The $1.8 Trillion Problem
Retail loses $1.8 trillion annually to 'inventory distortion' — the combination of stockouts (lost sales) and overstock (tied-up capital). Tightly's thesis: this is a forecasting failure, not a logistics failure.
Inventory distortion: two failure modes
Tightly's founding thesis is that retail has a $1.8 trillion problem. It comes in two forms:
Stockouts — you run out of a product while demand still exists. The customer either buys from a competitor or waits. You lose the sale, you lose the customer relationship, and you damage your brand's reliability signal.
Overstock — you bought too much and demand didn't materialize. Capital is tied up in unsold inventory. You're paying for warehouse space to store things that aren't selling. Eventually you markdown and destroy margin.
Both failures have the same root cause: the forecast was wrong. Either you underestimated demand (stockout) or overestimated it (overstock). The question is why forecasts fail — and what an AI-native system can do that spreadsheets and legacy tools can't.
A stone or group one move from capture. Stockouts and overstock are both atari positions — you're one bad forecast away from a serious loss. The PM's job is to keep the product's stones out of atari.
Why spreadsheets fail
Most brands at $1M–$10M GMV run inventory on Excel or Google Sheets. The formula is simple: if current stock / daily sales rate < reorder lead time, buy more.
This breaks in four ways:
Manual lag — updating a spreadsheet takes time. By the time a buyer notices a problem, it's too late to place an order before stockout.
No multi-signal awareness — a spreadsheet can't simultaneously account for a seasonal spike, a promotion next month, a supplier with a 3-week lead time, and a 3PL receiving backlog.
Static lead times — supplier lead times vary. A spreadsheet uses the average. Reality uses the actual current lead time.
No learning — the spreadsheet doesn't get smarter. It uses the same formula this year as last year, regardless of what it got wrong.
Tightly's approach: autonomous execution
Tightly describes itself as moving toward "autonomous execution" rather than "better dashboards." The distinction:
Dashboard tools (most competitors): show you better data, let you make better decisions manually. You still have to process the information and act.
Autonomous execution tools (Tightly's direction): analyze signals, generate recommendations, and execute replenishment with minimal human intervention. The human reviews exceptions, not every SKU.
For a $5M GMV Shopify brand with 500 SKUs across 3 warehouses, reviewing every SKU manually is impossible. Tightly's value prop: the algorithm does the routine work, the buyer handles the edge cases.
CEO Byron Berrisford: "Inventory isn't a spreadsheet problem — it's a sales problem." The framing matters: Tightly is positioned as a revenue enabler, not an ops tool.
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