[Springer] [Single chapter]https://link.springer.com/chapter/10.1007/978-3-032-07054-8_8 Copy

Abstract:This chapter emphasizes the importance of aligning AI and machine learning models with actual decision-making objectives in supply chain management. Rather than predicting metrics in isolation, a decision-focused AI approach directly incorporates business goals—such as cost efficiency, service level, or sustainability—into the learning and optimization process. The authors discuss how integrating these end objectives into algorithmic design can improve the relevance and impact of AI recommendations on supply chain outcomes. For example, an AI model might be trained not just to forecast demand accurately, but to minimize total supply chain cost or emissions in downstream decisions based on those forecasts.

By examining case studies and methods where objectives like cost minimization and service maximization are part of the AI model's training loop, the chapter illustrates the tangible benefits of this approach. It also addresses the practical challenges that come with decision-focused AI, including the complexity of encoding multiple objectives into algorithms and ensuring solutions remain interpretable and robust. Overall, the chapter shows that when AI tools are developed with supply chain decisions in mind, they can more effectively bridge the gap between predictive analytics and prescriptive action, leading to smarter and more goal-aligned operations.