Kuliah Praktisi_PdM 4.0

Introduction to Pertamina's maintenance strategy including reviewing maintenance activities, predictive maintenance concepts, data science in predictive maintenance, and the importance of maintenance activities for equipment reliability and functionality.

Discussing the RCM strategy including the selection of maintenance approaches based on failure analysis, the importance of preventive maintenance, and the decision-making process for RCM implementation on equipment.

Explanation of digital transformation in maintenance management, benefits of digitalization, utilization of data for optimized maintenance strategies, and examples of digital tools like decision diagrams for maintenance management.

Explanation of predictive maintenance, use of data analysis for predictive modeling, predictive maintenance applications for equipment failure prediction, and the impact of predictive maintenance on maintenance effectiveness and cost efficiency.

Description of descriptive, predictive, and prescriptive analytics in maintenance, focusing on analyzing historical data, predicting equipment behavior, and providing recommendations for maintenance actions based on data analysis.

Explaining the dashboard and prescriptive analytics functionality in maintenance management, including using the dashboard for equipment condition monitoring, fault analysis, and recommendations for maintenance actions based on data insights.

Discussion on the ease and challenges of implementing predictive maintenance, monitoring by engineers, software installation, and managing change for software adoption.

Exploring the challenges faced in implementing predictive maintenance, such as software installation without utilization, managing change for software adoption, and building capabilities through training workshops.

Discusses the monitoring of Key Performance Indicators (KPIs) in predictive maintenance, the importance of meetings to address equipment conditions, dashboard usage for monitoring, and tracking performance indicators for alerts and failures.

Examining the need for system optimization in predictive maintenance, addressing failures due to training inaccuracies or equipment context dependencies, and avoiding false negatives to maintain KPIs.

Detailing a case study on preventing equipment failures through predictive maintenance, emphasizing the cost-effectiveness and benefits of proactive maintenance strategies.