An Analysis of Manufacturing Machine Failures and Optimization Using Replacement Year Prediction

The manufacturing industry is highly susceptible to equipment failures, leading to costly downtime, production delays, and increased maintenance expenses. Effective maintenance planning and resource allocation depend on the early detection of possible faults and the precise forecasting of replacement years. The fundamental technique for assuring operational resilience, limiting disruptions, and improving preventative maintenance processes is manufacturing failure analysis. It entails the methodical analysis of failures and spans several sectors, including the automobile, aerospace, electronics, and heavy machinery. In this research, an integrated methodology for predicting replacement years in the manufacturing industry using operations research approaches and the Python-based machine learning algorithm Random Forest Classifier (RFC) is proposed. The program first calculates the total failure rate after importing manufacturing data from a dataset. The failure rate for each manufacturing line is then determined, and the lines with a high failure rate are identified. The program uses machine learning to improve the analysis by teaching a Random Forest classifier to anticipate failures. The model's performance is assessed by measuring the accuracy of a test set. To determine machine replacement years, it also incorporates replacement theory assumptions. Based on the company's founding year and the current year, it determines the replacement year considering the machine's lifespan. This program's advantages include recognizing production lines with high failure rates, employing machine learning to forecast problems, and offering suggestions on when to replace machines. Manufacturers may enhance their processes, lower failure rates, and increase overall efficiency by utilizing statistical analysis, machine learning, and optimization strategies. As technology advances, the field of failure analysis will continue to evolve, enabling firms to achieve improvements.