Inventory Intelligence for Multi-Channel E-Commerce
The Problem
Sellers who work more than one channel lose thousands a month by running out, or by buying too much. They keep track of Amazon FBA, their Shopify shops, and more than one store room in a sheet that is out of date before they finish typing it. The tools out there cost too much for a growing seller, or handle one channel only, or hand you a screen full of numbers and no answer.
The core challenge is a forecasting problem. Every SKU has a different demand pattern. A best-seller with smooth, predictable sales needs a different forecasting approach than a seasonal product with intermittent spikes. Generic tools apply one model to every SKU and get it wrong. Sellers need intelligent, per-SKU demand forecasting that adapts to their actual sales data and turns those forecasts into purchase decisions they can act on immediately.
Our Solution: Multi-Model Demand Forecasting
ReplenishRadar works out how each product sells, picks the math that suits it, and turns that into a draft order and a warning before you run out.
Our Demand Intelligence Engine is sits at the heart of it. It reads how each product has sold and puts it in one of four groups: steady, jumpy, now and then, or lumpy. Then it picks the math that suits that group. We try several proven methods, allow for the time of year, and test each one against sales it was never shown.
It does not stop at the forecast. It turns that into things you can act on. Draft orders, timed to the days your suppliers take them. FBA top-ups that allow for what is on the way and how long Amazon takes to book it in. Warnings early enough to be worth having. And a screen that puts a number on what running out, or over-buying, has cost you.
We are building toward a closed-loop system where the sizing corrects itself from outcome data. Did the suggestion prevent a stockout? Did it cause overstock? Today that loop is not closed. We measure the outcomes, warned coverage, in-stock rate and days of cover, and we read them ourselves. Feeding them back into the next cycle automatically is ahead of us, not behind us.
How It Works
1. Demand Pattern Classification
Each SKU's sales history is analyzed to classify its demand pattern (smooth, erratic, intermittent, lumpy) using coefficient of variation and average demand interval metrics. This determines which forecasting model will perform best.
2. Multi-Model Forecasting
We try several proven methods on each product, allowing for the time of year. Then we test each one against sales it was never shown, and keep the one that called them best.
3. Cadence-Aware Ordering
Forecasts are translated into purchase order suggestions that respect your actual ordering rhythms. Monthly POs from overseas suppliers and weekly FBA transfers are treated as separate decision points with different lead times and safety stock buffers.
4. Profit Intelligence
We put a number on what your stock choices cost. The sales you lost by running out. What it costs to hold what you over-bought. And what you would save by ordering the way we suggest.
Where We Are Today
ReplenishRadar is live. Shopify and Amazon integrations, the demand forecasting engine, cadence-aware purchase order generation, and the FBA replenishment workflow are all in production today.
Sellers connect their stores, see all their stock in one place, get a forecast for every product, and draft orders that land on the days their suppliers take them. All from one screen.
Why We Built This
ReplenishRadar was started by Scot. He sells on Amazon FBA and Shopify, and came to this after ten years building cloud and data systems. It exists because the tools for sellers like him cost thousands a month, needed a team to set up, or only ever handled one channel.
So we built what we needed. A forecast for each product that moves with the way it really sells. Orders that land on the days your suppliers actually take them. FBA top-ups that allow for what is already on the way and how long Amazon takes to book it in. And one view across Amazon and Shopify. We built it to the same bar we hold cloud systems to.
How We Use ReplenishRadar in Our Own Inventory Operations
Before we opened ReplenishRadar to sellers, we ran it against our own multi-channel inventory operation for several months. Here is what changed.
3+
Sales channels unified
4 hrs to 20 min
Weekly inventory review
Every SKU
Carries its own order-by date, not just a stock level
Before
Inventory across Amazon FBA, a Shopify storefront, and a warehouse, tracked in spreadsheets that were outdated as soon as they were saved. Reorder timing was a gut-feel decision made under pressure when stock got low.
With ReplenishRadar
The forecasting engine classified each SKU's sales pattern and selected the best-fit model automatically. Cadence-aware alerts triggered at the right point in the ordering cycle based on forecast-driven reorder points, not arbitrary low-stock thresholds. FBA transfer suggestions factored in current FBA inventory, inbound shipments, Amazon receiving delays, and predicted sell-through rate.
Result
Weekly inventory review dropped from 4+ hours of spreadsheet reconciliation to a 20-minute dashboard check. Supplier purchase orders are generated automatically based on forecasted demand. FBA replenishment timing is driven by the forecast, the inbound pipeline and Amazon's receiving lag instead of gut feel.
Technology & Security
Secure Integrations
- Amazon SP-API (Official Selling Partner)
- Shopify OAuth
- Stripe for billing
Data Protection
- Encrypted at rest and in transit
- Row-level security on all tenant data
- Role-based access controls
Infrastructure & Compliance
- Hosted on Google Cloud Platform, whose infrastructure is SOC 2 Type II and ISO 27001 certified (Google's certification, not ours: see our security page)
- Container image signing and Binary Authorization for production deployments
- Secrets managed via Google Secret Manager and Cloud KMS
- Registered Amazon Selling Partner API developer
- Compliant with all Amazon API usage policies and data protection requirements
See what AI-driven inventory intelligence looks like
Doing $5M+ in revenue? Talk to our team