In the context of supply chain management, two crucial strategies are often employed: prediction and optimization. Prediction involves forecasting future outcomes based on historical data and key influencing variables, allowing for proactive measures in the supply chain. Optimization, on the other hand, seeks to find the most effective solution from a set of alternatives, often by maximizing or minimizing certain variables within known constraints, such as cost, time, or resources.

This session delves into the relationship between these strategies in the supply chain industry. In many supply chain decisions, predictions are used first, with optimization applied subsequently to assess different options under known conditions. The predict-then-optimize approach involves creating a predictive model first, followed by an optimization step. However, this separation can lead to less than optimal outcomes in supply chain efficiency. In contrast, the predict-and-optimize method incorporates cost considerations directly into the predictive model, aiming to enhance decision-making efficiency from the beginning.