Vehicle grading is a process followed to assess the condition of a vehicle before it is sold, leased, auctioned, or returned (at the end of a rental / leased period). It helps document the level of wear and tear a vehicle has and whether it needs repairs. Grading systems are widely used in the automotive industry to ensure transparency, fair pricing, and repair decisions. This blog covers
- Commonly used industry standards for vehicle grading – NAMA, BVRLA Fair Wear and tear guide
- Different purposes of these vehicle grading options
- How does AI help in vehicle grading and practical use cases
Various Vehicle Grading Options
1. NAMA Grading
NAMA (National Association of Motor Auctions) Grading is a standardized system used by vehicle auctions in the UK to assess and classify the condition of used vehicles. It helps buyers and sellers make informed decisions by providing a clear understanding of a vehicle’s condition before purchase. This grading system is widely used across auctions to ensure consistency and transparency in the vehicle resale market.
Benefits of NAMA Grading
- Transparency in Vehicle Condition: Buyers know exactly what to expect, reducing uncertainty.
- Consistency Across Auctions: Standardized grading ensures all vehicles are assessed similarly, making comparisons easier.
- Time-Saving: Buyers can quickly evaluate a vehicle’s condition without the need for extensive inspections.
- Accurate Pricing: Sellers can set fair prices based on the grade, preventing overpricing or undervaluation.
- Increased Buyer Confidence: Reduces the risk of unexpected repair costs after purchase.
How NAMA Vehicle Grading Works
The vehicle undergoes a thorough inspection, covering aspects such as paint condition, and interior wear. Any dents, scratches, missing parts, or other damage are noted. Based on the findings, the vehicle is assigned a grade from 1 to 5, with 1 being the best condition and 5 being the worst.
NAMA Grades Explained
- Grade 1: The vehicle is in excellent condition, with minimal or no visible wear. It may have tiny marks that do not require repairs. No significant damages.
- Grade 2: The vehicle has minor issues such as small scratches, dents, or scuffs, but no serious damage. No major repairs are needed.
- Grade 3: Noticeable wear and tear, including moderate scratches, small dents, and minor repairs required. The vehicle is still functional and roadworthy.
- Grade 4: The vehicle has significant damage, such as larger dents, deep scratches, or minor issues. It requires moderate repairs to restore its condition.
- Grade 5: The vehicle has heavy damage or major faults. It may need extensive repairs before it can be used again.
2. What is BVRLA Fair Wear and Tear Guide?
The BVRLA (British Vehicle Rental and Leasing Association) Fair Wear and Tear Guide is a set of guidelines used by leasing companies, rental businesses, and fleet operators in the UK. It defines acceptable levels of wear and tear on vehicles when they are returned at the end of a lease or rental period.
Benefits of the BVRLA Fair Wear and Tear Guide
- Prevents Unfair Charges: Ensures that customers are not penalized for normal usage.
- Reduces Disputes: Clearly outlines what is acceptable wear and tear, reducing disagreements between leasing companies and customers.
- Encourages Proper Maintenance: Helps customers understand how to take care of their vehicle to avoid extra costs.
- Standardized Assessments: Ensures that all vehicles are evaluated similarly, making the process fair.
How It Works
The guide sets specific standards for different vehicle parts, such as the exterior, interior, wheels, etc. The customer may be charged for repairs if a vehicle is damaged beyond acceptable wear and tear.
BVRLA Wear and Tear Categories
- Acceptable Wear: Minor scratches, small dents, and general aging that do not affect vehicle performance.
- Unacceptable Wear: Deep scratches, large dents, cracked or chipped glass, worn-out tyres, interior stains, or missing parts. These would result in additional charges.
The BVRLA produces three different Fair Wear and Tear Guides:
- Car: for drivers of contract-hired, leased, and financed cars
- LCV: for drivers and operators of contract-hired, lease, and financed light commercial vehicles
- CV: for drivers and operators of contract-hired, leased, and financed goods vehicles over 3.5t, and minibuses up to 17 seats
3. Other grading schemes
- BCA Grading:
British Car Auctions (BCA) is one of the largest vehicle auction companies in the UK, and it uses a grading system that gives an overview of the vehicle’s true condition from minor scratches to severe damages. - CAP HPI:
It’s the popular vehicle data provider for car retailers have a 3 level grading system for used vehicles (clean, average and below average) - There are other standards developed and used by various auction houses such as the South Western Vehicle Auctions (SWVA grading process)
How AI Can Help Improve Vehicle Grading
Artificial Intelligence (AI) is transforming the way vehicle grading is performed. Here’s a list of problems with traditional approaches to vehicle grading that can be solved with the help of AI:
Problem 1: Time taken for inspection (Image capture)
Manual vehicle inspections can be time consuming and labour intensive, especially at high-throughput auction centers, where each vehicle must be carefully manually assessed.
AI Solution:
Aston Barclay has introduced the Proovstation Inspection Gantry, an innovative automated technology that uses AI to assess vehicle damage. As the vehicle moves through the gantry, tire scanners measure wear, and cameras map the exterior, detecting chips, dents, and scratches. Within 90 seconds, the data is processed and sent to an inspector’s handheld device. Aston Barclay’s damage appraisal engine then uses the information to calculate repair costs and assign a NAMA grading to the vehicle.
Problem 2: Logistics costs of moving vehicles from customer location to vehicle evaluation centers
Transporting vehicles from customer locations to centralized evaluation centers is costly. Many vehicles are moved without knowing their actual condition, leading to unnecessary expenses and delays in the resale or auction process.
AI Solution:
By using AI-based remote self-inspections, for example retailers and leasecos can decide whether vehicles should be moved to an evaluation center, directly to auction, or for repairs. This approach reduces logistics costs, and speeds up decision-making.
ClearQuote, PAVE, and Tchek offer mobile weblink-based inspections for leasecos to remotely assess vehicles via a weblink, providing AI-driven damage detection.
Problem 3: Inconsistent manual damage assessment
Human inspections can lead to inconsistencies in assessing vehicle damages. Different assessors may classify the same damage differently as well as miss certain damages.
AI Solution:
AI-based damage assessment ensures consistency by analyzing images and detecting damage. This removes human bias and improves accuracy. ClearQuote specifically offers functionality to automate NAMA grading (demo here)
Problem 4: Time taken for inspection (Information capture)
Additional information needs to be captured while evaluating vehicles such as Registration number, VIN number, Tyre spec (make, model, year of manufacture etc.) which consume time and are prone to manual errors.
AI Solution:
ClearQuote app supports license plate scanning, mileage and warning lights readout from the odometer/instrument cluster, tyre information from images. These solutions reduce manual errors and actual time taken for the inspection.
An AI-based solution like Anyline can help in automating data capture for registration number, VIN, tyre information.
Interested in knowing about other AI-powered solutions for several fleet challenges? Check out this detailed blog.
Conclusion
Vehicle grading systems like NAMA and BVRLA ensure standardization and fairness in assessing vehicle conditions. Integrating AI into these systems enhances accuracy, speeds up processes, and provides valuable insights. Whether it’s for auctions, leasing, fleet management, or insurance, AI-driven grading tools are set to redefine how the automotive industry evaluates vehicles.
