── ── Industry
Tax Prep — Season Throughput (Theory of Constraints)
The parent theory-of-constraints finds the one step that caps total output and subordinates everything to it. In a tax practice the constraint is almost never "preparation" — it's usually the reviewer or client missing-documents. Optimizing anything else is wasted motion during season.
Run Tax Prep — Season Throughput (Theory of Constraints) on a real problem
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How it works
Pipeline stages: intake → doc-complete → prep → review → e-file. 1. Measure WIP at each stage; the stage with the growing queue = constraint. 2. Exploit: protect reviewer time (no admin), pre-triage returns so review is fast. 3. Subordinate: preparers pace to reviewer capacity, not their own. 4. Elevate: add reviewer capacity / async review / raise complexity threshold for partner review.
When to use it
- returns are piling up before a deadline
- reviewer/preparer is the bottleneck
- 'we can't get through them all,' 'clients waiting on missing docs,' planning staffing for Jan–Apr
When not to use it
off-season, low volume; the issue is a single stuck return (use debugging, not capacity theory).
Worked example
Tax Prep — Season Throughput (Theory of Constraints)
The parent theory-of-constraints finds the one step that caps total output and subordinates everything to it. In a tax practice the constraint is almost never "preparation" — it's usually the reviewer or client missing-documents. Optimizing anything else is wasted motion during season.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
Related mental models
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The parent peak-end-rule says memory is dominated by the emotional peak and the end, not the average.
The parent customer-relationship-ladder moves a contact up rungs from stranger → advocate.