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Safety Stock Planner

Written in-house · editorially graded

Every SkillsBank offering is written in-house — never scraped or crowd-sourced — and graded against a fixed editorial rubric in a separate grading step.

Dry-run test passed · Aug 21, 2026

Trigger check passed

Editorially graded · Aug 21, 2026

"Builds a working safety-stock and reorder-point XLSX workbook (with dashboard) plus a plain-English PDF one-pager from a customer's historical usage data and supplier lead times, using the combined-variability (APICS) formula. Applies to cleaning, janitorial, paper goods, and similar recurring-use supply items, though the same method works for any per-SKU consumption data. Use when a facilities, procurement, or operations manager has past consumption records and wants per-SKU safety stock and reorder points to cut stockouts without overstocking."

What you get

An installable SKILL.md file for your AI assistant. Produces an Excel workbook (.xlsx) and a PDF.

Works in
Claude Code · Cursor
Native — drops into .claude/skills/
Copilot · Codex · Gemini CLI
Native — reads .agents/skills/
claude.ai — web & desktop
Upload the .zip in Customize → Skills
ChatGPT
Via a Project — paste into its instructions

See it in action

A real input → output sample

Example Input
Here's the usage data for my cleaning supplies:
1. Bleach 1 Gal – weekly usage (12 weeks): 5,6,4,7,5,5,6,8,4,5,6,5. Lead time: 10 days. Service level: 98 %. On‑hand: 15. 4 units per case.
2. Trash Liners 33 gal – weekly usage (8 weeks): 20,22,19,21,20,23,20,21. Lead time: 5 days. On‑hand: 25. 10 units per case.
3. Glass Cleaner 19 oz – weekly usage (4 weeks): 10,10,10,10. Lead time: 14 days. Service level: 95 %. On‑hand: 20. 6 units per case.
4. Disinfectant Wipes – Lead time: 7 days. On‑hand: 30. 1 unit per case.
Example Output
• XLSX workbook with three sheets (Raw Data, Calculations, Dashboard). Calculations include daily demand, demand‑standard deviation, safety‑stock, reorder‑point and risk flags for each SKU; Disinfectant Wipes left blank with a data‑missing note.
• PDF one‑pager summarizing the safety‑stock methodology, default 95 % service level for Trash Liners, zero buffer for deterministic Glass Cleaner, and a recommendation to monitor high‑variability Bleach.
• Chat‑based Data Notes:
  - Trash Liners – service level defaulted to 95 % (editable).
  - Glass Cleaner – zero variance, low confidence (4 weeks).
  - Disinfectant Wipes – no usage history; add data to compute.
  - Items needing input: service level for Trash Liners, usage history for Disinfectant Wipes.

Questions

What data do I need to provide to use this?

Per-SKU historical usage (ideally 8-13+ weeks, daily or weekly), plus supplier lead times if you have them. Service level targets and current on-hand quantities are optional but improve the output. It works with whatever consumption records you already track.

What exactly do I get back?

An editable XLSX workbook with Raw Data, Calculations, and Dashboard sheets showing per-SKU safety stock, reorder points, confidence levels, and risk flags — all live formulas, not static numbers — plus a plain-English PDF one-pager explaining how to read and adjust the numbers.

What happens if I'm missing some inputs, like lead-time variability or service level targets?

It proceeds anyway using sensible defaults (95% service level, demand-only variability if lead-time variance is missing) and flags every assumption in the workbook's Data Notes column and in a closing reader note, so nothing is silently guessed or left blank as '[TBD]'.

Does this work with the messy usage data I already have in spreadsheets?

Yes — paste or attach your existing per-product consumption history and lead times; the skill builds a fresh workbook from that data each run. It doesn't require a prior safety-stock file, just your raw usage records.

Why not just ask my AI assistant to calculate this directly?

It applies the specific combined-variability (APICS) formula rather than a simplified demand-only version, builds it as auditable live formulas you can edit, and adds diagnostics like coefficient-of-variation flags, sensitivity tables, and a risk dashboard — output most one-off chat answers won't structure this consistently.

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