Author: Vamshidhar Reddy Peesari is an Anaplan Solution Architect in the BI Planning Center of Excellence, specializing in large-scale Polaris models and model governance.
If you build on Polaris, you live with the number 64. The Dimension Index Total — the sum of the index scores of every dimension on a line item — is the engine's one hard ceiling. Cross it and Anaplan simply blocks the change: line item is too large.
Checking one line item against the Anapedia table takes five minutes. My problem was a consolidation model with 671 modules and about 6,600 line items, and a question no one could answer: which line items are sitting close to the ceiling, and which growing list pushes them over first?
Nobody audits that by hand. So I didn't. I gave the problem to Claude, and I want to share how it went — including the part where I told it the results were wrong.
The setup
I never gave the AI access to my model. Three standard blueprint exports were enough: Modules (Applies To, Time Scale, Versions), General Lists (item counts, parent hierarchies, top levels, subsets), and the full Line Items blueprint.
One thing I'd insist on if you try this: before any math, make the AI state the scoring rules and cite its source. Claude pulled the current Anapedia page and confirmed the details that are easiest to botch — composite hierarchies count the list plus all parents plus the top-level item, Time's member count includes summary periods, and Time and Versions score like any other dimension. Audit the method before you audit the output.
From there it parsed every Applies To string, resolved the hierarchy chains, handled the export quirk where a dash on a line item means "inherit the module's dimensionality," skipped the 737 No Data line items that hold no cells, and produced a DIT for all 5,917 real line items — each one broken down dimension by dimension.
The part where I said "that's wrong"
The first pass flagged a bunch of line items over 64. My reaction was probably yours right now: impossible — the model runs.
Here's the thing: we were both right. Subset member counts don't exist anywhere in a General Lists export — only the subset names do. So every ss. dimension had been scored at the full parent list size, clearly labelled as an upper bound. A working model sitting "above" that bound isn't a contradiction; it's a measurement. It tells you exactly how many index points your subsets must be saving.
And the pattern confirmed it: every single flagged line item had at least one subset dimension. Not one line item built purely on full lists was over the limit. That's exactly what a healthy Polaris model should look like, because subsets are the standard trick for staying under 64.
That exchange was the most valuable part of the whole exercise. The AI didn't fold when challenged, and it didn't bluff. It explained precisely which missing input caused the gap — and then turned that input into something I could supply.
The deliverable that outlives the chat
I didn't ask for a report. I asked for a live Excel workbook, and that made all the difference.
Sheet one: every line item with its dimensions side by side, each dimension's index score, and the total DIT with a status flag. Sheet two: every subset used as a dimension, with a yellow column where I type the real item counts from the model. Each entry instantly recalculates every affected DIT — no AI in the loop anymore, just VLOOKUPs and ROUNDUP(LOG(n,2)). Calendar length and version count are input cells too.
That last part earned its keep immediately. When I corrected my assumptions to our real settings — a 7-year calendar and 10 native versions — the picture refreshed in seconds. Fun fact I hadn't internalized before: going from 3 versions to 10 quietly adds two index points to every versioned line item in the model. Small setting, real DIT.
A final filtered version kept only line items above 60 — Anaplan's own "start paying attention" line — which shrank the remaining manual work to entering counts for 33 subsets. That's the entire cost of an exact, model-wide DIT audit.
The math on time
Since someone will ask: my hands-on effort was about an hour, spread across roughly three hours end to end. That includes exporting the three blueprints, the back-and-forth where I challenged the results, correcting the calendar and version assumptions, and requesting the filtered final file — the gap between the two numbers was mostly me reading outputs and sanity-checking figures against the model.
The honest manual estimate is uglier than people think. It's not just scoring 5,917 line items — it's parsing every Applies To string against 200+ lists and 97 subsets, resolving parent chains and top levels for every composite hierarchy, and tracking which line items override module dimensionality. Even if you built yourself a helper spreadsheet first (a solid day of work on its own) and then averaged a brisk 30 seconds per line item, you're looking at 50–60 hours — a week and a half of doing nothing else. And that's one pass, with no recalculation when the calendar extends or a version gets added.
Which is the real punchline: at that price, the analysis simply doesn't happen. The realistic comparison isn't three hours versus sixty — it's three hours versus never.
What I'd tell other model builders
The AI multiplied my Anaplan knowledge; it didn't replace it. I still had to know what a DIT is, which exports hold what, that subsets would be the blind spot, and what a dash in Applies To means. What I got in return was consistency across thousands of rows and a tool instead of a snapshot.
Argue with the numbers. An analysis you've pushed back on is worth ten you've merely received.
Make every assumption an editable cell, not a footnote. That one design choice turned an afternoon's analysis into a governance asset we'll still be using next planning cycle.
If you're on Polaris — or sizing up a migration from Classic — this kind of audit tells you your true headroom before a growing list tells you the hard way. It used to be the analysis nobody had time for. It cost me an hour of real work — sixty if I'd done it the old way, and let's be honest, I never would have.
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