I have audited hundreds of DTC brands in the last few years. The ones that struggle to scale past a certain revenue point almost always have the same problem buried in their unit economics: their inventory is working against them. Either they are constantly running out of stock at the worst possible moment, or they have cash tied up in product that is not moving. Usually both, at the same time, on different SKUs.
Inventory forecasting is treated as an operational problem. It is actually a cash flow problem. When you get it wrong, the cost does not show up on your Shopify dashboard. It shows up in your bank account at the end of the month, and in your inability to scale ad spend when you have a winning creative.
What stockouts actually cost you
The obvious cost of a stockout is the sale you did not make. The less obvious cost is everything that happens downstream. When a product page shows out of stock, 70 to 80% of shoppers buy from a competitor rather than waiting. That is not a delayed sale. It is a lost customer, and in most DTC categories, a lost customer who never comes back.
The compounding damage is what makes stockouts genuinely dangerous. A stockout during an active Meta or Google campaign is one of the most expensive combinations in ecommerce. You are buying traffic that cannot convert. Your cost per purchase spikes because only in-stock units can generate purchases. Your ROAS collapses. And the algorithm is trained on a period of degraded performance that it carries forward even after you restock.
Brands that run out of stock mid-campaign typically see 30 to 60% higher cost per acquisition for two to four weeks after restocking, while the algorithm relearns the landscape. You do not pay for the stockout once. You pay for it every day until the campaign recovers its rhythm.
What overstock actually costs you
Overstock feels safer than stockouts. It is not. For a brand carrying an average of £200,000 in inventory, a 25% overstock position ties up £50,000 in working capital that cannot be used for anything else. No ad spend. No new product development. No cash buffer for the inevitable dip. That capital is sitting in a warehouse, costing you 20 to 30% annually in storage, insurance, handling, and opportunity cost on top of the product value itself.
Overstock also corrupts your reporting. When a slow-moving SKU is costing you 3PL storage fees every month, it inflates your carrying costs and suppresses your true margin on the products that are actually performing. The brands I see with the cleanest unit economics are ruthless about their inventory turns. They treat unsold stock as a liability from day one, not something to hold onto in case demand picks up.
The five forecasting mistakes DTC brands make
Most DTC brands do not have a forecasting problem. They have a method problem. The approach they are using is structurally incapable of producing accurate forecasts, regardless of how much effort goes into it.
Using a 30-day sales average as the forecast
A 30-day average treats every day as equal. It cannot see the weekly rhythm of your sales (most DTC brands sell 40 to 60% more on weekends), seasonal peaks, the post-promotion drop-off, or the demand spike from a viral post. It also ignores supplier lead times entirely, which means the reorder point it implies assumes stock arrives instantly. The result is chronic understocking during peak periods and chronic overstocking during quiet ones.
Not building lead time into the reorder trigger
Brands reorder when they are about to run out, not when they should run out given their supplier's lead time. If your supplier takes 45 days, you need to trigger a reorder when you have 60 to 75 days of stock remaining, not when you have 10 days. This sounds obvious. I have audited brands doing £3 million a year that still reorder reactively. Their ops team is always in firefighting mode because the process itself creates emergencies.
Treating all SKUs the same
Your hero SKU that drives 60% of revenue needs a different forecasting approach from a new SKU with three months of data. Your seasonal SKU needs a different approach from your evergreen staple. Most brands apply the same method across their entire catalogue, which means their forecasting is accurate for some products by coincidence and wrong for most of them by design.
Ignoring the marketing calendar
Inventory forecasts built in isolation from the marketing calendar will always be wrong. A planned email campaign to 80,000 subscribers will spike demand. A Meta campaign scaling from £500 to £3,000 per day will change your sell-through rate significantly. A TikTok creator posting about your product can move three months of inventory in 48 hours. If your forecasting model does not talk to your marketing plan, it is forecasting a version of your business that does not exist.
Not reviewing forecast accuracy
Most brands set a reorder point once and never revisit it. Demand changes. Seasonality shifts. You launch new products that cannibalise existing ones. Your supplier lead times vary. A forecast that was accurate six months ago may now be systematically wrong. The brands with the best inventory management review their forecast accuracy monthly: actual sales vs. predicted sales by SKU. The gap is where the next optimisation lives.
How to build a forecasting system that actually works
You do not need a six-figure forecasting tool to get this right. You need a method, the discipline to follow it, and the tools to support it at your current scale.
Calculate your true reorder point per SKU
Reorder point = (average daily sales x supplier lead time in days) + safety stock. Safety stock = (maximum daily sales minus average daily sales) x maximum lead time in days. Do this calculation for every active SKU and put it in a spreadsheet or your inventory tool. This single step eliminates reactive reordering.
Segment your SKUs by behaviour
Group your catalogue into four categories: high volume and predictable (your heroes), high volume and volatile (trend-driven or seasonal), low volume and predictable (steadies), and low volume and volatile (experimental). Apply a different forecasting method to each group. Heroes get the most sophisticated forecasting. Experimental SKUs get tight minimum orders until you have enough data.
Connect inventory to the marketing calendar
Every planned campaign should trigger an inventory check. Before you brief creative, set a campaign live date, or book a creator, someone on your team should be asking: do we have enough stock to support this campaign at peak demand, including a safety buffer? Build this into your campaign planning process as a hard gate, not an afterthought.
Review forecast accuracy monthly
At the end of each month, compare your forecast to actuals by SKU. Calculate your forecast accuracy percentage: (1 minus the absolute percentage difference between forecast and actual) x 100. Any SKU below 70% accuracy needs a different forecasting approach. Most mature brands target 85% or above on their top 20% of SKUs by revenue.
Choose the right tool for your current scale
Under £500k revenue: a structured Google Sheet with proper reorder point calculations and lead time buffers is sufficient. £500k to £2M: Shopify's native inventory reporting combined with a purpose-built forecasting tool like Inventory Planner by Sage. Above £2M, especially with multiple warehouses or sales channels: a dedicated solution like Flieber, Prediko, or Redo that handles multi-location inventory and marketing-aware forecasting.
What good inventory management looks like in practice
I worked with a supplement brand doing £180,000 a month in revenue. Their ops team was running on reactive reorders, and they had two SKUs that were chronically out of stock during their peak weeks while four other SKUs were carrying 90-plus days of excess inventory. Their cash flow problem was actually an inventory allocation problem.
We calculated proper reorder points for each SKU, connected the inventory model to their campaign calendar, and cut safety stock on the slow-moving SKUs while increasing it on the heroes. Within 60 days, they freed £34,000 in working capital from overstock, reduced their out-of-stock incidents by 70%, and ran their first Meta campaign without a stockout interruption.
The revenue impact was not from a new channel or a better creative. It was from removing the friction that was preventing their existing channels from performing. That is what good inventory management delivers: your growth levers can finally work the way they are supposed to.
Inside the system
How we build this for brands
When we take a brand on at Purposeful Profits, inventory is one of the first things we look at before we touch ad spend or email. Cash tied up in overstock cannot fund growth. Campaigns running into stockouts will never produce accurate performance data. The inventory layer has to be right before the growth levers can work.
We build a profit and cash-flow dashboard from live Shopify and ad data, with a reporting agent that surfaces inventory risk alongside revenue and margin data weekly. This connects directly to the campaign planning process: every campaign brief is cross-checked against current stock levels and projected sell-through before it goes to creative production. Part of this runs live for portfolio brands today; the full system is what we deploy when we take a brand on.
Growth Audit
Find Out What Your Inventory Is Costing You
I will review your inventory position, identify the cash tied up in overstock, and show you exactly where stockouts are suppressing your growth. Direct numbers, clear fixes, no fluff.
Book Your AuditFrequently asked questions
How do I calculate reorder points for my Shopify store?
Reorder point = (average daily sales x supplier lead time in days) + safety stock. Safety stock = (maximum daily sales minus average daily sales) x maximum lead time in days. For example: if you sell 50 units per day on average, your supplier takes 14 days to deliver, and your peak daily sales are 80 units with a maximum lead time of 18 days, your reorder point is (50 x 14) + (80 - 50) x 18 = 700 + 540 = 1,240 units. Trigger a reorder when stock drops to that level.
What is the best inventory forecasting tool for Shopify DTC brands?
For most mid-size DTC brands, Inventory Planner by Sage is the most comprehensive option. Prediko works well for brands that want marketing-aware forecasting incorporating ad spend and campaign calendars. Flieber suits fast-growing brands managing multiple warehouses and channels. For early-stage brands, a structured spreadsheet with proper reorder point calculations is often sufficient before investing in a paid tool.
How much cash does overstock tie up for a typical DTC brand?
For a brand carrying an average of £200,000 in inventory, a 25% reduction in overstock frees £50,000 in working capital. Beyond the cash tied up in the product, excess inventory adds storage costs of 20-30% annually when you factor in 3PL fees, insurance, handling, and opportunity cost. Most brands I audit are carrying 30-45 days more stock than they need on their slow-moving SKUs.
What happens to my Meta ads when I have a stockout?
A stockout during an active campaign means you are buying traffic that cannot convert. Your cost per purchase spikes, ROAS collapses, and the algorithm is trained on degraded performance it carries forward post-restock. Brands typically see 30-60% higher CPAs for two to four weeks after restocking. The fix is to pause campaigns when stock drops below a safe threshold, not after it hits zero.
How does a 30-day sales average fail as an inventory forecasting method?
A 30-day average treats every day as equal. It misses weekly sales rhythms (most DTC brands sell 40-60% more on weekends), seasonal peaks, post-promotion drops, and demand spikes from viral content. It also ignores supplier lead times, meaning the reorder point it implies assumes instant stock arrival. The result is chronic understocking during peak periods and overstocking during quiet ones.
About the author
Caner Veli built Liquiproof from zero to 3,000+ global retailers in under 6 years, then exited profitably. He now helps DTC and CPG brands fix broken growth engines. In the last 90 days, he 10x'd monthly revenue in his own business.