Quarra — Inventory & FBA Optimization

Overview

Inventory stockouts are the primary performance bottleneck for this account. When top-selling SKUs go out of stock, ROAS suffers directly and immediately — there is no amount of ad optimization that compensates for zero available units. This article documents the stockout situation observed in early March 2026, the root causes, and the AI-driven shipment model being developed to prevent recurrence.

See also: ^[1] | ^[2]

The Problem: Stockouts Suppressing ROAS

As of the March 4, 2026 weekly call, ROAS was 3.35 (up slightly from 3.3 the prior week), but performance remained below trend. The cause was confirmed as inventory, not ad quality.

Active Stockouts at Time of Review

The Old World White Popcorn stockout is particularly damaging given its sales velocity. With 425 units/month in normal conditions, every day out of stock represents meaningful lost revenue and suppressed organic rank.

Compounding Factor: Shipment Pickup Delays

Inbound shipments scheduled for March 9 were delayed due to unresolved pickup logistics on Carly's end. Units were in the system but not yet moving. This is a process gap — shipments need to be confirmed in motion, not just scheduled.

Positive Signals (Despite Stockouts)

Even with inventory constraints, several metrics were trending in the right direction:

• ROAS: 3.35, up from 3.3 week-over-week
• TACOS: Decreasing → improving profit margins
• Organic units: Growing
• Subscribe & Save sales: Growing

The interpretation: the underlying account health is improving. Resolving inventory issues should produce a step-change improvement in ROAS, not just incremental gains.

Solution: AI-Driven Inventory Shipment Model

Gilbert is building an AI tool to automate optimal shipment quantity calculations and prevent future stockouts. The model uses three inputs:

1. 30-day sales velocity — units ordered in the trailing 30 days per SKU
2. Lead time — typically 2–3 weeks from order to FBA receipt
3. Current FBA stock — existing units at fulfillment centers

The model outputs a recommended shipment quantity that accounts for the time gap between ordering and availability, ensuring buffer stock is maintained.

Demo planned for the following week's call.

Why This Matters

Manual inventory management at scale is error-prone. The current process relies on someone noticing a stockout or low-stock condition and reacting. The AI model shifts this to proactive, data-driven reorder triggers — the same logic as a reorder point system, but automated and continuously recalibrated against actual velocity.

Process Recommendations

• Never let a top-10 SKU by velocity reach zero FBA stock. Set a minimum buffer of at least 2× weekly velocity.
• Confirm shipments are physically moving, not just scheduled. "Pickup pending" is not the same as "in transit."
• Escalate pickup delays immediately. A one-week delay on a 425-unit/month SKU costs roughly 100 units of sales.
• Track inbound units separately from available units in reporting — ROAS benchmarks should note when top SKUs are stocked out, to avoid misreading ad performance.

Related

• ^[2] — Google Ads overhaul for Quarrowstone from the same call
• ^[3] — Claude Code demo diagnosing and fixing the Quarrowstone ad account
• ^[1]