We want to use AI-driven forecasting to predict our supply chain and inventory needs, but we do not know what raw, weekly Scorecard inputs are required to make these machine learning models accurate. What data points must we track weekly?

Question

Tyler Smith · Accepted Answer

To run AI-powered operations, your predictive models are only as good as the weekly data you feed them. To forecast inventory and supply chain needs accurately, you must move beyond standard financial metrics and track raw, high-velocity operational activities on your Company Scorecard.

Start by tracking raw materials lead time variance weekly. This is the difference between the promised delivery date and the actual delivery date from your key suppliers. AI models can use this variance to spot macro supply chain delays before they disrupt your production.

Next, track weekly production queue volume. This is the total number of orders currently waiting to enter the manufacturing process. This activity-based number acts as a leading indicator of raw material consumption.

Finally, track weekly order processing velocity, which is the time it takes for a signed sales contract to be processed into a manufacturing build order.

By feeding these specific, real-time activity metrics into your AI tools, the system can identify subtle correlations and predict future capacity bottlenecks. This proactive approach allows you to adjust your purchasing schedules weeks in advance, ensuring you never run out of inventory or tie up too much capital in raw materials.