I turn warehouse data into decision-grade evidence.

Inventory was being deducted in WMS without creating valid pick assignments, so product never moved while the system behaved as though it had shipped.

Assignment logic allowed deductions without a corresponding executable pick path, hiding the failure behind system-valid looking transactions.

Built the evidence trail, quantified the site-level exposure, escalated the issue to leadership, and pushed it into an official vendor investigation path.

Vendor-level ticket opened after leadership escalation; the finding moved beyond local operations into enterprise review.

The mainframe enforced a four-unit minimum while WMS allowed sub-minimum shipments, creating repeatable overbilling and fulfillment contradictions.

Cross-system business rules were not aligned, so one environment permitted transactions the other priced and interpreted differently.

Analyzed shipment patterns by order size, reconciled system behavior across both environments, and documented a network-level architecture mismatch.

Established that the issue was systemic cross-system logic conflict rather than user error or isolated transaction noise.

Valid reserve locations were being skipped or underfilled because location descriptions, cube assumptions, and putaway logic were misaligned.

Wrong height limits, incorrect cube capacities, and missing location types in WMS location master data blocked otherwise usable storage.

Audited location data against physical conditions, mapped the logic path, and quantified recoverable space without infrastructure spend.

Produced a clear zero-capex recovery path that translated system cleanup into tangible storage capacity.

Persistent discrepancies between cycle count quantities and billed quantities appeared across many SKUs instead of behaving like isolated counting noise.

Process gaps in the inventory-control workflow allowed count-to-billing mismatches to persist without a consolidated view of impact by item.

Built SQL queries to join CC2 records against billing tables, grouped findings by item, and quantified both per-item and aggregate overage.

Full analysis documented and reported, reframing the issue as systemic financial exposure rather than local variance.

WMS cube values did not match physical product dimensions, undermining slotting, utilization, trailer loading, and transportation assumptions.

Cubiscan measurement values stored in WMS were systematically lower than actual product dimensions.

Re-measured representative items, compared physical dimensions to system values, and modeled how understated cube data distorted planning.

Converted a hidden data-quality issue into an operations and cost story decision-makers could act on.

"Twilight Zone" inventory appeared at rates suggesting systemic causes rather than random human error or one-off handling misses.

Reserve overstates led to Cherry Pick failures, which triggered stocking abandons and ultimately generated stray-case inventory exceptions.

Extracted and segmented 20K+ ABN records by reason code, location, time, and item, then cross-referenced those records against XYZ defect data to establish the causal chain.

Investigation moved from loose suspicion to repeatable failure-mode analysis with a defensible logic path.

High-velocity SKUs were sitting in low-efficiency slots while putaway rules lagged behind actual demand patterns and corrected cube dimensions.

Slot assignments were not aligned with velocity tiers, and putaway logic was not incorporating corrected cube dimensions, anchor locations, and reserve constraints.

Ranked SKUs by velocity, cross-referenced them with corrected Cubiscan data, and built a putaway decision framework around cube capacity, reserve height, and anchor-location logic.

Developed a practical optimization model aimed at reducing travel, overflow, and storage inefficiency without adding infrastructure.

Receiving discrepancies and ASN accuracy issues were costly, repetitive, and hard to prioritize without a structured view of repeat offenders and trends.

Vendor compliance issues were dispersed across daily exceptions instead of being tracked as a recurring analytical pattern with accountable sources.

Tracked discrepancies by vendor, receiving event, and exception type to surface repeat offenders and support better operational follow-through.

Turned ad hoc exception handling into a repeatable compliance signal that supports vendor conversations and internal accountability.