PriyaVenkatesh OP
Inventory Planner · Chennai
5 days ago
Eight months ago we swapped out our 7-year-old Excel macro forecasting system for a Prophet + XGBoost ensemble and it has been the most impactful change our team has made in years. Our MAPE (mean absolute percentage error) dropped from 31% to 14% on a dataset of about 4,200 SKUs across 6 warehouses. The real lesson though is that model accuracy alone didn't move the needle on business outcomes — we had to rebuild our reorder triggers and safety stock formulas around the new confidence intervals the model produces, because the old rules were calibrated for a model that was much less precise. If you're making this switch, budget as much time for process redesign as for the ML work itself; the model is only half the job. Meta's Prophet library documentation at facebook.github.io/prophet is genuinely beginner-friendly and a good starting point.

from prophet import Prophet import pandas as pd df = pd.read_csv('sku_demand.csv') # columns: ds, y df['ds'] = pd.to_datetime(df['ds']) m = Prophet( seasonality_mode='multiplicative', changepoint_prior_scale=0.05, yearly_seasonality=True, weekly_seasonality=True ) m.add_country_holidays(country_name='IN') # Indian holidays m.fit(df) future = m.make_future_dataframe(periods=90) forecast = m.predict(future) print(forecast[['ds','yhat','yhat_lower','yhat_upper']].tail(10))

AlexMueller_3PL
Data Engineer · Hamburg
4 days ago
Great write-up. One thing we found invaluable that most tutorials skip: exogenous variables are often more predictive than historical demand itself. We feed in weather forecasts, local event calendars, competitor promotional dates (scraped legally from public pages), and even public holiday indicators for every region we serve. When we added just weather data — temperature, precipitation probability — to our food-adjacent SKUs, accuracy improved by another 6 percentage points. Nixtla's open-source library nixtla.github.io/statsforecast handles exogenous regressors really elegantly and it runs significantly faster than Prophet on large datasets. In 2026 with energy costs squeezing margins everywhere, over-stocking is no longer just a cash flow problem — it's a warehouse heating/cooling cost problem too.

LinaNordstrom_LogTech
Product Manager · Stockholm
2 days ago
Something nobody mentions when selling ML forecasting to leadership: you need to explain prediction intervals, not just point forecasts. Your model saying "we'll sell 430 units next month" means nothing without the uncertainty band. We built a simple internal dashboard using Plotly that always shows the 80% and 95% confidence intervals alongside the point forecast, and it fundamentally changed how our buying team makes decisions — they stopped asking "what will demand be?" and started asking "how much risk are we comfortable holding inventory for?" That shift in framing led to much smarter purchasing decisions than any model improvement alone. Probabilistic thinking is genuinely the most underrated skill in supply chain right now