Analytiva Fleet Analysis using PowerBi

Introduction

These recommendations reflect my role as a fleet analyst in guiding Analytiva toward optimizing its generator fleet for cost-efficiency, reliability, and compliance with industry standards and regulations. The insights and recommendations will help demonstrate my ability to provide valuable insights and drive data-informed decisions.

Page 1: Overview

Generator Type Distribution:

There is a balanced distribution of generator types (Diesel and Gas) which ensures versatility in the fleet. An imbalanced distribution with a majority of one type might lead to operational risks if regulations change or if one type becomes costlier.

Average Fuel Efficiency:

Lower values of g/kWh indicate good fuel efficiency, leading to cost savings. Eg the 1375 kVA Gas generator has a low fuel efficiency.

Higher g/kWh values may indicate inefficient fuel consumption, increasing operational costs. Eg the 30kVA generator.

Recommendation:

Identify the generators with the lowest fuel efficiency and prioritize them for efficiency improvements or replacement with more efficient models. Implement regular maintenance and tuning to improve fuel efficiency across the fleet.

Average Maintenance Cost:

Lower annual maintenance costs are generally better and contribute to cost efficiency. Higher annual maintenance costs may indicate a need for improved maintenance practices or equipment replacement.

Recommendation:

Analyze the generators with high maintenance costs and investigate if there are patterns or recurring issues. Develop a proactive maintenance strategy to reduce costs while maintaining reliability.

Total Fuel Cost:

Lower total annual fuel costs are typically better, provided they meet operational needs. High annual fuel costs could indicate inefficient fuel consumption or a need to explore alternative fuels.

Recommendation:

Examine fuel consumption patterns and consider alternative, more cost-effective fuel sources or technologies. Invest in fuel management strategies to optimize fuel consumption and reduce costs.

Page 2: Performance Metrics

Utilization Rate Distribution:

A distribution with high utilization rates (closer to 100%) indicates effective fleet utilization. Low utilization rates (closer to 0%) suggest underutilization, leading to inefficiencies and potential cost increases.

Recommendation:

Investigate generators with low utilization rates and explore ways to increase their usage, such as load-sharing, reallocation, or diversifying their applications.

Average Utilization Rate:

A high average utilization rate (e.g., above 90%) reflects efficient fleet utilization. A low average utilization rate may indicate underutilized units and increased operating costs.

Recommendation:

Promote load balancing and efficient resource allocation to increase utilization rates. Optimize scheduling to reduce downtime and maximize operational efficiency.

Downtime Hours:

Lower downtime hours are better as they indicate less disruption to operations. High downtime hours can lead to operational inefficiencies and increased maintenance costs.

Recommendation:

Investigate the reasons behind high downtime hours, especially for specific generator models. Implement predictive maintenance, regular inspections, and scheduled maintenance to reduce unplanned downtime.

Energy Generated vs. Fuel Consumed:

A strong positive correlation between energy generated and fuel consumed suggests efficient generators. A weak or negative correlation could indicate inefficiency, with high fuel consumption for lower energy output.

Recommendation:

Analyze the generators with high fuel consumption relative to energy generation and explore opportunities for improving fuel efficiency, which can lead to substantial cost savings.

Page 3: Cost Analysis

Total Maintenance Cost:

Lower total maintenance costs indicate cost-efficient maintenance practices. High total maintenance costs might suggest poor maintenance management or equipment issues.

Recommendation:

Review maintenance procedures and schedules for cost-effective maintenance practices. Consider predictive maintenance and condition-based monitoring to reduce unplanned maintenance costs.

Maintenance Cost vs. Fuel Cost:

A negative or weak correlation between maintenance cost and fuel cost can indicate cost-efficient maintenance practices. A strong positive correlation may suggest that high maintenance costs are linked to inefficient fuel consumption.

Recommendation:

Correlate maintenance costs with fuel costs to identify cost-effective maintenance intervals and practices that minimize fuel-related issues. Implement preventive maintenance and real-time monitoring.

Page 4: Operational Efficiency

Compliance Rate:

High compliance rates (close to 100%) indicate adherence to operational and maintenance standards, reducing operational risks. Low compliance rates suggest deviations from standards, potentially leading to costly operational issues or regulatory non-compliance.

Recommendation:

Ensure strict adherence to operational and maintenance standards and invest in training and equipment upgrades to improve compliance. Regular audits and assessments can help identify areas of improvement.

Generator Age Distribution:

A balanced distribution of generator ages may indicate an effective fleet renewal strategy. A skewed distribution with many older generators could lead to increased maintenance costs and lower efficiency.

Recommendation:

Develop a balanced fleet renewal strategy that takes into account the age distribution. Consider gradually phasing out older units to reduce maintenance costs and enhance fleet efficiency.

Depreciation Rate:

A low depreciation rate indicates that the value of assets is retained well over time. A high depreciation rate might suggest a need for more efficient asset management or equipment replacement.

Recommendation:

Evaluate the factors contributing to high depreciation rates, such as maintenance costs or low utilization. Develop a depreciation reduction strategy that may involve more efficient maintenance practices and asset management.