Learn about demand planning and forecasting
Practical guides on sales forecasting, Machine Learning, S&OP and inventory to make better decisions with your data.
Demand forecast vs. sales forecast: data and decisions
Demand forecast vs. sales forecast: what each measures, the data each needs, and the decisions each should guide.
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Forecast-based purchasing planning: from forecast to purchase order
Purchasing planning: turn forecast demand, safety stock and reorder points into purchase quantities, dates and priorities.
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Forecast Studio now has an API — everything the app does, now programmable
New for Pro and Enterprise plans: the Forecast Studio API exposes the full platform — data injection, variables, model training, forecasts and metrics — so your systems can automate forecasting end to end, the same pattern as SAP Service Layer, Stripe or Shopify.
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ARIMA and SARIMA models: the Box-Jenkins method for time series forecasting
How ARIMA(p,d,q) and seasonal SARIMA models work: autoregression, differencing, moving averages, ACF/PACF identification, and automatic order selection with the Hyndman-Khandakar algorithm.
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Evaluating forecast models: train/test splits, time-series cross-validation and error metrics
How to measure a forecasting model honestly: why in-sample fit overstates accuracy, how to split time series into train and test sets without leakage, rolling-origin cross-validation, and when to use MAE, RMSE, MAPE, sMAPE or MASE.
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Exponential smoothing and ETS models: from simple smoothing to Holt-Winters
How exponential smoothing works, from simple exponential smoothing to Holt's trend method and Holt-Winters seasonal models, the ETS state-space framework, and when to prefer it over ARIMA.
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Prediction intervals in forecasting: quantifying uncertainty beyond the point forecast
What prediction intervals are, how ETS and ARIMA derive them analytically, why bootstrapping matters for non-Gaussian errors, and how to evaluate interval quality with coverage and pinball loss.
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Stationarity in time series: unit-root tests, differencing and the Box-Cox transform
What a stationary time series is, why ARIMA needs one, how to test for stationarity with the ADF and KPSS tests, and when to use differencing or a Box-Cox transform to get there.
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Time series decomposition: separating trend, seasonality and the residual with STL
How classical and STL decomposition split a time series into trend, seasonal and remainder components, when to use additive vs. multiplicative, and how decomposition feeds forecasting and anomaly detection.
Read article → Forecast Studio now has a Free plan — and full support for Free users
The new Free plan runs on the public tenant at app.forecaststudio.com.mx: ML forecasting models, the full variables toolset, CSV export and scheduled support, at no cost and with no time limit.
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ABC/XYZ analysis: stop treating every SKU the same
How to segment your catalog with ABC (value) and XYZ (demand variability), the 3×3 matrix with a strategy for each cell, and where sharper forecasts pay off most — with formulas and a worked example.
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Demand planning: the complete guide (process, metrics, inventory and tools)
Everything demand planning covers in one guide: the 5-step monthly process, the accuracy metrics that matter, how forecasts drive safety stock and purchasing, S&OP alignment, and how to choose your tooling — with evidence behind every claim.
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