abc-xyz-segmentation

# ABC-XYZ Segmentation Value tells you where the money is. Variability tells you whether forecasting, buffering or restructuring can work. Never output a classification without the policy consequences. ## Required data Per-SKU demand history (`sku`, `period`, `qty`) covering 12+ periods, plus unit value (`unit_price` or cost). Without unit value, ABC degrades to a volume ranking - say so and ask for prices before presenting conclusions about money. ## Workflow 1. **ABC on annual value.** Rank by annual consumption value; cumulative 80% = A, next 15% = B, rest = C. Report the actual concentration found (e.g. "15 SKUs = 80%"), not the folklore 20/80. 2. **XYZ on variability.** CV = std/mean of period demand per SKU. Defaults: X < 0.5, Y 0.5-1.0, Z >= 1.0. These are conventions - check the CV histogram for natural breaks and state the thresholds used. SKUs with structural zero periods (intermittent) belong in Z regardless of CV arithmetic; mean-based CV understates their risk. 3. **Build the 9-box** with SKU counts AND value share per cell. Value share is what makes managers act. 4. **Attach the policy per cell** (adapt wording to context): - A-X: tight forecasting pays; low buffer, frequent review, automate replenishment - A-Y: forecast + healthy buffer; investigate variability drivers - A-Z: do not chase forecasts - strategic buffer, lead-time negotiation, or make-to-order - B-X / C-X: min-max autopilot; withdraw planner attention - B-Z: buffer or longer promise dates; check if variability is self-inflicted (promotions, batching) - C-Z: rationalization shortlist - kill, consolidate, or on-demand sourcing 5. **Name the reallocation.** The deliverable is planner-hours and buffer money moving between cells - state explicitly which cells gain and lose attention. 6. **Validate.** Sum of cell value shares must equal 100%; spot-check two SKUs' classifications against their raw series before presenting. ## Pitfalls to check explicitly - ABC computed on quantity while unit values vary 10x+ ranks the wrong items. - Self-inflicted variability (order batching, month-end pushes, promotions) shows up as Z; flag it as a process fix, not a demand fact. - Classifications rot - recommend re-running quarterly and tracking cell migrations. - A dominant "C-Z is 60% of SKUs" finding usually signals assortment bloat, not a planning problem. ## Output format 1. The 9-box (counts + value share per cell) 2. Policy table per occupied cell 3. Attention-reallocation paragraph (from where, to where) 4. Rationalization shortlist (top C-Z items by holding cost or shelf age, if data allows) Worked example including a safety-stock stress test: https://github.com/gulmezeren2-byte/abc-xyz-inventory --- Source: [industrial-engineering-ai-skills](https://github.com/gulmezeren2-byte/industrial-engineering-ai-skills) by Eren Gulmez (MIT). The full method pack - entry skill, role agents, data-hygiene rules and artifact templates - lives there.