LEVERAGING DATA SCIENCE TO ENHANCE FMEA: A PROACTIVE APPROACH TO RISK MANAGEMENT IN PRODUCT DEVELOPMENT AND MANUFACTURING

Abstract

FMEA – Failure Mode Effect Analysis method is used as a critical tool for identifying and mitigating risks to ensure efficiency, cost-effectiveness, and high product reliability.

FMEA is usually done with the traditional methods that are often manual, time- consuming, subjective, biased, and prone to inconsistencies due to non-standardized

failure records. The purpose of this study is to develop and propose a data-driven FMEA framework to reduce manual effort, increase objectivity in risk assessment, and enable proactive quality improvement. The study will use data science with the integration of K-Nearest neighbors (KNN) and fuzzy logic to enhance the identification, classification, and prioritization of failure modes. Collected real-world FMEA data from PT X, a screw manufacturing company in Indonesia, and gained expert insights into AI integration, which provides a foundation for developing adaptive FMEA models. Traditional FMEA at PT X is reactive, manual, with non-standardized failure modes. The proposed enhanced FMEA framework will enable automated failure classification, objective Risk Priority Number, faster scoring, and manage more data without manual intervention, and it will be faster and efficient in the meantime. It will increase consistency and objectivity. The integration of KNN and Fuzzy logic methods into the FMEA process can improve risk management practices in product development in manufacturing, making it more adaptive, data-driven, and proactive. For PT X, it can reduce the time to respond when addressing potential failure. The approach is scalable and can be applied across industries, aiming to modernize the FMEA process. The proposed method will enable decision makers to use a new way for application of machine learning techniques in FMEA as align with industrial 4.0 to leverage digitalization.