About this Video

• Video Title: Using Key Performance Indicators (KPIs) to measure your plant’s maintenance health
• Channel: Visual K
• Speakers: Ernesto Perez, Angel Torres
• Duration: 00:30:46

Introduction

This webinar explains how to use Key Performance Indicators (KPIs) to assess the health of a power plant's maintenance operations. It focuses on the "Maintenance Asset Health Index," a system designed by Visual K to analyze various factors and provide a comprehensive health score.

Key Takeaways

• Maintenance Asset Health Index (MAHI): A system using six key indicators (probability of failure, PM compliance, work order split, work order priority, asset condition, and critical spares) to assess plant maintenance health.
• Six Key Indicators: The MAHI considers the probability of failure based on time until the next overhaul, preventive maintenance (PM) compliance, the ratio of preventive to corrective work orders, the prioritization of work orders, the condition of assets as reported by field technicians, and the availability of critical spare parts.
• Data-Driven Approach: The MAHI leverages existing maintenance transactional data, minimizing the need for complex engineering calculations. It uses a weighted average of the six indicators to produce a single health score.
• Actionable Insights: The resulting dashboard provides a clear, hierarchical view of maintenance health from the plant level down to individual components, allowing for targeted interventions and improved decision-making.
• System Applicability: The MAHI methodology is adaptable to different CMMS systems and generation technologies (steam turbines, wind turbines, etc.).

The six components of the Maintenance Asset Health Index are:

1. Probability of Failure: This is calculated based on the time elapsed since the last major overhaul relative to the time until the next scheduled overhaul. The closer to the next overhaul, the higher the probability score.

2. PM Compliance: This measures the percentage of preventive maintenance work orders scheduled and completed on time within the period between overhauls.

3. Work Order Split: This is the ratio of preventive to corrective work orders generated during the period between overhauls. A higher ratio of preventive to corrective work orders indicates better PM effectiveness.

4. Work Order Priority: This factor assesses how effectively urgent and critical work orders are addressed. It uses an algorithm that considers the asset's criticality, the work order's impact, and the work order's age.

5. Asset Condition: This is a subjective assessment of the asset's condition, provided by field technicians on a scale, typically ranging from 1 (very good) to 5 (needs immediate replacement). The worst condition among critical components is rolled up to the system level.

6. Critical Spares: This compares the required quantity of critical spare parts to the available stock. A perfect match is a "hit", and a shortage is a "miss".

The transcript does not provide the specific weights assigned to each of these six factors in the final MAHI calculation. It only mentions that a weight factor is used, and this can be configured at the company, plant, or unit level.