Holimization: Why Optimization Is Not Enough (www.lokad.com)

The concept of "holimization," introduced as a contraction of "holistic optimization," proposes a significant rethinking of traditional optimization approaches in supply chain management. The author argues that merely optimizing models with predefined objectives and constraints often leads to decisions that don't align with the realities of the supply chain. This misalignment creates issues such as stockouts and excess inventory, highlighting a critical gap: the need to frame optimization questions appropriately. Holimization posits that effective optimization should be an iterative process that evolves with real-world feedback, ensuring that objectives and constraints reflect actual operational complexities. This approach has profound implications for the AI and machine learning community, particularly in how algorithms are deployed in dynamic environments. By focusing on understanding and refining the "frame" of optimization—including objectives, constraints, and data semantics—holimization encourages continuous learning and adjustment based on system performance. This mindset shifts the focus from simply finding optimal solutions to ensuring that the questions driving optimization are relevant and adaptable to changing conditions. In doing so, holimization not only aims to enhance decision-making but also to align AI systems more closely with organizational goals and the practical nuances of real-world challenges.