Automate Inventory Management and Eliminate Stockouts

Many small businesses run inventory from a combination of their e-commerce platform's built-in tools and a complex Google Sheet. Tools like Shopify Inventory or Katana track current stock levels perfectly but offer only rudimentary forecasting. Their logic is based on a simple trailing sales average, which cannot account for seasonality, upcoming promotions, or sudden demand shifts.

Consider a 10-person e-commerce business selling 50 SKUs of coffee beans. The owner spends 5 hours every Monday in a spreadsheet, trying to guess what to order. When they run a promotion in October, sales triple. The spreadsheet, based on a 4-week moving average, cannot react fast enough. They stock out two weeks before the promotion ends, losing an estimated 300 sales and disappointing customers.

In January, the opposite happens. After a holiday gift-set promotion, demand plummets. But the manual forecast does not adjust down quickly enough, and they over-order. This ties up $5,000 in cash in slow-moving inventory that could have been used for marketing or new product development. Basic reorder point tools in QuickBooks Commerce or Zoho Inventory face the same issue; a static reorder point of '20 units' is blind to demand velocity.

The structural problem is that off-the-shelf tools are designed for inventory tracking, not dynamic demand forecasting. Their data models are rigid and cannot learn the unique demand patterns of your specific products. They see what you have, but cannot accurately predict what you will need. This requires a custom model trained on your business's unique sales history.

The first step is a data audit. Syntora would connect to your sales platforms like Shopify or Amazon and pull at least 12 months of sales data per SKU. This audit identifies data quality gaps, seasonality, and trends. You receive a report that outlines which SKUs have enough data for a reliable forecast and a concrete plan for the build.

The technical approach uses a time-series forecasting model, built in Python with libraries like LightGBM, to predict demand for each SKU. This model runs on a nightly schedule on AWS Lambda, keeping costs under $50 per month. The forecast outputs update dynamic reorder points and safety stock levels stored in a Supabase database. A FastAPI service then exposes this data to your existing tools or a simple dashboard.

The delivered system integrates directly into your current workflow. When a product's inventory level drops below its new, dynamically calculated reorder point, the system can draft a purchase order and send a Slack notification for approval. You receive the full source code, a dashboard to monitor forecast accuracy, and a runbook detailing how to manage the system. The entire process is automated, turning 5 hours of weekly guesswork into a 5-minute daily review.