This is the Electe Review. Today, why better forecasting algorithms will not fix broken inventory decisions unless merchants clean their data and tie predictions to purchasing rules? When a launch order goes wrong, one warehouse ends up with empty shelves while another holds dead stock that only moves at a discount. The standard instinct is to blame the math and shop for a more complex model. That instinct is usually wrong. An algorithm cannot rescue a sales history that treats out-of-stock items as unpopular ones, and a forecast is useless if buyers ignore it when placing purchase orders. UNCTAD estimated global e-commerce at $26.7 trillion in 2019, with online retail rising from 16% pre-pandemic to 19% in 2020. Channel assumptions shift fast, and raw sales columns blur trend, seasonality, and sudden shocks into the same number. A retail forecasting study of dataset series with 20.9% missing data found that 99.9% of product series had at least one zero sales day, averaging 492 zero sales days per series. In that same study, localized tree-based models beat deep learning architectures. Why? Because before modeling anything, you have to explain your zeros. If a product was out of stock or delisted, that is censored demand, not customer rejection. Complexity has to earn its place against a simple seasonal baseline. In one short-term e-commerce study, granular discount classifications improved linear, ridge, and lasso regression may be by up to 36.19%, yet delivered no significant gain for random forest, support vector machine, or gradient boosting models. Another study of a Danish discount grocery chain showed XGBoost worked best only when products had more than five historical promotions, with fewer than five, penalized linear regression won. Even accurate promotion forecasts can mislead buyers. A four-day campaign selling 120 units daily instead of 40 looks like a 320-unit lift, until a 10-day post-promotion slump to 25 units daily erases 150 units of that gain. Worse, a 2025 International Journal of Forecasting study found that promotions push forecasters to overestimate demand when service targets are high. Whether you test six methods automatically in Electe or run a 90 day pilot on your top 20% of SQUs, judge forecasts by stockout and excess costs, not chart elegance. That's the review.