cycle time consistency by 8% in chassis closure assembly by conducting ergonomic assessments and collaborating with weld operators and tooling engineers to reconfigure layout and integrate a robotic weld cell.
• Reduced assembly line downtime by 13% by leading Kaizen workshops (5 technicians) that streamlined process mapping, uncovered hidden bottlenecks and accelerated mechanical and PLC changeovers.
• Supported NPI by reviewing Creo models and proposing design tweaks to align with fixture constraints and routing logic, DFM
• Implemented TPM at the work cell level by coordinating predictive and preventive maintenance, reducing unplanned downtime and improving MTTR in high-utilization engine assembly zones.
• Managed multi-product BOMs in Windchill, aligned configurations with shop needs, and supported ramp-up by preparing RFQs, validating fixtures, fine tuning OEE tracking and ensuring IQ/OQ/PQ compliance
• Reduced fixture and program changeover time by 28% by applying RCA and aligning mechanical changeouts with digital reprogramming for high-mix, low-volume builds.

• 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.