The $800 billion figure gets cited often enough in logistics conferences that it has started to sound routine. It should not. That number, drawn from industry research on inventory distortion, represents a genuine drain that shows up in write-downs, expedite fees, stockout losses, and bloated safety stock sitting in warehouses eating margin. Most companies experiencing it are not aware how much of it traces back to a single root cause: freight data that arrives too late to act on.
Inventory distortion has two faces. Overstock happens when teams carry too much of the wrong product because they cannot trust inbound ETAs. Stockouts happen when replenishment orders are placed too late because the current stock position was masked by in-transit inventory that nobody could accurately account for. Both failures are expensive. Both are largely preventable.
What Bad Freight Data Looks Like in Practice
The issue rarely announces itself as a data problem. It shows up as a planning problem, or a procurement problem, or a warehouse space problem. A retailer sees a spike in their markdown rate and attributes it to a bad buying decision. A manufacturer posts a miss on fill rate and blames the carrier. The real culprit - inaccurate in-transit inventory data fed into the demand planning system - stays invisible.
Consider what happens when an ETA shifts by five days and your planning system does not catch the update in time. If your replenishment trigger was set to fire based on the original arrival date, you might delay a new purchase order. That delay compounds if the next inbound shipment also has an ETA shift. By the time the first shipment actually arrives, you have a gap in your inventory position that takes two to three weeks to recover. Multiply that across dozens of SKUs and multiple lanes, and you have the beginnings of a systematic inventory accuracy problem.
The Data Freshness Problem
Most companies get freight data once a day. The EDI 214 comes in overnight, the WMS pulls it in the morning, and the planners are looking at a snapshot that is already 12 to 18 hours old by the time they start their workday. For domestic truckload that is tolerable, though still imperfect. For ocean shipments with a 25-day transit, where a single day's shift in port arrival can cascade into a week of buffer stock adjustment, it is not nearly good enough.
We pulled ETA accuracy data from 420,000 ocean shipments that moved through our platform in 2025. The average ETA at the time of vessel departure was off by 4.2 days from actual port arrival. That sounds manageable until you realize that in a typical replenishment cycle, a 4-day ETA error shifts your safety stock calculation enough to trigger either an emergency reorder or a delayed replenishment, depending on which direction the error runs. Either outcome costs money.
How Overstock and Stockout Costs Add Up
Let's put concrete numbers on this. A mid-sized consumer goods importer with $400 million in annual revenue and a 40-day average ocean transit maintains roughly $65 million in in-transit inventory at any given time. If 15% of those shipments have ETA errors exceeding five days, and each error drives an average of $80,000 in downstream cost (expedite freight, emergency reorders, or markdowns on goods that arrived too early and displaced planned inventory), the annual cost is $7.8 million. That is not a rounding error. That is a capital efficiency problem with a clear data solution.
Overstock is actually the more expensive side of the equation for most shippers. Carrying costs - warehouse space, handling, insurance, opportunity cost of tied-up capital - typically run 20 to 30 cents on the dollar annually. If poor visibility causes a company to carry an extra three weeks of buffer stock across their product line, the carrying cost on that unnecessary inventory compounds quarter over quarter into a significant drag on their operating margins.
The Demand Planning Dependency
Demand planning teams have become increasingly sophisticated at modeling customer behavior, seasonality, and promotional lift. Most of that sophistication is wasted if the input data about in-transit inventory is unreliable. A planning model that feeds off accurate, real-time freight position data makes fundamentally better decisions than the same model running on daily batch updates with a high error rate.
This is not theoretical. One of our enterprise retail clients ran a controlled comparison over two quarters. One segment of their business used their existing TMS for in-transit data. Another segment was connected to our platform with real-time ETA updates. The planning team used identical models for both. At the end of six months, the segment with real-time visibility had 22% less excess inventory and a 9% better in-stock rate. The only variable was data quality and timeliness.
Where to Start
The companies making meaningful progress on inventory distortion typically start with their highest-volume ocean lanes. That is where ETA variability is highest and where improving data quality has the biggest leverage. Get accurate, frequently-updated position data on your top 20 suppliers by volume, and you will cover the majority of your in-transit dollar exposure.
From there, the work becomes iterative: connect your freight data to your planning system in a way that updates ETAs automatically, set exception alerts for shipments that shift outside their planned arrival window, and build a feedback loop where actual arrival data informs your planning system's safety stock parameters going forward.
The $800 billion problem is large because it is diffuse. But at the individual company level, the fix is specific. Better freight data, integrated closer to where planning decisions are made, earlier in the cycle. That is a solvable problem.