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
Additional cameras had to be installed at the bay to track the activities2-3% error rate across shifts due to fatigue, high volumes, and inconsistent counting methods between staff.
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No independent verification layer
One person counted per bay per shift with no cross-check mechanism. Errors and misreporting were equally invisible until end-of-day reconciliation.
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Unstructured bag placement
Irregular stacking and placement made accurate bag-by-bag counting difficult during high-volume loading 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 bay cameras integrated across all loading and unloading zones, with additional coverage where gaps existed.
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Trained to count bags accurately across varied stacking arrangements, lighting conditions, and movement speeds.
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Model trained on correct and incorrect placement scenarios to handle real bay conditions reliably.
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Dual validation layer runs independently alongside the human counter - any discrepancy flagged before count is confirmed.
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Inflow and outflow counts logged separately per bay, giving a complete movement record for every shift.
