In this article, learn about:
Why inventory distortion keeps costing retailers billions even as forecasting tools get more sophisticated
How a single late ASN cascades into a corrupted demand forecast, step by step
What Target Canada's collapse and Nike's i2 rollout reveal about fixing the wrong layer of the problem
What fixing supplier execution data returns, verified in Forrester's Total Economic Impact study of the SPS Commerce Supply Chain Performance Suite
What's Actually Causing Poor Forecast Accuracy in Retail?
The instinct is to blame the model. A retailer's forecast misses, and the response is almost always the same: Buy a better algorithm, hire another data scientist, add more external signals. Meanwhile the miss rate barely moves.
Here's the harder truth. Inventory distortion, the combined cost of stockouts and overstocks, cost the global retail industry $1.73 trillion in 2025, according to IHL Group's annual research. Supply chain disruption was the single largest contributor, responsible for $301 billion of that figure on its own. This is a structural problem, and it sits upstream of where most retailers are looking.
The forecast is only as good as what feeds it. If a supplier confirms a purchase order (PO) late, ships early or short, or sends an inaccurate advance shipping notice (ASN), the retailer's system of record is wrong before the forecast model ever touches the data. No amount of algorithmic sophistication fixes a training set that was corrupted at the source.
How Does One Late ASN Turn Into a Bad Forecast?
Walk the chain one step at a time.
A supplier ships an order without sending an accurate ASN, or sends one with the wrong item count. The receiving team at the distribution center can't match what physically arrived against what the system expected, so the discrepancy sits unresolved. That unresolved discrepancy distorts the perpetual inventory count. Now the system thinks it has more (or less) of an item than it actually does.
From there, two things happen, and neither is good. Either the system shows a phantom in-stock position while the shelf is empty, or it triggers unnecessary safety stock and expedited freight to cover a shortage that doesn't exist. Either way, the demand signal for that SKU is now wrong. And that wrong signal becomes training data for next month's forecast.
Multiply that across a supplier base of any real size. Every late confirmation, every receiving discrepancy, every unconfirmed shipment stacks on the last. By the time next month's forecast runs, it was never working with real numbers to begin with.
The Target Canada Warning
Target Canada's 2015 collapse is the most visible example of this failure mode playing out at scale, even if the technology has moved on since. Target Canada launched with a new inventory system that the internal teams didn't fully understand, and no historical sales data to check it against. Product data accuracy across roughly 75,000 items was compromised from the start, with dimensions, units, and currencies entered inconsistently by newly hired staff working against an unrealistic timeline.
The forecasting system had nothing reliable to learn from. Distribution centers overflowed with product that had nowhere to go, while store shelves sat empty. Reporting on the collapse later found that some inventory analysts had even disabled auto-replenishment alerts to keep their in-stock percentages looking acceptable on paper, which only masked the problem further. The forecast wasn't the failure. The data feeding it was broken long before any model ran.
Doesn't AI-Driven Forecasting Fix This?
It's a fair question, and the honest answer is that AI-driven forecasting makes the execution-data problem more urgent, not less.
McKinsey's research on demand-sensing and machine learning forecasting found that companies using these methods achieved roughly 90% forecast accuracy with a three-month lag, compared with around 60% for manual forecasting. That's a real gain. But it depends entirely on feeding the model clean, connected external data, things like point-of-sale signals and confirmed stock-out events. A supplier data vacuum starves that exact input. An AI model inherits whatever corruption is already in the pipeline, and it inherits it faster and at greater scale than a human planner would have.
Nike's experience with its i2 demand-planning system in 2000 makes the same point from a different angle. The $400 million implementation produced forecasts so far off that Nike overbought roughly $90 million of slow-selling shoes while shorting popular models by $80 million to $100 million. Part of the failure was that the system couldn't communicate with Nike's existing data sources, and much of the underlying data was still entered by hand. A sophisticated model, fed on broken inputs, still produces broken output. That lesson has aged well into the AI era.
What Does Fixing Execution at the Source Actually Return?
This is where the case stops being theoretical. Forrester Consulting's Total Economic Impact (TEI) study of the SPS Commerce Supply Chain Performance Suite, commissioned by SPS Commerce and published in January 2026, interviewed 10 decision-makers across seven retail, grocery, and distribution organizations. For a composite organization with $3.5 billion in annual revenue and more than 1,000 vendors, Forrester found:
A 5% reduction in stockouts by year three, worth $15 million in incremental revenue over three years
A 40% productivity improvement for purchasing managers managing purchase orders
A 42% reduction in shipment receiving effort
A 30% reduction in invoices requiring manual reconciliation
An overall 360% return on investment (ROI) with payback in under six months
The stockout number gets the attention, but the mechanism behind it is what makes it repeatable. Interviewees described gaining earlier visibility into order fulfillment issues, using vendor performance data to hold suppliers accountable for on-time, in-full (OTIF) delivery, and catching inventory gaps before they became empty shelves. One interviewee, a senior manager of supplier operations at an industrial distributor, said the improved visibility helped their organization fine-tune ordering patterns and demand planning, pushing their annual OTIF performance across vendors to nearly 85%.
The productivity and receiving numbers aren't side benefits. They're the mechanism. Better PO management and cleaner receiving data are what stop the corruption chain before it reaches the forecast.
Retailers Already Know This, Even Without Calling It a Data Problem
Walmart's OTIF program is the clearest evidence that the industry has already reached this conclusion, even if it rarely gets framed as a forecasting fix. Walmart tightened its OTIF standard to 98% in September 2020, explicitly tying the change to keeping shelves stocked and improving product availability for customers. The requirement proved difficult enough in practice that Walmart recalibrated in 2024 to 90% on-time and 95% in-full for prepaid suppliers.
Whatever the exact threshold, the logic is the same one this article has been building toward: in-stock performance depends on supplier execution discipline, not just on how good the buyer's forecasting tool is. Walmart built an entire compliance program around that premise. Most retailers just haven't connected it back to their own forecast-accuracy complaints yet. Retailers who've formalized this connection, through a collaborative approach to supplier performance management, have seen real revenue gains: Research on OTIF improvements shows that even a five-point gain can generate tens of millions of dollars for a mid-size retailer.
Is Your Supplier Data Trustworthy Enough for Any Forecasting Tool to Work?
That's the real question, and it's a different one than "which forecasting tool should we evaluate next." A new model, however capable, still runs on the same execution data your current one does. If that data is fragmented, late, or unconfirmed, the new tool inherits the old problem.
SPS Commerce doesn't run the forecast. What the SPS network does is align and connect the execution data, POs, ASNs, receiving confirmations, and vendor performance history, that any forecasting system depends on to be trustworthy in the first place. Forrester's independent analysis quantified what fixing that layer is worth: a 5% stockout reduction, $15 million in incremental revenue over three years, and a 360% ROI with payback in under six months. See the full financial model, including the assumptions and sensitivity analysis, and share it with your finance team.
For a closer look at how execution gaps specifically undercut AI-driven buying decisions, see How Can Retail Buyers Use AI to Improve Supply Chain Execution?
Frequently Asked Questions
Why do retail demand forecasts keep missing even after investing in better models?
Usually because the problem was never the model. Late ASNs, unconfirmed POs, and receiving discrepancies corrupt the data before a forecast ever runs, so a smarter algorithm just processes bad inputs faster.
What is the connection between supplier execution and forecast accuracy?
A late or inaccurate ASN distorts receiving data and perpetual inventory counts. Those distortions become the demand history a forecasting model trains on, so accuracy erodes even when the model itself is fine.
Does AI-driven forecasting solve the supplier data problem?
Not on its own. McKinsey found AI forecasting can reach roughly 90% accuracy against 60% for manual methods, but only with clean, connected input data. Feed it a fragmented supplier pipeline and it inherits the same corruption, often faster than a human planner would have caught it.
What financial return comes from fixing supplier execution data?
Forrester's TEI study of the SPS Commerce Supply Chain Performance Suite put a number on it: a composite organization saw a 5% stockout reduction worth $15 million in incremental revenue over three years, and a 360% overall ROI.