*Enterprise demand forecasting has grown too complex for legacy tools—millions of time series, SKU proliferation, and tight planning cycles have outpaced both the technology and the talent available to run it *MMF Agent is a guided AI workflow built on Genie Code that makes Databricks' multi-model forecasting framework accessible without requiring deep data science expertise *Teams using MMF Agent compress days of setup into hours, produce cleaner training data, and unlock multi-model accuracy improvements that were previously limited to organizations with specialist forecasting talent

Demand forecasting has always been at the center of retail and CPG planning. It shapes inventory decisions, informs production schedules, drives trade promotion investment, and sets the conditions for every S&OP conversation that follows. When the forecast is wrong, costs accumulate quickly, leading to stockouts, excess inventory, margin erosion, and downstream disruption that ripples through the supply chain and commercial teams alike.

What has changed in recent years is not the importance of the forecast. It is the degree of difficulty.

A decade ago, a demand planner working with a few thousand SKUs across a handful of channels could manage forecast quality with a combination of statistical models, spreadsheets, and hard-won institutional knowledge. That world no longer exists for most Retail and CPG organizations. SKU proliferation, the explosive growth of e-commerce channels, regional fragmentation, and the rise of short-lifecycle promotional SKUs have created forecasting environments that most legacy tools were never built to handle.

Where a planner once managed hundreds of time series, today's enterprise forecasting problems routinely involve hundreds of thousands, sometimes far more. Each time series has its own seasonality profile, its own signal-to-noise characteristics, and its own sensitivity to external variables like promotions, weather, and macroeconomic conditions. The statistical techniques that served well at smaller scales simply do not generalize reliably at this volume and variety. Accuracy degrades. Exception management becomes unsustainable. The forecast loses its authority as a planning input.

The answer that most sophisticated forecasting teams have converged on is a multi-model approach: rather than selecting a single technique and applying it uniformly, you evaluate a range of models against your actual data and let the results determine which performs best for each time series. In practice, this produces noticeably better accuracy, but it also creates a new challenge.