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What hands-on Anaplan work reveals
Polaris at enterprise scale: speed is a design decision
Anaplan's new Polaris benchmarks are impressive. Here is what it takes to see that performance in your own model.
Anaplan recently published a white paper on the performance of its Polaris calculation engine. It explores how Polaris enables sub-second recalculation, trillion-cell dimensionality, and deep hierarchy aggregation for real-time, enterprise-scale planning. It also covers why planning teams rely on Polaris for consistent performance in high-concurrency environments, backed by real benchmarks.
These are meaningful numbers. In our experience, though, performance on Polaris is earned in the model design, not granted by the engine.
Anaplan API Live: learn the Anaplan APIs by running them
A free, open-source Jupyter notebook that lets model builders and integration teams run every Anaplan API live in the browser. By Anirudh Nayak. Anaplan's APIs unlock a great deal of value, from automating imports and exports to building integrations with ERP, CRM and data platforms. Yet for many model builders and business owners, the APIs remain unfamiliar territory. Documentation explains what each endpoint does, but there is no substitute for running a request and seeing the response. That is why we built Anaplan API Live, an instructional guide in the form of a Jupyter notebook that makes the Anaplan APIs approachable.
The CIO's role in connected planning, revisited for the AI era
Anaplan's CIO guide argued that planning is a strategic IT concern. With AI now reading planning data, that argument is stronger than ever. Some years ago, Anaplan published a guide on how CIOs can become strategic business executives through planning. It contrasted legacy planning with the Anaplan approach, and described a single platform on which business users plan across finance, sales, supply chain, human resources and marketing, collaborating in real time. The guide also described the Hyperblock engine, Anaplan's patented in-memory planning engine. The core argument has aged well. What has changed is the stakes
AI in planning is only as good as the model beneath it
Anaplan has published Polaris benchmarks for demand and inventory planning. Here is how to translate them into a meaningful proof of value. Supply chain is where planning models grow fastest. Every product, location, customer and week multiplies the model, and most of those combinations are empty. That is precisely the problem Anaplan addresses in its white paper on Polaris for supply chain. The paper shares benchmark results from Polaris-powered applications across demand and inventory workflows, and explains how Anaplan supports enterprise-scale planning across thousands of SKUs, locations and customers. It also examines what drives sub-second responsiveness in complex user journeys under heavy concurrent workloads, and how high-dimensional sparsity is managed across products, customers, locations and time
Supply chain planning on Polaris: test your peak cycle, not a demo
Anaplan has published Polaris benchmarks for demand and inventory planning. Here is how to translate them into a meaningful proof of value. Supply chain is where planning models grow fastest. Every product, location, customer and week multiplies the model, and most of those combinations are empty. That is precisely the problem Anaplan addresses in its white paper on Polaris for supply chain. The paper shares benchmark results from Polaris-powered applications across demand and inventory workflows, and explains how Anaplan supports enterprise-scale planning across thousands of SKUs, locations and customers. It also examines what drives sub-second responsiveness in complex user journeys under heavy concurrent workloads, and how high-dimensional sparsity is managed across products, customers, locations and time
Polaris at enterprise scale: speed is a design decision
Anaplan's new Polaris benchmarks are impressive. Here is what it takes to see that performance in your own model. Anaplan recently published a white paper on the performance of its Polaris calculation engine. It explores how Polaris enables sub-second recalculation, trillion-cell dimensionality and deep hierarchy aggregation for real-time, enterprise-scale planning. It also covers why planning teams rely on Polaris for consistent performance in high-concurrency environments, backed by real benchmarks. These are meaningful numbers. In our experience, though, performance on Polaris is earned in the model design, not granted by the engine.
Where do you want your planning to be next year?
Planning that's slower than business needs, forecasts nobody acts on, an Anaplan investment that isn't paying back. Describe it and we'll tell you honestly where we can help.
