The Awkward Middle
There's a particular kind of pain that comes with being a mid-market CPG brand. You're past the scrappy startup phase where the founder personally tracks every order. But you're not big enough to justify the enterprise systems and dedicated planning teams that large companies have.
This awkward middle—roughly $5M to $100M in revenue—is where inventory planning gets genuinely hard. The complexity has outgrown your tools, but the dedicated resources designed for $500M+ companies don't fit your budget or your team.
Here's what makes inventory planning so challenging at this stage and how successful mid-market brands navigate it.
Challenge 1: Multi-Channel Complexity
The Problem
You started DTC. Then you added Amazon. Then a retailer knocked on your door. Now you're selling through four or five channels, each with different demand patterns, fulfillment requirements, and planning horizons.
Retail orders come in quarterly with 60-day lead times. Amazon replenishment happens weekly with 2-week lead times. DTC fluctuates daily based on marketing spend.
Rolling this up into a single demand plan is nightmare-level complexity. Each channel essentially requires its own forecast, but they all draw from the same inventory pool.
What Works
Successful mid-market brands treat each channel as a distinct demand stream while maintaining a unified view of inventory allocation. That means separate forecasts per channel, combined into a total demand picture that drives purchasing decisions.
The key is having systems that can handle this natively rather than trying to force multi-channel complexity into a single spreadsheet model.
Red Flags
If your planning process involves maintaining separate spreadsheets per channel and manually reconciling them weekly, you've outgrown your tools. The reconciliation step is where errors creep in.
Challenge 2: Retail Velocity Uncertainty
The Problem
DTC demand is relatively predictable—your marketing spend and site traffic give you leading indicators. Amazon has its own rhythms you learn over time.
Retail is different. You ship to a distributor or directly to retailer DCs, and then you wait. How fast will it sell through? Will the buyer reorder? When?
The lack of real-time sell-through data makes retail forecasting feel like educated guessing.
What Works
Track door counts and per-store velocity separately from total retail volume. If you're in 500 doors at Target averaging 2 units/store/week, that's your baseline. Changes in door count or velocity changes are your leading indicators.
Build relationships with buyer contacts who can share early signals about reorder timing or promotional plans.
Account for the bullwhip effect: small changes in consumer demand get amplified through the retail supply chain. Pad your safety stock accordingly.
Red Flags
If you're forecasting retail as a single lump number without breaking down distribution and velocity, you're missing the mechanics that drive the business. Total volume isn't actionable—per-door productivity is.
Challenge 3: Lead Time Variability
The Problem
Your domestic supplier quotes 2-week lead time. Your overseas manufacturer says 8 weeks. In practice, both numbers are aspirational.
Lead times vary based on capacity, shipping delays, customs holds, and the general chaos of global supply chains. A shipment that should arrive in 8 weeks sometimes takes 12. Sometimes it takes 6.
That variability makes safety stock calculations difficult. Too little buffer means stockouts when shipments are late. Too much means capital tied up in inventory that arrived earlier than expected.
What Works
Track actual lead times, not quoted lead times. If your supplier says 8 weeks but you've averaged 10 weeks over the past year, plan for 10 weeks.
Consider lead time distribution, not just averages. If shipments typically arrive in 8-12 weeks, your safety stock needs to cover the worst-case scenario, not the average case.
Segment your products by lead time profile. Domestic items need less buffer. International items with high variability need more.
Red Flags
If you're using supplier-quoted lead times in your planning without validating against actual arrival history, you're planning based on fiction.
Challenge 4: The Seasonality Trap
The Problem
Seasonality seems straightforward until you're living it. Your sunscreen sells 5x more in summer. Your protein bars spike before New Year's. Your kids' products surge before back-to-school.
The trap: if you order based on current velocity, you're always behind. By the time you see the seasonal upswing, it's too late to react given your lead times.
The counter-trap: if you build inventory too early based on last year's seasonality, you're tying up cash and warehouse space for months before you need it.
What Works
Model seasonality explicitly. Use prior year patterns to shape your forecast curve, then overlay current trends.
Plan backwards from your peak season. If lead time is 8 weeks and peak season starts June 1, your orders need to be placed by early April at the latest.
Build pre-season inventory strategically. Calculate the carrying cost of early inventory against the stockout risk of ordering late.
Red Flags
If your seasonality "strategy" is hoping you remember to order extra before summer (or Q4, or whenever your peak is), you're setting yourself up for annual fire drills.
Challenge 5: Cash Flow Constraints
The Problem
Mid-market brands rarely have unlimited capital to deploy against inventory. You're balancing inventory investment against marketing spend, team growth, and operational improvements.
Over-ordering to avoid stockouts ties up cash you need elsewhere. Under-ordering to preserve cash risks missing sales that fund everything else.
The question is real: What's the right inventory level that maximizes availability while minimizing cash deployment?
What Works
Think in terms of inventory turns, not absolute inventory levels. High-velocity items should turn faster; they need less relative buffer. Slow-movers need more relative buffer because their demand is lumpier.
Calculate the true cost of a stockout. For your top sellers, a week of stockout might cost more in lost revenue than the carrying cost of an extra month of inventory.
Use days of supply as a planning metric. If your fast-movers have 90 days of supply and your slow-movers have 30 days, something's probably backwards.
Red Flags
If your inventory investment decisions are based on "what we can afford" rather than "what the demand requires," you're letting cash flow constraints drive stockouts.
Challenge 6: Team Scaling
The Problem
At $5M, the founder can hold the entire demand picture in their head. At $25M, that's impossible—there are too many SKUs, too many channels, and too much variability.
But mid-market budgets don't allow for dedicated demand planners, inventory analysts, and procurement specialists. You need one person (maybe two) doing work that large companies staff with teams of ten.
What Works
Invest in systems that amplify your limited team. If software can do the data aggregation, forecast calculation, and exception flagging, your people can focus on decisions and relationships.
Define clear ownership. Even on a small team, someone needs to own the demand plan, someone needs to own inventory targets, and someone needs to own purchasing execution. Those might be the same person, but the responsibilities should be explicit.
Create playbooks for common scenarios. When a retailer requests a promotion, what's the process? When a product stocks out, what's the escalation? Documented processes let limited teams move faster.
Red Flags
If inventory planning happens in the margins—when someone has time between other responsibilities—it's going to get deprioritized until something breaks.
Challenge 7: Data Fragmentation
The Problem
Your data lives everywhere: sales in Shopify, inventory in your 3PL, orders in QuickBooks, forecasts in a spreadsheet, lead times in an email thread with your supplier.
Pulling together a complete picture requires manual aggregation from multiple sources. By the time you've assembled the data, it's already stale.
What Works
Centralize your operational data in a system designed for planning. Modern tools integrate with your tech stack and maintain a single source of truth.
Define your data model clearly. What's a "sale" (gross orders vs. net of returns)? What's "inventory" (on-hand vs. available vs. in-transit)? Ambiguity in definitions creates confusion in planning.
Automate data flows wherever possible. Manual data entry is slow, error-prone, and nobody's idea of a good time.
Red Flags
If preparing for your weekly planning meeting requires someone spending hours pulling and reconciling data, your data infrastructure is a bottleneck.
Moving Forward
The challenges facing mid-market CPG brands aren't going away. If anything, they intensify as you grow. The question is whether your planning capabilities keep pace with your business complexity.
The brands that navigate this stage successfully share a common trait: they invest in systems and processes before they're in crisis. They don't wait until they're stocked out to address their forecasting gaps. They don't wait until they've hired three people to do spreadsheet management to look at better tools.
The transition from "we manage" to "we're in control" doesn't happen automatically. It takes intentional investment in the tools and processes that make complex planning manageable for lean teams.
Key Takeaways
- Mid-market CPG brands face unique challenges: complex enough to need robust planning, too lean to staff large planning teams.
- Multi-channel complexity requires treating each channel as a distinct demand stream while maintaining unified inventory visibility.
- Track actual lead times, not quoted ones. Build safety stock around the worst case, not the average case.
- Model seasonality explicitly and plan backwards from peak season.
- Cash flow constraints are real, but don't let them drive stockout decisions on high-velocity items.
- Invest in systems that amplify limited team capacity.
Frequently Asked Questions
What inventory challenges do growing CPG brands face?
The core challenges include: multi-channel demand complexity, retail velocity uncertainty, lead time variability, seasonal planning, cash flow constraints, limited team capacity, and fragmented data across systems. Each intensifies as brands grow from $5M to $100M in revenue.
When do CPG brands outgrow spreadsheets?
Most brands hit the wall around $10-15M in revenue, when SKU count exceeds 200-300, or when they're selling through three or more channels. The specific trigger varies, but it's usually when the time spent maintaining the spreadsheet exceeds the time spent making decisions.
How do mid-market brands handle inventory without big planning teams?
Successful mid-market brands invest in systems that automate data aggregation and calculations, allowing their limited team to focus on decisions and exceptions. They also document processes clearly so inventory planning doesn't depend on tribal knowledge.
What's the biggest mistake mid-market brands make with inventory?
Reactive rather than proactive planning. Waiting until stockouts happen to address forecasting gaps. Waiting until the team is overwhelmed to invest in better tools. The brands that navigate growth successfully invest ahead of crisis, not in response to it.
How much should mid-market brands invest in inventory planning?
A reasonable benchmark: 0.5-1% of revenue invested in planning systems and processes. For a $20M brand, that's $100,000-$200,000 annually, which could fund dedicated headcount plus modern planning software with room to spare.