Most sales forecasts hand you a single number and hope. Dispensight's engine is Bayesian — every prediction comes with a calibrated confidence range, so you can staff and stock against real uncertainty instead of a lucky guess. Here's how it held up over 35 consecutive days on a live retail deployment.
Each day's forecast (line) carries an 80% credible interval (shaded band); the dots are what actually happened. Values are indexed to the period average (= 100) — no store, date, or revenue figure is shown. When the dots land inside the band as often as the band promises, the uncertainty is honest. Here, they land inside even a little more often than the 80% target.
A point forecast that names one number for Friday is silently wrong almost every time — real demand lands a little above or below. A calibrated interval says “80% chance between here and here,” and then actually keeps that promise. That's what lets you set staffing and reorder points with a known safety margin instead of gut feel.
Calibration is the hard part: many models quote confidence they don't earn. Over this run, an 80% interval captured reality 91.4% of the time — slightly conservative, which is exactly the safe direction to miss.
32 of 35 days fell inside the 80% band. The three exceptions were single-day demand spikes tied to external shocks (e.g. extreme-weather events), not model drift — the forecast re-centred immediately after each.
Share of the 35 days whose actual sales landed within a given band of the forecast. Even single-store daily demand — the noisiest horizon there is — clustered tightly around the prediction.
Dispensight v4.5.2 · HMC-NUTS forecasting engine
A Hamiltonian Monte Carlo sampler with the No-U-Turn Sampler explores the full space of likely outcomes, so every forecast is a probability distribution — not a fragile single guess.
External regressors let the model anticipate how conditions move foot traffic, and cleanly attribute the demand shocks it can't prevent so they don't poison future forecasts.
Sampling chains are checked for convergence in-flight; any that fail restart automatically, so a bad draw never ships as a forecast.
Every forecast is scored against what actually happened — MAPE, interval coverage, and bias tracked daily — so accuracy is measured, not assumed.
Dispensight turns your point-of-sale history into calibrated demand forecasts your team can actually plan around.
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