Built and standardized an end-of-line (EOL) parameter collection system to reliably capture critical process and test values by unit/serial and shift, improving traceability and creating a clearer link between process conditions and downstream quality outcomes. The solution emphasized consistent parameter definitions, streamlined data entry and validation, and automated rollups for fast lookback analysis during quality events. Using structured data tables and automated reporting (e.g., Excel-based dashboards with pivot-style summaries and refreshable transforms), the tool enabled faster containment decisions, sharper root cause investigations, and more consistent cross-shift visibility into process health. For leadership, it reduced reliance on tribal knowledge and improved operational control by making performance drivers measurable, comparable, and reviewable.

Designed and deployed a changeover/startup sheet to standardize setup verification and early-run stabilization after product or configuration changes, reducing the variability that often drives startup scrap, rework, and schedule disruption. The sheet created a repeatable "green light" process that aligns operations, maintenance, and quality expectations by capturing critical checks (materials, settings, tooling, and CTQ verification) and clear acceptance criteria before normal production resumes. By applying standard work, visual controls, and shift-handoff integration, the approach improved consistency across teams and reduced the hidden factory created by inconsistent startups. This improved schedule attainment and predictability, tightened execution discipline, and made training and accountability easier because expectations were explicit and auditable.

Led a focused constraint-removal effort to eliminate cycle-time limitations by identifying bottlenecks and systematically addressing the highest-impact drivers of lost time and variability. The work combined time studies, station-to-station comparisons, and performance-loss review (OEE/performance loss logic) to isolate constraints, then implemented practical changes such as standardizing work content, removing non-value-added steps, tightening sequences and handoffs, and reducing recurring micro-delays that prevented consistent takt performance. The project used prioritization tools like Pareto ranking and workload balancing concepts (Yamazumi-style thinking) to ensure effort targeted throughput, not just activity. The result was a more stable production rhythm, improved capacity utilization, and better delivery reliability without requiring major capital investment.

Developed an advanced quality monitoring dashboard to shift quality management from reactive review to proactive detection by trending defects at a higher cadence with clearer categorization and ownership. The dashboard standardized defect taxonomy and organized signals by defect code/category, station/process step, and time window, enabling early identification of trend changes before they became major scrap spikes or customer risk. Built with automated reporting workflows (e.g., Excel/BI-style refreshable dashboards) and visual management tools (Pareto charts, trend indicators, and basic SPC-style thinking where appropriate), it created a consistent "single view" for daily/weekly quality review. For leadership, it improved prioritization and alignment, reduced the time to recognize and respond to emerging issues, and strengthened accountability by making the top drivers visible and trackable.

Improved battery testing efficiency by reducing avoidable delays and increasing repeatability in the test workflow while maintaining the rigor required for product confidence. The project focused on where time was being lost—waiting, retest loops, manual handling, inconsistent setup readiness—and implemented changes centered on process standardization, clearer test readiness criteria, simplified operator steps, and better visibility into failure modes and retest drivers. Using process mapping, time studies, standardized work, and automated rollups of test performance (throughput, retest trends, and failure categorization), the improvements increased effective station utilization and stabilized flow. This strengthened throughput capacity at a critical control point, improved schedule predictability, and reduced the operational drag of rework and repeated testing.

Led a structured defect reduction initiative targeting recurring quality losses that were driving scrap, rework, and operational instability, with an emphasis on sustainable corrective actions rather than short-term fixes. The approach combined defect stratification (where/when/how often), Pareto prioritization, and disciplined root cause analysis (5 Whys/fishbone-style thinking) to identify true drivers, then implemented countermeasures addressing both technical causes (process settings, tooling, materials) and execution causes (standard work clarity, training, and escalation triggers). Corrective actions were supported with control mechanisms—updated procedures, verification steps, and ongoing visibility—so regression could be detected early. The outcome was reduced cost of poor quality, improved yield protection, fewer production disruptions, and a stronger problem-solving culture grounded in data and repeatable controls.