Inventory Optimization
Right-size inventory across the network to maximize service levels while minimizing carrying costs, obsolescence risk, and working capital consumption.
Input Gathering
| Input |
Description |
Required |
| SKU master data |
Product list with unit cost, lead time, shelf life |
Yes |
| Demand history |
Minimum 12 months of consumption or sales data |
Yes |
| Current stock positions |
On-hand, in-transit, on-order by location |
Yes |
| Target service level |
Fill rate or cycle service level (e.g., 95%, 98%) |
Yes |
| Lead time data |
Supplier lead times with variability |
Yes |
| Cost parameters |
Holding cost %, ordering cost per PO, stockout penalty cost |
No |
| Storage constraints |
Warehouse capacity, shelf life limits, lot-size restrictions |
No |
Step-by-Step Process
Step 1 — ABC/XYZ Classification
Segment the portfolio on two dimensions:
ABC (Value) — based on annual revenue or cost of goods sold:
| Class |
Share of Revenue |
Typical SKU % |
Management Approach |
| A |
~80% |
~20% |
Tight control, frequent review |
| B |
~15% |
~30% |
Moderate control, periodic review |
| C |
~5% |
~50% |
Simple rules, exception-based |
XYZ (Variability) — based on coefficient of variation (CV) of demand:
| Class |
CV Range |
Demand Pattern |
Forecast Reliability |
| X |
0 - 0.5 |
Stable |
High |
| Y |
0.5 - 1.0 |
Variable |
Medium |
| Z |
> 1.0 |
Erratic |
Low |
Create a 3x3 matrix (AX, AY, AZ, BX, BY, BZ, CX, CY, CZ) and assign inventory policies per cell.
Step 2 — Safety Stock Calculation
Calculate safety stock using the appropriate method for each segment:
| Segment |
Method |
Formula |
| AX, BX |
Statistical |
SS = Z x sqrt(LT x sigma_d^2 + d_avg^2 x sigma_LT^2) |
| AY, BY |
Statistical + buffer |
SS = Z x sigma_demand_during_LT x (1 + buffer%) |
| AZ, BZ |
Simulation / min-max |
Run Monte Carlo on demand and lead time distributions |
| CX |
Fixed weeks of supply |
SS = avg_weekly_demand x weeks_target |
| CY, CZ |
Min-max with high max |
Set min = max observed demand during LT x 1.2 |
Where:
- Z = service level Z-score (e.g., 1.65 for 95%, 2.05 for 98%)
- LT = average lead time
- sigma_d = standard deviation of demand
- sigma_LT = standard deviation of lead time
Step 3 — Reorder Point and Order Quantity
- Reorder Point (ROP) = Average demand during lead time + Safety stock.
- Economic Order Quantity (EOQ) = sqrt(2 x D x S / H), where D = annual demand, S = ordering cost, H = holding cost per unit.
- Apply practical constraints: minimum order quantity (MOQ), pack sizes, container utilization, shelf-life limits.
- For expensive A-items, consider lot-for-lot ordering to minimize holding cost.
Step 4 — Carrying Cost Analysis
Build a full cost-to-carry model:
| Cost Component |
Typical Range |
Calculation Basis |
| Capital / opportunity |
8-15% |
WACC x average inventory value |
| Warehousing |
2-5% |
Space, labor, utilities |
| Insurance |
0.5-1% |
Insured value |
| Obsolescence / shrinkage |
2-10% |
Historical write-off rate |
| Total carrying cost |
15-30% |
Sum of above |
Compare current vs. proposed inventory investment and quantify savings.
Step 5 — Stockout Risk Assessment
For each SKU-location, calculate:
- Probability of stockout per cycle = 1 - Service Level achieved.
- Expected stockout units = integral of demand distribution beyond ROP during lead time.
- Revenue at risk = Expected stockout units x unit price x (1 - substitution rate).
- Customer impact score = Revenue at risk x customer criticality weight.
Rank SKUs by customer impact score and flag the top decile for immediate action.
Step 6 — Policy Recommendations and Monitoring
- Set inventory policies (ROP, safety stock, review period) per ABC/XYZ segment.
- Define exception triggers: days of supply < threshold, excess > X weeks, slow-moving flag.
- Create a dashboard cadence: A-items reviewed weekly, B-items biweekly, C-items monthly.
- Establish an obsolescence review quarterly for items with < 60 days shelf life remaining or > 12 months of supply.
Output Format
## Inventory Optimization Report — [Scope] — [Date]
### Executive Summary
- Total SKUs analyzed: [count]
- Current inventory value: [$X]
- Recommended inventory value: [$Y]
- Projected savings: [$Z] ([%] reduction)
- Service level impact: [current] -> [projected]
### ABC/XYZ Classification Summary
| Segment | SKU Count | Inventory Value | Revenue Share | Recommended Policy |
|---------|-----------|-----------------|---------------|--------------------|
| AX | ... | ... | ... | ... |
| AY | ... | ... | ... | ... |
| ... | ... | ... | ... | ... |
### Safety Stock and Reorder Points (Top SKUs)
| SKU | Current SS | Proposed SS | ROP | EOQ | Days of Supply |
|-----|-----------|-------------|-----|-----|----------------|
| ... | ... | ... | ... | ... | ... |
### Stockout Risk Register
| SKU | Stockout Probability | Revenue at Risk | Impact Score | Action |
|-----|----------------------|-----------------|--------------|--------|
| ... | ... | ... | ... | ... |
### Recommendations
1. [Specific, actionable recommendations]
2. ...
### Implementation Roadmap
- [Week 1-2]: ...
- [Week 3-4]: ...
Quality Checklist
Edge Cases
- New SKU with no demand history: Use analogous product demand profile; set conservative safety stock at 1.5x expected lead time demand until 3 months of actuals accumulate.
- Highly seasonal products: Calculate safety stock using within-season variability only; pre-build inventory using seasonal index.
- Perishable goods with short shelf life: Add shelf-life constraint to EOQ; reduce max stock to shelf-life minus lead time; implement FEFO (First Expired, First Out).
- Long and variable lead times (e.g., overseas sourcing): Use lead-time demand distribution from simulation; consider dual sourcing to reduce effective variability.
- Vendor-managed inventory (VMI): Shift analysis to consumption-based replenishment; share demand signals directly with supplier.
- Promotional spikes: Separate base and promotional demand; plan promotional inventory as a one-time forward buy outside the standard safety stock framework.
1---2name: inventory-optimization3description: Optimize inventory levels through safety stock calculation, reorder points, ABC/XYZ classification, carrying cost analysis, and stockout risk assessment. TRIGGER when: user says /inventory-optimization, "optimize inventory", "safety stock", "reorder point", "inventory analysis", "stock levels".4---56# Inventory Optimization78Right-size inventory across the network to maximize service levels while minimizing carrying costs, obsolescence risk, and working capital consumption.910---1112## Input Gathering1314| Input | Description | Required |15|------------------------------|-------------------------------------------------------------|----------|16| SKU master data | Product list with unit cost, lead time, shelf life | Yes |17| Demand history | Minimum 12 months of consumption or sales data | Yes |18| Current stock positions | On-hand, in-transit, on-order by location | Yes |19| Target service level | Fill rate or cycle service level (e.g., 95%, 98%) | Yes |20| Lead time data | Supplier lead times with variability | Yes |21| Cost parameters | Holding cost %, ordering cost per PO, stockout penalty cost | No |22| Storage constraints | Warehouse capacity, shelf life limits, lot-size restrictions | No |2324---2526## Step-by-Step Process2728### Step 1 — ABC/XYZ Classification2930Segment the portfolio on two dimensions:3132**ABC (Value)** — based on annual revenue or cost of goods sold:3334| Class | Share of Revenue | Typical SKU % | Management Approach |35|-------|-----------------|---------------|---------------------------|36| A | ~80% | ~20% | Tight control, frequent review |37| B | ~15% | ~30% | Moderate control, periodic review |38| C | ~5% | ~50% | Simple rules, exception-based |3940**XYZ (Variability)** — based on coefficient of variation (CV) of demand:4142| Class | CV Range | Demand Pattern | Forecast Reliability |43|-------|-----------|-----------------|----------------------|44| X | 0 - 0.5 | Stable | High |45| Y | 0.5 - 1.0 | Variable | Medium |46| Z | > 1.0 | Erratic | Low |4748Create a 3x3 matrix (AX, AY, AZ, BX, BY, BZ, CX, CY, CZ) and assign inventory policies per cell.4950### Step 2 — Safety Stock Calculation5152Calculate safety stock using the appropriate method for each segment:5354| Segment | Method | Formula |55|---------|--------|---------|56| AX, BX | Statistical | SS = Z x sqrt(LT x sigma_d^2 + d_avg^2 x sigma_LT^2) |57| AY, BY | Statistical + buffer | SS = Z x sigma_demand_during_LT x (1 + buffer%) |58| AZ, BZ | Simulation / min-max | Run Monte Carlo on demand and lead time distributions |59| CX | Fixed weeks of supply | SS = avg_weekly_demand x weeks_target |60| CY, CZ | Min-max with high max | Set min = max observed demand during LT x 1.2 |6162Where:63- Z = service level Z-score (e.g., 1.65 for 95%, 2.05 for 98%)64- LT = average lead time65- sigma_d = standard deviation of demand66- sigma_LT = standard deviation of lead time6768### Step 3 — Reorder Point and Order Quantity6970- **Reorder Point (ROP)** = Average demand during lead time + Safety stock.71- **Economic Order Quantity (EOQ)** = sqrt(2 x D x S / H), where D = annual demand, S = ordering cost, H = holding cost per unit.72- Apply practical constraints: minimum order quantity (MOQ), pack sizes, container utilization, shelf-life limits.73- For expensive A-items, consider lot-for-lot ordering to minimize holding cost.7475### Step 4 — Carrying Cost Analysis7677Build a full cost-to-carry model:7879| Cost Component | Typical Range | Calculation Basis |80|-------------------------|---------------|---------------------------|81| Capital / opportunity | 8-15% | WACC x average inventory value |82| Warehousing | 2-5% | Space, labor, utilities |83| Insurance | 0.5-1% | Insured value |84| Obsolescence / shrinkage| 2-10% | Historical write-off rate |85| **Total carrying cost** | **15-30%** | Sum of above |8687Compare current vs. proposed inventory investment and quantify savings.8889### Step 5 — Stockout Risk Assessment9091For each SKU-location, calculate:9293- **Probability of stockout per cycle** = 1 - Service Level achieved.94- **Expected stockout units** = integral of demand distribution beyond ROP during lead time.95- **Revenue at risk** = Expected stockout units x unit price x (1 - substitution rate).96- **Customer impact score** = Revenue at risk x customer criticality weight.9798Rank SKUs by customer impact score and flag the top decile for immediate action.99100### Step 6 — Policy Recommendations and Monitoring101102- Set inventory policies (ROP, safety stock, review period) per ABC/XYZ segment.103- Define exception triggers: days of supply < threshold, excess > X weeks, slow-moving flag.104- Create a dashboard cadence: A-items reviewed weekly, B-items biweekly, C-items monthly.105- Establish an obsolescence review quarterly for items with < 60 days shelf life remaining or > 12 months of supply.106107---108109## Output Format110111```112## Inventory Optimization Report — [Scope] — [Date]113114### Executive Summary115- Total SKUs analyzed: [count]116- Current inventory value: [$X]117- Recommended inventory value: [$Y]118- Projected savings: [$Z] ([%] reduction)119- Service level impact: [current] -> [projected]120121### ABC/XYZ Classification Summary122| Segment | SKU Count | Inventory Value | Revenue Share | Recommended Policy |123|---------|-----------|-----------------|---------------|--------------------|124| AX | ... | ... | ... | ... |125| AY | ... | ... | ... | ... |126| ... | ... | ... | ... | ... |127128### Safety Stock and Reorder Points (Top SKUs)129| SKU | Current SS | Proposed SS | ROP | EOQ | Days of Supply |130|-----|-----------|-------------|-----|-----|----------------|131| ... | ... | ... | ... | ... | ... |132133### Stockout Risk Register134| SKU | Stockout Probability | Revenue at Risk | Impact Score | Action |135|-----|----------------------|-----------------|--------------|--------|136| ... | ... | ... | ... | ... |137138### Recommendations1391. [Specific, actionable recommendations]1402. ...141142### Implementation Roadmap143- [Week 1-2]: ...144- [Week 3-4]: ...145```146147---148149## Quality Checklist150151- [ ] Demand data covers at least 12 months and is cleansed of stockout periods152- [ ] ABC/XYZ classification completed and validated with stakeholders153- [ ] Safety stock method appropriate for each segment's demand pattern154- [ ] Lead time variability captured (not just averages)155- [ ] Carrying cost rate reflects actual company cost structure156- [ ] Stockout cost includes both direct and downstream impact157- [ ] MOQ, lot size, and shelf-life constraints applied to order quantities158- [ ] Exception triggers defined for proactive monitoring159- [ ] Recommendations quantified with expected financial impact160- [ ] Review cadence established per segment161162---163164## Edge Cases165166- **New SKU with no demand history**: Use analogous product demand profile; set conservative safety stock at 1.5x expected lead time demand until 3 months of actuals accumulate.167- **Highly seasonal products**: Calculate safety stock using within-season variability only; pre-build inventory using seasonal index.168- **Perishable goods with short shelf life**: Add shelf-life constraint to EOQ; reduce max stock to shelf-life minus lead time; implement FEFO (First Expired, First Out).169- **Long and variable lead times (e.g., overseas sourcing)**: Use lead-time demand distribution from simulation; consider dual sourcing to reduce effective variability.170- **Vendor-managed inventory (VMI)**: Shift analysis to consumption-based replenishment; share demand signals directly with supplier.171- **Promotional spikes**: Separate base and promotional demand; plan promotional inventory as a one-time forward buy outside the standard safety stock framework.
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