ecom-inventory-health
用週轉率、ABC 分類和安全庫存診斷缺貨與囤貨問題
中文翻譯
---
name: "ecom-inventory-health"
description: "用週轉率、ABC 分類、安全庫存計算，以及缺貨與庫存過多的診斷，分析庫存健康度。當使用者需要最佳化庫存水位、降低持有成本、避免缺貨，或依庫存優先順序為商品分類時使用。就算使用者只說「我們庫存太多了」、「熱銷品老是缺貨」、「安全庫存要多少」或「該把心力放在哪些商品」也要用。"
metadata:
category: "WP-01 電商"
tags: ["e-commerce", "inventory", "supply-chain", "abc-analysis"]
---
# 庫存健康度分析
## 概述
庫存健康度要在兩種風險之間取得平衡：缺貨（流失銷售、客戶不滿）和庫存過多（持有成本、過時報廢）。這個 skill 提供衡量、分類和最佳化庫存水位的工具。
## 框架
```
IRON LAW: Not All SKUs Deserve Equal Attention
ABC classification shows that ~20% of SKUs drive ~80% of revenue.
Treat A-items (top 20% revenue) with tight control and frequent review.
C-items (bottom 50% revenue) get simple rules and less attention.
Equal treatment of all SKUs wastes resources on low-impact items.
```
### 關鍵指標
| 指標 | 公式 | 健康範圍 |
|--------|---------|--------------|
| **庫存週轉率** | COGS / 平均庫存 | 每年 4-12 次（依產業而異） |
| **庫存天數（DOI）** | 365 / 庫存週轉率 | 30-90 天 |
| **缺貨率** | 缺貨次數 / 總需求次數 | < 2-5% |
| **滿足率（Fill Rate）** | 完整出貨的訂單 / 總訂單 | > 95% |
| **持有成本** | 平均庫存 × 持有成本率（通常每年 20-30%） | 越低越好 |
| **呆滯庫存比例** | 6 個月以上零銷售的品項 / 總 SKU | < 10% |
### ABC 分類
| 等級 | 營收占比 | SKU 占比 | 策略 |
|-------|----------|-------|----------|
| **A** | 約 80% | 約 20% | 嚴格控管、頻繁檢視、最佳化安全庫存 |
| **B** | 約 15% | 約 30% | 適度控管、定期檢視 |
| **C** | 約 5% | 約 50% | 簡單規則、最小/最大庫存量、考慮淘汰 |
### 安全庫存計算
```
Safety Stock = Z × σ_d × √(Lead Time)
Where:
- Z = service level factor (1.65 for 95%, 2.33 for 99%)
- σ_d = standard deviation of daily demand
- Lead Time = supplier lead time in days
```
### 再訂購點
```
Reorder Point = (Average Daily Demand × Lead Time) + Safety Stock
```
### 診斷步驟
**階段 1：整體健康檢查**
- 計算整體庫存的週轉率和 DOI
- 跟產業基準比較
- 找出趨勢：在改善還是惡化？
**階段 2：ABC 分類**
- 依營收貢獻替所有 SKU 排名
- 分成 A/B/C
- 檢查：A 類商品庫存充足嗎？C 類商品是不是囤太多？
**階段 3：找出問題**
- **庫存過多**：DOI > 90 天、呆滯庫存 > 10%、持有成本上升
- **缺貨**：滿足率 < 95%、有流失銷售的紀錄、客訴
- **失衡**：A 類商品庫存不足，C 類商品卻庫存過多
**階段 4：最佳化**
- 依 ABC 等級設定安全庫存
- 對 A 類商品導入再訂購點
- 清掉呆滯庫存（折扣、組合包、捐贈）
- 透過跟供應商談判縮短前置時間
## 輸出格式
```markdown
# Inventory Health Report: {Business}
## Summary
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Turnover | {X}x | {X}x | 🟢/🟡/🔴 |
| DOI | {X} days | {X} days | 🟢/🟡/🔴 |
| Fill Rate | {X%} | >95% | 🟢/🟡/🔴 |
| Dead Stock | {X%} | <10% | 🟢/🟡/🔴 |
## ABC Distribution
| Class | SKUs | Revenue % | Avg DOI | Issue |
|-------|------|----------|---------|-------|
| A | {N} | {%} | {days} | {stockout risk?} |
| B | {N} | {%} | {days} | ... |
| C | {N} | {%} | {days} | {overstock?} |
## Top Issues
1. {issue with specific SKUs and data}
## Recommendations
1. {action with expected impact}
```
## 常見陷阱
- **季節性商品要另外處理**：1 月的泳裝會顯示成「呆滯庫存」，但不該清倉。要做季節調整，或依季節分開分析。
- **庫存週轉率因產業差異極大**：生鮮雜貨每年 20-50 次，時尚 4-6 次，電子產品 6-12 次。一定要在同產業內比較。
- **週轉低不一定不好，如果是刻意的**：策略性庫存（在漲價前先進貨、確保貨源）可以合理化較低的週轉。
- **ABC 分類會變動**：去年是 A 類的商品，今年可能變成 C 類。每季重新分類。
- **持有成本常被低估**：要包含倉庫租金、保險、過時報廢、資金成本（被綁住的錢的機會成本）、搬運人力。總計通常是每年庫存價值的 20-30%。
## 參考資料
- EOQ（經濟訂購量）模型，見 `references/eoq-model.md`
- 季節性需求預測，見 `references/seasonal-forecasting.md`英文原文
---
name: "ecom-inventory-health"
description: "Analyze inventory health using turnover ratios, ABC classification, safety stock calculations, and stockout vs overstock diagnostics. Use this skill when the user needs to optimize inventory levels, reduce carrying costs, prevent stockouts, or classify products by inventory priority — even if they say 'we have too much stock', 'we keep running out of bestsellers', 'how much safety stock do we need', or 'which products should we focus on'."
metadata:
category: "WP-01 電商"
tags: ["e-commerce", "inventory", "supply-chain", "abc-analysis"]
---
# Inventory Health Analysis
## Overview
Inventory health balances two risks: stockouts (lost sales, unhappy customers) and overstock (carrying costs, obsolescence). This skill provides tools to measure, classify, and optimize inventory levels.
## Framework
```
IRON LAW: Not All SKUs Deserve Equal Attention
ABC classification shows that ~20% of SKUs drive ~80% of revenue.
Treat A-items (top 20% revenue) with tight control and frequent review.
C-items (bottom 50% revenue) get simple rules and less attention.
Equal treatment of all SKUs wastes resources on low-impact items.
```
### Key Metrics
| Metric | Formula | Healthy Range |
|--------|---------|--------------|
| **Inventory Turnover** | COGS / Avg Inventory | 4-12x/year (industry-dependent) |
| **Days of Inventory (DOI)** | 365 / Inventory Turnover | 30-90 days |
| **Stockout Rate** | Stockout incidents / Total demand occasions | < 2-5% |
| **Fill Rate** | Orders filled completely / Total orders | > 95% |
| **Carrying Cost** | Avg Inventory × Carrying Cost % (typically 20-30%/year) | Minimize |
| **Dead Stock %** | Items with zero sales in 6+ months / Total SKUs | < 10% |
### ABC Classification
| Class | Revenue % | SKU % | Strategy |
|-------|----------|-------|----------|
| **A** | ~80% | ~20% | Tight control, frequent review, safety stock optimized |
| **B** | ~15% | ~30% | Moderate control, periodic review |
| **C** | ~5% | ~50% | Simple rules, min/max levels, consider dropping |
### Safety Stock Calculation
```
Safety Stock = Z × σ_d × √(Lead Time)
Where:
- Z = service level factor (1.65 for 95%, 2.33 for 99%)
- σ_d = standard deviation of daily demand
- Lead Time = supplier lead time in days
```
### Reorder Point
```
Reorder Point = (Average Daily Demand × Lead Time) + Safety Stock
```
### Diagnosis Steps
**Phase 1: Overall Health Check**
- Calculate turnover and DOI for total inventory
- Compare to industry benchmarks
- Identify trend: improving or deteriorating?
**Phase 2: ABC Classification**
- Rank all SKUs by revenue contribution
- Classify into A/B/C
- Check: are A-items well-stocked? Are C-items over-stocked?
**Phase 3: Problem Identification**
- **Overstock**: DOI > 90 days, dead stock > 10%, carrying costs rising
- **Stockout**: Fill rate < 95%, lost sales reports, customer complaints
- **Imbalance**: A-items understocked while C-items overstocked
**Phase 4: Optimization**
- Set safety stock by ABC class
- Implement reorder points for A-items
- Liquidate dead stock (discount, bundle, donate)
- Reduce lead times through supplier negotiation
## Output Format
```markdown
# Inventory Health Report: {Business}
## Summary
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Turnover | {X}x | {X}x | 🟢/🟡/🔴 |
| DOI | {X} days | {X} days | 🟢/🟡/🔴 |
| Fill Rate | {X%} | >95% | 🟢/🟡/🔴 |
| Dead Stock | {X%} | <10% | 🟢/🟡/🔴 |
## ABC Distribution
| Class | SKUs | Revenue % | Avg DOI | Issue |
|-------|------|----------|---------|-------|
| A | {N} | {%} | {days} | {stockout risk?} |
| B | {N} | {%} | {days} | ... |
| C | {N} | {%} | {days} | {overstock?} |
## Top Issues
1. {issue with specific SKUs and data}
## Recommendations
1. {action with expected impact}
```
## Gotchas
- **Seasonal products need separate treatment**: Swimsuits in January will show as "dead stock" but shouldn't be liquidated. Use seasonal adjustment or analyze by season.
- **Inventory turnover varies hugely by industry**: Grocery: 20-50x/year. Fashion: 4-6x. Electronics: 6-12x. Always benchmark within industry.
- **Low turnover ≠ bad if intentional**: Strategic inventory (buying ahead of price increases, securing supply) may justify lower turnover.
- **ABC classifications shift**: A product that was A-class last year may be C-class this year. Reclassify quarterly.
- **Carrying cost is often underestimated**: Include: warehouse rent, insurance, obsolescence, capital cost (opportunity cost of money tied up), handling labor. Total is typically 20-30% of inventory value per year.
## References
- For EOQ (Economic Order Quantity) model, see `references/eoq-model.md`
- For seasonal demand forecasting, see `references/seasonal-forecasting.md`