Every retail and CPG leader I work with this year is shopping for a new forecasting model. They have heard the pitch: transformer architectures, probabilistic ensembles, foundation models for time series. They want the best one. Almost none of them have a model problem.
They have a signal problem. And no amount of algorithmic sophistication will fix it.
LatentView said the quiet part out loud in their 2026 retail forecasting guide: “Model selection gets the attention. Data readiness determines whether the model delivers.” Teams that focus on algorithms before completing a data audit are, in their words, optimizing for the wrong variable. From the forward-deployed seat inside enterprise retail, I can tell you that is not a nuance. It is the whole game.
The model is not the bottleneck. The inputs are.
Here is what actually happens inside a planning function. The point-of-sale feed lands two or three days late, so the model is forecasting against a past it cannot fully see. The promotional calendar lives in a merchandising spreadsheet the forecasting engine never ingests, so a 40 percent-off event registers as an unexplained demand spike the model treats as noise. Weather signals, web browse-to-buy behavior, and external macro indicators arrive in the wrong grain, at the wrong cadence, or not at all.
Then a vendor sells the team a smarter algorithm to compensate.
This is the trap. You cannot model your way out of a bad input layer. Feed a state-of-the-art model dirty, lagging, partial signal and it returns a more confident wrong number. Garbage in, sophisticated garbage out. Worse, the confidence is the problem. A naive model that is obviously wrong gets corrected. A polished model that is subtly wrong gets trusted, shipped into replenishment, and acted on at scale before anyone catches it.
The tell is the override rate. When planners systematically overrule the system, leadership blames adoption or change management. The real cause is that the planners have learned, correctly, that the model is running on signal they do not trust. They are not resisting the technology. They are protecting the business from it.
Clean signal beats clever math, every time
The hierarchy is not subtle once you have lived it. A mid-tier model running on clean, granular, timely signal will beat a best-in-class model running on noise. The retailers winning on inventory in 2026 are not the ones with the most exotic algorithms. They are the ones who wired POS, promo, pricing, weather, web, and external demand signals into a single timely feed at the right grain, and connected the forecast output directly to a replenishment decision.
That last part matters more than the architecture. A forecast that does not flow into an action is a research project. The competitive advantage is the pipeline: signal in, decision out, fast enough to matter.
So when the next forecasting RFP lands on your desk, change the first question. Do not ask which model is most accurate on a benchmark. Ask: what signals does this consume, at what latency, at what grain, and what decision does the output trigger? If the vendor wants to talk architecture before they have seen your data, you are about to overpay for a more articulate version of the wrong answer.
The CODN is hiding in the input layer
Leaders consistently underprice the Cost of Doing Nothing on signal quality because the failure is silent. There is no outage, no error log. There is just a number that is quietly off, a stockout that did not have to happen, a markdown that did not have to be taken, a truck dispatched against demand that was never real.
LatentView puts global retail inventory distortion, the combined drag of overstocking and stockouts, at roughly 1.73 trillion dollars a year. That cost does not live in the model. It lives in the inputs feeding the model, and in the disconnect between a forecast and the decision it should drive. Every quarter you spend evaluating algorithms while your signal layer leaks is a quarter that distortion compounds against you.
This reframes the investment case for the C-suite. The highest-ROI move in your forecasting program is almost never a model upgrade. It is data engineering: instrumenting clean capture, closing the latency gap, standardizing grain, and integrating the external signals your model has been flying blind without. It is unglamorous. It does not demo well. It wins.
The retailers who internalize this will stop treating forecasting as a model-shopping exercise and start treating it as a signal-engineering discipline. The ones who do not will keep buying better algorithms to launder worse data, paying full price for confident, expensive, well-architected mistakes. Fix the signal first. The math was never the hard part.