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).

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