Maintenance Performance Determinants

1. Summary

The analysis of maintenance performance across various plants highlighted several key factors that can significantly impact operational efficiency. Equipment characterized by robotic arms generally experiences fewer maintenance issues, suggesting that these advanced machines promote higher productivity. Conversely, certain equipment types, particularly grinders and those with higher humidity levels or vibrations, tend to face more frequent maintenance challenges. Aging equipment emerges as a critical concern, with factors like discontinued parts, degraded seals, and system failures commonly leading to increased downtime and higher operational costs. Focusing on these variables can help organizations implement targeted strategies to enhance overall maintenance performance.

2. Key Findings

  1. Robot Arms: The use of robotic arms lowers maintenance issues, likely due to reduced mechanical wear and tear.
  2. Grinders: This equipment type has a 69% increased risk of maintenance problems, linked to greater wear, scarcity of spare parts, and electrical malfunctions.
  3. High Vibration Levels: Increased vibration correlates with more maintenance needs, signaling mechanical problems.
  4. Humidity Conditions: Higher humidity levels raise the likelihood of maintenance interventions due to faster degradation of equipment components.
  5. Aging Equipment: Common challenges include discontinued parts, lubrication issues, and increased failure rates as equipment ages, ultimately leading to extended production disruptions.

3. Method

The analysis employed a survival model to evaluate the factors influencing maintenance performance, specifically using Cox's proportional hazard model. This model estimates the likelihood of maintenance issues based on various covariates, allowing us to understand how different factors interact with equipment performance. It identifies variables associated with higher or lower risks for maintenance problems. However, the model assumes that the effects of the covariates are consistent over time, which might not always hold true. Additionally, while it effectively highlights relationships, it cannot definitively establish causality, so further investigation might be necessary to pinpoint underlying issues thoroughly.