Stockorlo connects to your existing data and produces SKU-level replenishment recommendations your buying team can approve without switching tools.
Mid-market e-commerce operators running 3 to 15 SKU categories with seasonal demand spikes face a structural problem that spreadsheet-based forecasting cannot solve. During off-peak periods, buying teams carry 18 to 28% excess safety stock because they build conservative buffers without a reliable signal for when demand will drop. During peak windows, those same teams stock out on 6 to 11% of their top-velocity SKUs because the manual reorder process runs 3 to 5 days behind actual demand signals.
The root cause is that Shopify, NetSuite, and Cin7 all hold accurate, timely data. But none of those systems talk to each other in a way that produces a replenishment recommendation. The buying team is the manual integration layer. They pull export files, reconcile discrepancies across systems, build formulas, and eventually make a decision — usually too late. Every Monday morning is four hours of that process instead of four hours of decisions that actually require human judgment.
Stockorlo is not a better spreadsheet. It is the layer that reads your existing systems, runs the forecast, and hands your buying team a ranked list of replenishment decisions to approve. The manual reconciliation step disappears. The Monday morning is back.
Stockorlo connects directly to your existing tools via native APIs. No data warehouse migration, no custom middleware, no new system to learn.
Authorize the Shopify Admin API, NetSuite SuiteScript, or Cin7 REST API. Stockorlo also ingests supplier lead-time data from Anvyl or a manual CSV upload. The first sync pulls historical order data, returns, on-hand inventory, in-transit receipts, and your existing promotions calendar. For most operators the initial data pull completes within a few hours.
Every night Stockorlo runs gradient-boosted time-series decomposition on a rolling 90-day window. The model applies promotion-lift adjustments for upcoming calendar events and inflates safety stock buffers automatically for suppliers with a documented lead-time drift above 15%. Each run produces a fresh SKU-level risk score and a ranked replenishment recommendation for every active SKU in your catalog.
Each morning your buying team opens a prioritized queue showing only the SKUs that have crossed the risk threshold you set. Each entry includes current on-hand inventory, in-transit receipts, trailing 30-day velocity, and recommended reorder quantity with context on why the recommendation changed from the prior day. Reviewing the queue typically takes 15 to 20 minutes rather than four hours.
One-click approval pushes the purchase order draft directly into NetSuite, Cin7, or ShipBob with vendor details, SKU quantities, and target receipt dates pre-populated from your existing supplier records. Stockorlo logs the approval and tracks the resulting receipt against the forecast, feeding those outcomes back into the model to improve future recommendations.
Stockorlo scores each SKU on a 0-100 stockout risk index updated every morning before the operations team starts their day. The score integrates current on-hand inventory, in-transit receipts, trailing 30-day velocity, and upcoming promotions from the calendar. SKUs crossing a configurable risk threshold surface in a priority queue.
Score history is retained for 12 months so operators can backtest against actual stockout events and calibrate their threshold.
When an operator adds a planned sale or influencer campaign to the Stockorlo promotions calendar, the demand model recalculates affected SKU forecasts within two hours using historical promotion lift data. For new SKUs without promotion history, the system uses category-level lift benchmarks from similar events.
Operators see a before-after forecast comparison so they can approve the adjusted reorder quantity before it enters the purchase order queue.
Stockorlo builds a rolling lead-time accuracy profile per supplier by ingesting confirmed purchase order dates and actual receipt timestamps from Anvyl or manual import. When a supplier drifts more than 15% above their stated lead time over the prior 60 days, the system automatically inflates the safety stock buffer for that supplier's SKUs.
Variance is surfaced in the weekly lead-time report so operators can have an informed conversation with the supplier.
When Stockorlo identifies a replenishment need, it assembles a purchase order draft with vendor details, SKU quantities, and target receipt dates drawn from your existing supplier records in NetSuite or Cin7. The draft is staged for one-click approval rather than requiring manual entry.
Stockorlo logs the approval and tracks the resulting receipt against the forecast to improve future recommendations.
For operators running parallel channels across DTC Shopify, wholesale, and Amazon Seller or Vendor Central, Stockorlo consolidates demand signals across channels before running SKU-level forecasts. Channel weights are configurable so operators can reflect that Amazon velocity is higher-confidence than a single wholesale buyer's reorder pattern.
The consolidated forecast prevents over-ordering because wholesale and DTC teams no longer run separate spreadsheet forecasts for the same SKU pool.
Stockorlo pulls active return authorizations from Shopify and ShipBob to estimate expected re-sellable inventory arriving within the next 14 days. This restockable return pool is reflected as a discount to the gross replenishment recommendation, reducing over-ordering during periods of elevated return activity.
Operators can exclude specific SKUs from the returns pool if condition inspection is required before re-sell.
Stockorlo is deliberately narrow. It does one job well for a specific type of e-commerce business.
Mid-market e-commerce operators running 500 to 15,000 active SKUs with $5M to $80M in annual GMV and one to four fulfillment locations. Typically on Shopify as the storefront with NetSuite or Cin7 as the ERP. Managing seasonal demand across multiple product categories. Buying team of one to four people currently spending significant time each week on manual replenishment decisions.
You are probably a good fit if you have experienced a stockout on a high-velocity SKU during peak season while simultaneously carrying excess inventory on slower movers, and you know the cause was the manual process rather than the underlying data.
Single-SKU DTC brands with simple reorder logic do not need a forecasting layer — a basic reorder point and safety stock formula handled in Shopify is sufficient. Enterprise retailers with dedicated supply chain planning teams already running Blue Yonder, o9 Solutions, or similar platforms have more infrastructure than Stockorlo replaces. Stockorlo is also not the right tool for businesses selling exclusively through wholesale or B2B channels where demand is driven by a small number of accounts rather than consumer velocity signals.
Stockorlo connects to your existing storefront, ERP, fulfillment, and supplier systems. No migration required.
Request a demo and we will walk you through a live example using data similar to your current SKU mix and channel setup. No commitment required.