ASHWIN R S

B-Platform & X-Platform Reliability Engineer | [Dates]

• Led emerging issue detection and signal identification for B & X platform engines using advanced statistical techniques including Life Data Analysis, Weibull Modeling, Decision Trees, and Hypothesis Testing, enabling early risk detection and proactive design/maintenance improvements.
• Performed deep-dive reliability analytics to assess issue criticality and presented actionable insights to global teams, driving faster closure of emerging issues and improved fleet reliability.

Global Reliability Prediction Tool SME and Testing team lead

• Developed logics for LDA analysis, Mixed Weibull, and Repairable System Analysis, etc aligning with industry best practices in reliability forecasting.
• Testing Leadership: Managed a 7-member testing team to validate GRPT features, designed execution plans, and ensured error-free releases, improving tool reliability and adoption across global reliability teams.

Special Project: RAM Analysis for Genset for Defense Customer

• Successfully conducted Reliability, Availability, and Maintainability (RAM) analysis of the PSBU Genset for a defense application.
• Collaborated with cross-functional teams to perform DFMEA, criticality assessments, and reliability predictions, presenting results to the Directorate of Marine Design & Engineering (DMDE).
• Delivered availability and maintainability predictions that guided design decisions for defense-grade genset development.

Key Achievements:

• Enhanced fleet reliability by proactively detecting high-risk issues, reducing repeat failures.
• Strengthened corporate reliability analytics by embedding repairable systems and mixed-Weibull logic into GRPT.
• Delivered RAM analysis that supported successful defense genset qualification with DMDE.

Life Data Analysis (LDA) Tool Development (R Programming)

• Designed and developed an R-based dashboard integrated with Cummins’ database to automate data preprocessing and Life Data Analysis (LDA) for Engine Business Unit products.
• Standardized the reliability analysis process by embedding consistent statistical methodologies (Weibull, life data distributions) into the tool, ensuring repeatability and accuracy across teams.
• Eliminated manual data errors and reduced analysis cycle time by 80%, significantly accelerating issue detection and decision-making for product reliability improvements.
• Enabled engineers across business units to access a self-service reliability analytics tool, improving the adoption of data-driven reliability practices.