inventory-policy
GitHub按ABC/XYZ维度对物料分类，设定各细分群体的服务水平、安全库存逻辑及补货策略（ROP/Min-Max等），并规划呆滞库存审查周期，以优化库存结构并降低缺货与积压风险。
Trigger Scenarios
Install
npx skills add mohitagw15856/pm-claude-skills --skill inventory-policy -g -y
SKILL.md
Frontmatter
{
"name": "inventory-policy",
"description": "Set inventory policy for an item class: segmentation, safety stock, and replenishment method. Use when asked to set safety stock levels, segment items by ABC\/XYZ, choose reorder points vs min-max, define stocking policy, or review excess and obsolete inventory. Produces a segmentation grid, per-segment service targets and safety-stock logic, a replenishment method choice per segment, and an E&O review cadence."
}
Inventory Policy Skill
Inventory policy set item-by-item on gut feel produces the classic warehouse: too much of what doesn't sell, stockouts on what does. This skill sets policy by segment — classify items by value and demand variability, assign service targets and safety-stock logic per segment, choose the replenishment method that fits the demand pattern, and put excess & obsolescence review on a calendar so write-offs stop arriving as year-end surprises.
What This Skill Produces
- An ABC/XYZ segmentation grid with the item class placed in it
- Per-segment service-level targets and safety-stock sizing logic
- A replenishment method recommendation (reorder point vs. min-max vs. order-to-demand) per segment
- Review frequencies: how often parameters get recalculated per segment
- An excess & obsolescence (E&O) review cadence with aging triggers and disposition paths
Required Inputs
Ask for these if not provided:
- Item scope — the items or class under review; count, annual usage value, unit costs
- Demand pattern — average demand, how lumpy/variable it is, seasonality, item lifecycle stage
- Lead times — supplier replenishment lead time and its variability
- Service expectations — target fill rate or customer commitments; consequence of a stockout
- Constraints — MOQs, shelf life, storage limits, working-capital pressure
From a thin brief, place the item in the grid using stated context, label placements [inferred — confirm with 12 months of usage data], and proceed.
Segmentation & Policy Framework
ABC by annual usage value (A ≈ top 80% of value, B next 15%, C last 5%). XYZ by demand variability (X = steady/predictable; Y = variable but forecastable, e.g. seasonal; Z = lumpy/intermittent).
| X (steady) | Y (variable) | Z (lumpy) | |
|---|---|---|---|
| A (high value) | 97–99% service; lean SS; tight ROP, frequent review | 95–98%; SS sized to lead-time demand variability; ROP, monthly recalc | Do not blanket-stock: order-to-demand or contract supplier-held stock; each stocking decision is a named business call |
| B | 95–97%; ROP with standard SS | 92–95%; ROP or min-max | Min-max with small max, or make-to-order |
| C (low value) | 90–95%; min-max, generous max (cheap to hold, expensive to expedite) | 90%; min-max, quarterly review | Stock only if stockout stops a line or an A-item sale; else non-stocked |
Safety-stock logic (z-score framing, no heavy math): safety stock buffers demand and lead-time variability over the replenishment lead time. The service target sets a z multiplier on that variability — roughly z ≈ 1.28 at 90%, 1.65 at 95%, 2.05 at 98%, 2.33 at 99%. Two judgments matter more than the formula: the curve is nonlinear (95→99% costs far more stock than 90→95% — spend those points only on A-items), and for Z-items the variability estimate itself is unreliable, so formula-driven SS produces nonsense — use lead-time-demand coverage plus judgment, and say so.
Reorder point vs. min-max: ROP (order a fixed/economic quantity when stock hits demand-over-lead-time + SS) suits steady movers with continuous tracking — A/B items. Min-max (order up to max when stock falls to min) suits cheap, periodically reviewed, or lumpy items — most C and Z items. Respect MOQs: if MOQ ≫ the economic quantity, that's a supplier negotiation or a stocking-decision review, not a bigger max.
E&O cadence: monthly — flag items with >180 days of supply on hand or no usage in 90 days; quarterly — disposition review (rework / return / redeploy / discount / scrap) with finance, reserve recommendation per aging band; at lifecycle events — last-time-buy sizing when a supplier or product end-of-lifes.
Output Format
Inventory Policy: [item class / scope]
1. Segmentation — the grid populated with item counts and value per cell; method used.
2. Policy table — Segment | Service target | Safety-stock logic | Replenishment method | Parameter review frequency.
3. Item-class recommendation — for the specific scope: segment, target, SS sizing, method, and the parameters to set, with assumptions labelled.
4. E&O cadence — triggers, review calendar, disposition paths, reserve approach.
5. Exceptions — items policy must not automate (shelf-life, LTB, contractual stock) and their handling.
Quality Checks
- Segmentation uses both value (ABC) and variability (XYZ) — never ABC alone
- Service targets differ by segment and the stock cost of high targets is acknowledged
- AZ cell is handled as named decisions, not a formula output
- Replenishment method matches demand pattern and review practicality, with MOQ conflicts flagged
- E&O review has thresholds, a calendar, and disposition paths — not "review periodically"
- Every inferred parameter is labelled with the data needed to confirm it
Anti-Patterns
- Do not set one service level for everything — 98% across the board is working capital burned on C-items
- Do not apply z-score safety stock to lumpy Z-demand — the variability input is garbage and the output will be too
- Do not size safety stock off the forecast alone — lead-time variability is half the buffer's job
- Do not treat MOQ-driven stock as safety stock — it's a cost of the deal and should be challenged with the supplier
- Do not let E&O wait for the annual count — aging inventory loses disposition options every month it sits
- Do not recalculate parameters weekly for C-items or annually for A-items — review effort follows value
Version History
- 54fad50 Current 2026-07-19 13:30
