Balancing the maintenance strategies to making decisions using Monte Carlo method

Abstract

This study aims to develop comprehensive maintenance strategies tailored to enhance the dependability, performance, and lifespan of critical assets within industrial and organizational settings. By integrating proactive, preventive, predictive, and corrective maintenance tactics, our strategy seeks to minimize downtime, reduce costs, and optimize asset performance. Drawing from extensive case studies across various industrial sectors, our research utilizes robust data analysis to inform strategy development.We employ mathematical cost models and simulations using the Monte Carlo Method in MATLAB to evaluate the performance and robustness of different maintenance strategies, including time-based and condition-based approaches. Our findings demonstrate that a holistic maintenance approach significantly improves operational efficiency and asset longevity. Specifically, our analysis reveals that integrated maintenance strategies lead to reduced downtime, lower maintenance costs, and enhanced asset reliability.Policy implications of our research suggest that organizations should adopt integrated maintenance strategies to enhance asset reliability and performance, ultimately achieving sustained operational excellence. By emphasizing the importance of proactive maintenance measures alongside traditional reactive approaches, organizations can effectively manage their critical assets, leading to improved operational outcomes and long-term success. – Integration of proactive, preventive, predictive, and corrective maintenance tactics – Evaluation of performance and robustness through mathematical cost models – Application of the Monte Carlo Method in MATLAB for comparative analysis

Keywords

• Monte Carlo Method