mcpField
E
ecom-inventory-health

ecom-inventory-health

github.com/asgard-ai-platformData & databases24336 installsUpdated 21w ago
Repository

About ecom-inventory-health

ecom-inventory-health is a skill for e-commerce inventory analysis, helping assess stock health, identify shortages or excess, and optimize inventory levels. It combines ABC classification, inventory metrics, and replenishment calculations to guide SKU-specific decisions.

Key features

  1. 01Calculate turnover and days of inventory
  2. 02Classify SKUs by revenue contribution
  3. 03Calculate safety stock and reorder points
  4. 04Identify overstock, stockouts, and imbalance
  5. 05Generate inventory health reports

Use cases

  1. 01Review overall inventory performance and trends
  2. 02Prioritize stock for high-revenue SKUs
  3. 03Identify and address dead stock
  4. 04Set reorder points using demand and lead times

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/asgard-ai-platform/skills --skill ecom-inventory-health

After installing, type/ecom-inventory-healthin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

examples/
sample_scenario.md
references/
eoq-model.md
seasonal-forecasting.md

Details

From the project README

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
MetricFormulaHealthy Range
Inventory TurnoverCOGS / Avg Inventory4-12x/year (industry-dependent)
Days of Inventory (DOI)365 / Inventory Turnover30-90 days
Stockout RateStockout incidents / Total demand occasions< 2-5%
Fill RateOrders filled completely / Total orders> 95%
Carrying CostAvg Inventory × Carrying Cost % (typically 20-30%/year)Minimize
Dead Stock %Items with zero sales in 6+ months / Total SKUs< 10%
ABC Classification
ClassRevenue %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

# 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

Comments

Markdown supported