VEHICLE REPAIR REPORT
by Courage Ekwonye
INTRODUCTION
This data project is aimed at identifying themes to help manage the fleet of trucks, with a specific focus on repairs. In this project, I will analyze a comprehensive dataset of truck repair records to extract valuable insights and trends that can aid in effectively managing and maintaining the fleet.
The primary objective of this data project is to uncover recurring themes and patterns related to truck repairs within the context of a national truck rental compny. By analyzing the repair data, we aim to identify common repair types, underlying reasons for repairs, and any significant trends or correlations that can provide actionable insights to optimize fleet management practices.
PREPARATION AND PROCESSING
Data Collection Method:
The dataset utilized in this analysis was obtained via a methodical data collection approach intended to capture pertinent details regarding automobile upkeep and fixes. The implemented method of collection is as follows:
The vehicle maintenance records of a truck rental company served as the data source. The company regularly maintains these records as a component of their fleet management procedures.
The truck rental company employs a digital maintenance tracking system to document and oversee all maintenance operations for their fleet. The implemented system facilitates the effective acquisition and retention of maintenance and repair information, guaranteeing precision and uniformity.
Maintenance documentation is created by maintenance personnel whenever a vehicle undergoes maintenance or repair. The documentation includes details of the maintenance event. The dataset comprises of vehicle ID, state, repair type, reason for repair, year, make, and body type.
The process of data entry and verification involves the input of maintenance-related information into the maintenance tracking system, with a focus on ensuring the completeness and accuracy of all pertinent fields. Validation checks can be incorporated into the system to reduce data entry errors.
The maintenance tracking system undergoes periodic updates to incorporate the most recent maintenance and repair data as vehicles receive servicing or repairs. The implementation of this process guarantees that the dataset is current and accurately represents the latest maintenance operations.
The truck rental company employs data quality assurance techniques such as regular data audits and verification processes to ensure the accuracy and reliability of their data. The process aids in the detection of discrepancies, inconsistencies, or gaps in the data, which are subsequently resolved and corrected.
Appropriate security measures are implemented to safeguard the privacy and confidentiality of the data. The maintenance tracking system and its associated dataset are subject to access restrictions that limit usage to authorized personnel. Additionally, data security measures are in place to prevent unauthorized access or data breaches.
The dataset is subjected to data cleaning procedures to address missing values, outliers, and inconsistencies before analysis. The reliability and suitability of the data for analysis are ensured.
Fleet Maintenance data cleaned through use of Google Sheets
Version History: https://docs.google.com/spreadsheets/d/12nLQxWerc3cKvmOQRzYvr3ETCOyxV23sxhiNJP0rTl4/edit?usp=sharing
import pandas as pd
vehiclerepairs = pd.read_csv('CLEAN_FleetMaintenanceRecords.csv')
vehiclerepairsANALYSIS AND INSIGHTS
Most Common Repairs
SELECT DISTINCT Repair, COUNT(*) AS Frequency
FROM 'CLEAN_FleetMaintenanceRecords.csv'
GROUP BY Repair
ORDER BY Frequency DESC;from matplotlib import pyplot as plt
import pandas as pd
most_common_repairs = most_common_repairs.sort_values(by='Frequency', ascending=True)
plt.figure(figsize=(10, 6))
plt.barh(most_common_repairs['Repair'], most_common_repairs['Frequency'], color='xkcd:bright blue')
plt.xlabel('Frequency')
plt.ylabel('Type of Repair')
plt.title('Most Common Repairs')
plt.show()
Most common repairs: By looking at the repairs with the highest frequencies, we can identify the most common issues encountered in the fleet. In this case, "Fuel Tank" appears to be the most frequent repair, followed by "Tire Repair" and "Tire Replacement." This information can help prioritize resources, plan maintenance schedules, and ensure adequate inventory for the most frequently needed repairs.
Maintenance patterns: The data provides insights into the types of repairs that require more attention and resources. Repairs such as "Windshield Replacement," "Battery Replacement," and "Brake Line Replacement" appear with relatively high frequencies, indicating their significance in fleet maintenance. Identifying such patterns can inform proactive maintenance strategies to minimize downtime and potential safety risks.
Cost considerations: While the data does not explicitly provide cost information, it is reasonable to infer that repairs with higher frequencies may have an impact on maintenance costs. Repairs like "Fuel Tank," "Windshield Replacement," and "Tire Replacement" may require higher expenditures due to the frequency of occurrence. By integrating cost data, it is possible to analyze the financial implications and plan budget allocations accordingly.
Maintenance priorities: The insights obtained from the data can aid in setting maintenance priorities. For example, repairs like "Tire Repair" and "Tire Replacement" occur frequently, suggesting the need for regular tire inspections, proper tire maintenance, and timely replacements to ensure fleet safety and performance.
Potential areas for improvement: By identifying repairs that occur with relatively high frequencies, such as "Brake Line Replacement" or "Fender Replacement," fleet managers can investigate the root causes of these issues. Addressing underlying problems, such as faulty components or operational issues, can help reduce the occurrence of these repairs and improve overall fleet reliability.
Regions: Total Failures and Replacements
SELECT
CASE
WHEN State IN ('AZ', 'NM', 'TX', 'OK')
THEN 'Southwest'
WHEN State IN ('AR', 'LA', 'MS', 'AL', 'GA', 'FL', 'KY', 'TN', 'SC',
'NC', 'VA', 'WV', 'DE', 'MD', 'DC') THEN 'Southeast'
WHEN State IN ('PA', 'NJ', 'NY', 'CT', 'RI', 'MA', 'VT', 'NH', 'ME')
THEN 'Northeast'
WHEN State IN ('ND', 'SD', 'KS', 'NE', 'MN', 'WI', 'IA',
'MO', 'MI', 'IN', 'IL', 'OH') THEN 'Midwest'
WHEN State IN ('WA', 'ID', 'MT', 'OR', 'WY', 'CO', 'UT', 'NV', 'CA')
THEN 'West'
ELSE 'Other'
END AS Region,
COUNT(*) AS Frequency
FROM 'CLEAN_FleetMaintenanceRecords.csv'
WHERE Reason NOT IN ('Collision', 'Rust')
GROUP BY Region
ORDER BY Frequency DESC;