Divya Soundarapandian
In recent years, supply chain planning has become more complicated and less predictable. This means that companies need intelligent tools to beat disruptions and make decisions at a fast pace. The two main options available in the market today include traditional planning software, such as SAP IBP, and new custom AI planning systems. SAP IBP is considered under an extremely popular commercial product used by large companies worldwide. On the other hand, many firms simultaneously create their own AI-driven planning tools to enjoy more flexibility while also attaining speedier outcomes. Yet, as both approaches take the center stage increasingly more empirical evidence comparing performance under similar conditions has been remarkably scant. In short, what is heard around the world by way of debates are opinions and not facts. This study sets up SAP IBP against a custom-built AI planning engine on equal footing with matched demand, supply, and network constraints. Rather than try to prove one solution better than the other at this point in time benchmark testing, this study observes how each system reacts as market conditions turn against it and planning assumptions break down. The three key metrics considered were forecast accuracy, planner effort required, and speed of response. SAP IBP does well where stability and governance are key, but it becomes very slow to adjust when the demand begins to misbehave in ways that history cannot explain. But custom AI systems perform better when things go bad. They get on the move quickly, do not need a lot of manual effort, and permit teams to try different scenarios much faster. These findings provide practical insights for organizations that are weighing the trade-offs between governance-focused planning platforms and more flexible, AI-driven approaches. Keywords: SAP IBP, AI Planning Systems, Supply Chain Planning, Forecast Accuracy, Scenario Planning, Decision Support Systems