Notes

Brief Report: AI-Enabled Digitalisation of Driver Movement Logs

Prepared by: Ronny Scott
Department: IVMS / Fleet Technology
Date: 20 July 2026

1. Background

Management is exploring the digitalisation of driver movement logs to replace manual record-keeping and improve operational efficiency.

The long-term objective is to integrate driver movement logs with existing tracking and operational data so that Artificial Intelligence (AI) can automatically identify abnormalities and reduce manual verification.


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2. Current Challenges

The current process relies heavily on manual records and comparisons, which presents several challenges:

Manual driver movement logs are time-consuming to complete and verify.

Comparing movement logs with GPS tracking and fuel usage is largely manual.

Potential discrepancies may go unnoticed or take significant time to investigate.

Reporting requires considerable administrative effort.



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3. Proposed Solution

The proposal is to digitise driver movement logs using an electronic platform that allows drivers to record trips electronically.

The digital records would then be compared with data from existing systems such as:

IVMS / GPS Tracking (Wialon)

Fuel card transactions

Vehicle movement history

Dispatch records (where applicable)


AI tools such as Claude would assist in analysing this data and highlighting any inconsistencies for management review.


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4. Examples of AI Use Cases

AI could automatically identify:

Differences between logged routes and actual GPS routes.

Unauthorised vehicle movements.

Excessive or unexplained fuel consumption.

Missing or incomplete driver logs.

Unexpected vehicle stops or route deviations.

Trips outside approved working hours.


Instead of manually reviewing every trip, supervisors would only investigate the exceptions identified by AI.


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5. Expected Benefits

The proposed solution is expected to provide the following benefits:

Reduced manual paperwork.

Faster verification of driver movements.

Improved fleet accountability.

Better fuel monitoring and fraud detection.

Improved compliance with operational procedures.

Faster reporting and investigations.

Better decision-making through accurate operational data.



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6. Potential Future Opportunities

Once implemented, the same concept could be expanded to support other operational areas including:

Response vehicle dispatch optimisation.

Guard patrol verification using geolocation.

Automated incident reporting.

Technician activity monitoring.

Fleet performance dashboards using Power BI.



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7. Role of the IVMS Department

The IVMS department will play a key role by providing vehicle tracking data, GPS history, mileage information, and system integrations required to compare movement logs with actual vehicle activity.

This will ensure that AI analysis is based on accurate and reliable operational data.


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8. Conclusion

This initiative is intended to improve operational efficiency by replacing manual verification with automated data analysis. By combining digital movement logs with GPS tracking, fuel data, and AI-assisted analysis, the company can improve accountability, reduce administrative workload, and identify operational anomalies more quickly.