The warehouse team was responsible for manually tallying every gunny bag moving in and out of the facility across multiple shifts. While staff were diligent, the volume of inbound and outbound movement - combined with shift handovers and irregular stacking arrangements - created consistent conditions for errors to accumulate undetected.
In a warehouse environment, a 2-3% error rate sounds manageable - until one missed gunny bag on an outbound load creates a client dispute, or an uncounted inbound bag creates a stock discrepancy that takes hours to trace back. With a single staff member tallying per gate and no cross-check in place, honest mistakes and deliberate misreporting were equally invisible until end-of-day reconciliation.
Problem Statement
THE CHALLENGE
Challenges
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High manual counting errors
2-3% error rate across shifts due to fatigue, high volumes, and inconsistent counting methods between staff members across shift handovers.
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No independent verification layer
One warehouse staff member counted per gate per shift with no cross-check mechanism. Errors and misreporting were equally invisible until end-of-day stock reconciliation.
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Unstructured gunny bag placement
Irregular stacking, tilting, and partial visibility of gunny bags during loading and unloading made accurate bag-by-bag counting difficult during high-volume windows.
Solution
THE SOLUTION
We implemented an AI-powered gunny bag counting system that integrates with the warehouse's existing camera infrastructure. The system does not replace the warehouse counter - it validates them. Every count the staff member records is independently verified by the AI, with any discrepancy flagged immediately before it becomes a stock record error. The model was specifically trained on gunny bag characteristics across stacked, loose, tilted, and partially visible scenarios to handle real warehouse floor conditions reliably.
Flow: Live gate camera feed -> Gunny bag detection and count -> AI count verified against staff tally -> Discrepancy flagged if mismatch -> Count confirmed and logged -> Full audit trail on dashboard
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Existing warehouse gate cameras integrated across all inbound and outbound movement points, with additional coverage where gaps existed.
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Model trained specifically on gunny bag characteristics - stacked, loose, tilted, and partially obscured - across varied lighting conditions and movement speeds.
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Dual validation layer runs independently alongside the warehouse counter - any discrepancy flagged before count is confirmed.
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Inbound and outbound counts logged separately per gate, giving a complete movement record for every shift.
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Full audit trail maintained on dashboard with timestamp, count, and image for every inbound and outbound movement.
