Artificial Intelligence-Based Product Durability Assessment

Abstract This study provides an AI-based product durability assessment framework for estimating the longevity of products based on historical repair logs. It uses a repair and maintenance dataset of medical equipment to develop a scoring model that assesses product longevity and failure trends despite limited data attributes. The dataset includes work order numbers, asset identifiers, equipment descriptions, manufacturers, models, serial numbers, service dates, and repair determinations. The study applies machine learning techniques to analyze patterns in failure frequency, time between failures, equipment types, and manufacturer-specific issues to develop a durability score that aids in optimizing maintenance scheduling and resource allocation. To improve the model, feature engineering is used on categorical fields, such as equipment type, manufacturer, and model, to change these into predictive features that demonstrate failure likelihood and product lifespan trends. Moreover, temporal analysis on repair dates shows the long-term reliability of specific models over time and further improves the durability score. The study correlates repair outcomes (determination descriptions) with failure frequency and time to failure to identify high-risk equipment and provide recommendations for preventive maintenance. The analyses demonstrate that, despite limited data attributes, it is possible to generate practical information about product longevity using AI models trained on categorical and temporal data.