Generating $10M in annual value through AI-driven forecasting for the lumber division of a $200B Manufacturing Conglomerate.
Annual Value Created
$10M
Forecast Accuracy Lift
+7 Pts
Average Lead Time Reduced
-3 Days
In the highly commoditized lumber market, protecting margins requires flawless supply chain execution. For the lumber division of this $200 billion manufacturing and investment conglomerate, cyclical market volatility and manual forecasting processes severely restricted overall profitability. Compounding this challenge, the finished good SKUs yielded from a single log can vary significantly, rendering it exceptionally difficult to predict the exact output of the production line.
The business recognized an urgent need to embed artificial intelligence into its daily operations across all mills to optimize scheduling and enhance the profitability of its finished goods. The strategic objective was clear: leverage a vast repository of historical and real-time data to forecast supply over a three-month horizon and optimally match it to demand, thereby optimizing: customer orders, safety stock levels, and market values for daily operations.
Our team partnered directly with the division's Vice President to architect, integrate, and deploy an end-to-end AI/ML forecasting and optimization engine. Transitioning entirely away from static spreadsheets, our Forward Deployed Engineering (FDE) team built an application that enables users to input customer orders, current inventory levels, market lumber prices, and historical finished goods production data to predict the probability of specific SKUs emerging from the production.
We developed two distinct models to power a single, unified engine. First, the team implemented an AI model to actively forecast supply. The results of this initial model were then fed into a secondary optimization model, which assigned finished goods to customer demand in order to maximize profitability. By accurately predicting supply and automating mill fulfillment scheduling, the engine drastically reduced late orders, increased customer contract volumes (and thus revenues), expanded gross margins, and lowered the opportunity cost of key resources.
"By embedding AI into the core of our mill operations, we completely shifted our posture from reactive to proactive. The solution actively protects our margins against suboptimal planning."
— VP, Lumber Division
