3PL Inventory Forecasting

When Inventory Stops Behaving and Starts Taking Over Your Week

Most brands learn the hard way that inventory does not care about your feelings. It runs out when you need it, piles up when you do not, and finds the most inconvenient times to reveal hidden mismatches. As the catalog grows, inventory forecasting becomes less about intuition and more about data, timing, and tight operational discipline. That is when 3PL inventory forecasting becomes a serious topic. The right 3PL helps you avoid stockouts, sidestep costly overstocks, and stay ahead of demand instead of chasing it.

Forecasting is not guesswork. It is a system built from reliable historical data, clean inventory movements, and real visibility into sales velocity. Without those ingredients, brands end up buying too much, buying too late, or buying without knowing what problem they are solving. A 3PL with the right tools helps you build accuracy from the inside out.

Why Forecasting Fails Without Clean Data

Forecasting fails for predictable reasons. If receiving is loose, counts drift. If picks are unscanned, stock levels lie. If returns are not processed correctly, availability gets distorted. Inaccurate data corrupts every model built on top of it. Maureen Milligan, Director of Operations and Projects at G10, points out that many incoming brands struggle with the same root problems. She explains that "most of the customers who come to us from another 3PL, their challenges have always been access to their data, order accuracy and efficiency, and meeting the committed requirements." Without accurate data, even the best forecast is fiction.

Scanning as the Non Negotiable Foundation

Every solid forecast begins with truth. That truth comes from scanning. Connor Perkins, Director of Fulfillment at G10, puts it simply: "you want everything to be scanned in the warehouse, nothing done on paper." Scanning captures accurate movement at receiving, picking, packing, replenishment, and returns. That produces the clean signals a forecasting engine needs. When the system knows exactly what moved and when, you can trust the historical patterns.

Understanding SKU Velocity and Seasonal Patterns

Not all SKUs behave the same. Some pulse with major promotional spikes. Others move steadily all year. Some disappear during summer. Others surge only during gifting season. A mature forecasting system recognizes these cycles and adjusts future expectations accordingly. G10’s ChannelPoint WMS tracks SKU velocity at a granular level so planners know which items are predictable and which need closer attention.

When SKU velocity is mapped against promotional calendars and marketplace trends, forecasting becomes sharper. The brand stops treating every SKU the same and starts aligning purchasing strategies with real demand curves.

Multi Node Networks Multiply Forecasting Complexity

Once inventory spreads across multiple warehouses, forecasting complexity jumps. Each node needs the right quantity of the right SKUs at the right time. Too much stock in one location creates waste. Too little in another forces long distance transfers or delayed orders. A strong 3PL forecasting model treats nodes as parts of one system instead of isolated islands.

G10 operates facilities in South Carolina, Wisconsin, Nevada, Arizona, and Texas, which allows inventory to stage closer to customers. That geographic spread only works if forecasting accounts for regional demand. The system must know which SKUs are popular on the West Coast versus the Southeast. ChannelPoint feeds these signals back into planning so the network stays balanced.

Forecasting for Marketplaces Requires More Precision

Marketplaces like Amazon, Walmart, and TikTok Shop punish stockouts aggressively. A single bad run of low inventory can push your listings down, lower conversion, and force you to spend more on ads to regain placement. That means forecasting must be precise. You cannot afford sloppy data or unclear replenishment schedules.

Joel Malmquist, VP of Customer Experience at G10, explains the importance of unified flows. He says that Shopify orders move directly into G10 while B2B retail shipments flow into "places like Target and Walmart." Forecasting has to tie both worlds together. You cannot overbuy for DTC and starve your retail channels. A unified 3PL forecasting system prevents those conflicts.

Using Returns Data to Improve Forecast Accuracy

Returns reveal demand you misread. Maybe customers disliked a variant. Maybe sizing was off. Maybe the SKU was oversold during a promotion. Whatever the reason, returns should be fed back into forecasting models as corrective signals. When combined with return reasons, refund timelines, and product disposition, you get a more accurate picture of true demand.

Joel describes how the G10 team categorizes returns: "it looks good, we are going to restock this, or it looks damaged, we are going to either dispose of it or put it in a quarantine area." That discipline matters for forecasting because restocked items re enter the availability pool. Without that clarity, your system buys inventory you already have.

Forecasting Protects Cash Flow

Overbuying ties up cash. Underbuying destroys revenue. Smart forecasting balances both by aligning purchasing with reality. With a 3PL providing clean operational data, brands can confidently place POs, negotiate with suppliers, and prepare for peak season without panic. The biggest win is predictability. Inventory stops swinging wildly between feast and famine.

Connor notes that G10 clients "can see their daily orders, they can see KPIs, and they can see historical transactions." That visibility ensures your forecast is not blind. You always know where demand is heading before it becomes a crisis.

Aligning Purchasing With Operational Capabilities

Forecasting is not just about quantities. It is about timing. Purchase orders must land when the warehouse has capacity to receive them without causing congestion. A 3PL with strong forecasting will advise on inbound scheduling, dock availability, and staging needs. That prevents the warehouse from being overwhelmed by suppliers delivering too much at the wrong time.

G10’s forecasting approach looks beyond SKU counts. It considers receiving labor, storage availability, pick capacity, peak season prep, and lead times. The result is a plan that works operationally instead of just mathematically.

Peak Season Forecasting and Pre Build Strategies

Peak season exposes the weaknesses in any forecasting model. A small error in July becomes a major error in November. To avoid this, G10 uses disciplined forecasting paired with pre build strategies. Holly Woods, Director of Operations at G10, explains: "we start planning peak times months ahead of time. We run forecast models, staffing models, and we audit inventory."

This preparation ensures that high velocity SKUs are stocked early, slow movers are deprioritized, and kitting or bundling work begins long before the rush hits. Forecasting becomes an ongoing conversation instead of a quarterly scramble.

Forecasting as a Growth Multiplier

Forecasting is ultimately about confidence. When you know how much inventory you need and when you need it, you scale with intention. You launch new products with fewer surprises. You negotiate better supplier terms. You avoid panic buying. You enter new channels without starving existing ones.

Mark Becker, CEO and founder of G10, sums up this direction clearly. He says that "we are going to grow with them." Forecasting is one of the ways growth becomes manageable instead of risky. When your 3PL gives you not just space but insight, inventory becomes a strategic tool, not a financial threat. If forecasting currently feels like educated guessing, it may be time to work with a 3PL that gives you the data discipline needed to plan like a mature operation.