Techniques for Anaplan model optimization (performance + sizing)

bhanuchalotra

Author: Bhanu Chalotra is a Certified Master Anaplanner working with Anaplan community and designing different solutions around FP&A, headcount planning, and ICM.

Anaplan is not just about how a model is built — it’s also about how efficiently it runs. This article shares proven optimization techniques to improve model performance and right-size your solution, with practical examples to help you design an optimized model.

1. Design at the lowest necessary grain and use subsets
   Practice: Build modules at the minimum granularity the process truly needs.
   Example: If finance plans headcount monthly by department, don’t store it at employee level for detail. Keep the planning module at department × month and only add employee-level modeling in a separate, purpose-built module if there’s a real use case.
2. Avoid using whole model calendar
   Practice: Build modules with the time ranges wherever applicable.
3. Avoid using Multiple IF – ELSE Condition
   Practice: Build the formula with lesser if else condition. If more than 3 if else condition, then try to use mapping module.
4. Avoid combining high cardinality lists
   Practice: Don’t multiply large lists together unless required.
5. Reduce sparsity by splitting modules
   Practice: If most cells are blank, redesign to reduce sparsity.
   Example: One module holds marketing spend for 12 channels, but only 3 channels apply to each brand. Split into separate modules by channel group (or use a mapping module + allocation approach) so you aren’t storing empty intersections for non-applicable combinations.
6. Separate Input / Calc / Reporting modules
   Practice: Keep input simple, calculations controlled, and reporting shaped for UX.
   Example:
   1. Input module: planners enter “Units” and “Price” only (Product × Month)
   2. Calc module: margin, FX conversion, allocations, phasing logic
   3. Reporting module: adds executive-friendly line items (YoY, variance, KPI flags) without burdening the input layer
7. Minimize formulas in the largest modules
   Practice: Large modules should be mostly data + simple references, not heavy logic.
   Example: Instead of calculating complex pricing logic inside a large SKU × Customer × Month module, compute “Net Price” in a smaller SKU × Price Listmodule, then reference it into the detailed module.
8. Be intentional with summaries (rollups)
   Practice: Turn on rollups only where they’re needed.
   Example: If users only report totals at Region and Country, don’t enable summaries for every intermediate hierarchy level (e.g., district/store). Keep rollups selective to avoid extra compute/storage.
9. Centralize shared logic to prevent duplication
   Practice: One source of truth for common calculations.
   Example: If “Active Product” logic (based on launch date, status, end-of-life) is repeated in 6 modules, create one Product Attributes / Flags module and reference the Boolean flag everywhere. This improves consistency and reduces maintenance.
10. Optimize UX pages for rendering speed
    Practice: Design pages to load quickly and show only what users need.
    Example: Instead of a single page showing 40-line items across multiple large grids, create two pages:
    1. “Plan inputs” (small grid + key KPIs)
    2. “Plan review” (aggregated reporting view + variances)
       This improves initial load and reduces unnecessary rendering.
11. Use lean, dedicated integration views
    Practice: Build saved views specifically for imports/exports — minimal dimensions/line items.
    Example: For an actuals load, create a view with only: entity, account, period, amount (and the exact line item needed). Don’t export an entire reporting view that includes variances, KPIs, and extra line items.
12. Continuously monitor and govern model growth
    Practice: Make optimization a recurring activity, not a one-time cleanup.
    Example: Add a monthly “model maintenance” routine: review top largest modules, identify any new sparse combinations, and retire unused line items or old versions/scenarios from prior planning cycles.

Thoughts or questions? Leave a comment!

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Community Manager note: Optimization tactics may differ for Polaris models. Both Polaris and Classic will leverage core model building best practices but will differ when optimizing around sparsity as this is not a key consideration for Polaris.