Reinventing Forecasting: Predictive Analytics in the Supply Chain

Supply chain forecasting has long been one of the most consequential — and most error-prone — functions in logistics. When forecasts are off, the ripple effects are felt across the entire operation: excess inventory ties up capital, stockouts damage customer relationships, and reactive purchasing erodes margins.

This is the challenge that brought one of our logistics clients to Adaptive & Co. They were running on a spreadsheet-based forecasting process that had served them well when the business was smaller but was clearly showing its limits as volume and complexity grew.

Diagnosing the Problem

Our first step was to audit the existing forecasting process and understand where the errors were concentrated. The data revealed a clear pattern: forecast accuracy was highest for established product lines with stable demand, and lowest for newer lines, seasonal products, and items with high promotional variability.

The existing model treated all SKUs essentially the same, applying a uniform approach regardless of demand characteristics. That was the core problem.

Building the Predictive Model

Working closely with the client’s operations and data teams, we built a multi-model forecasting system that applied different techniques to different demand profiles. Stable, high-volume SKUs were handled with classical time-series methods. Volatile and seasonal products used machine learning models trained on a broader set of signals, including promotional calendars, weather patterns, and macroeconomic indicators.

The system was integrated directly into the client’s ERP, so forecast outputs were available to planners in the tools they already used — no additional context-switching required.

Results

Within six months of deployment, inventory costs had fallen by 30%. Stockout incidents dropped by 44%. And perhaps most significantly, planner time spent on manual forecast adjustments fell by over half — freeing the team to focus on exception management and supplier relationships rather than data wrangling.

The lesson here is not simply that predictive analytics works. It is that the right model for demand forecasting depends entirely on the characteristics of the demand being forecast. Generic solutions applied uniformly rarely match the precision of purpose-built approaches.