Supply Chain & Operations
Inventory, 3PL, Shipping, Returns
Build Reactive Systems. Don't Stack Assumptions.
Early-stage forecasting shouldn't pile assumptions on assumptions. You're growing, things change fast. The direction is to build a supply chain that reacts to marketplace demand. Your digital marketing is a major lever on demand, and you don't want the supply chain acting as a handbrake. Project current run rates forward. Keep lead times shorter than projected sell-through. This is how we scaled Quad Lock while bootstrapped: keeping popular products in stock without pulling back on acquisition channels.
Key topics covered
- Inventory Planning & Demand ForecastingInventory is where DTC brands go to die. Not because they can't sell, but because they either run out (lost revenue) or order too much (dead cash, markdowns).
- 3PL (Third-Party Logistics) Selection & ManagementStrong supplier relationships get your product made. The next question is how you get it to the customer.
- Shipping StrategyShipping is simultaneously a cost centre and a conversion lever.
- Returns & Exchanges PolicyReturns are the tax for selling online. The goal: reduce unnecessary ones and make necessary ones painless.
Inventory Planning & Demand Forecasting
Inventory is where DTC brands go to die. Not because they can't sell, but because they either run out (lost revenue) or order too much (dead cash, markdowns). The brands that win get this boring bit right.
Early-stage forecasting is mostly educated guessing. You don't have years of data or stable demand curves.
The hardest supply chain challenge when bootstrapping is cash. When marketing is working and demand accelerates, the instinct is to order big. But you're funding inventory from revenue. Avoid letting popular SKUs go out of stock, even if that means paying slightly more per unit for smaller, more frequent orders, or using air freight.
What works at each stage:
Launch ($0-$1M): Forecast from marketing spend and conversion assumptions. Monthly ad spend / target CAC = new customers × units per order = monthly demand. Add a buffer, often around 20-30% for early-stage brands, depending on lead times and stock-out risk. Plan in 30-60 day cycles. Don't overthink it - at this stage, being roughly right and reacting fast beats a sophisticated model.
Growth ($1-$10M): Use trailing 3-4 month sales velocity as your baseline. Apply seasonal patterns from your own data. Layer in planned marketing pushes and launches. Tools: Excel or Google Sheets. The key shift here is moving from gut feel to data-driven ordering.
Scale ($10-$50M): Multi-channel demand signal. Integrate DTC, wholesale, and marketplace data into a single forecast. Dedicated ops hire owns this. Tools: Middle market ERPs and, of course, Excel and Google Sheets.
Established ($50-$100M): Demand planning team with formal S&OP process. Supplier diversification across factories and potentially geographies. An ERP is now non-negotiable.
Enterprise ($100M+): Full sales and operations planning. Global inventory optimisation across multiple 3PLs and markets. Predictive analytics and AI-assisted demand sensing. The complexity here is managing dozens of SKUs across dozens of markets, with potentially dozens of retail customers in each.
Keep reading in the full playbook.
All 30 sections, the diagnostic Health Check, 400+ checklist items, and 8 tools. Free and always will be.
Open the full playbookWhat you'll walk away with
- Demand forecast built on run-rate data (not assumptions stacked on assumptions)
- Safety stock formula applied to hero SKUs
- Reorder points set and monitored for all active SKUs
- 3PL evaluated or self-fulfilment systems documented
- Shipping strategy defined (free threshold, carrier mix, flat-rate option)
- Multi-carrier setup with rate-shopping platform
- Document a cycle count process with owners, frequency, and variance thresholds.