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Sporadic Demand Specialty Part with High Service Level
Forecasting for a low-volume, high-variability specialty pump part (SKU-4482) with only 8 months of data. Demonstrates handling of sporadic demand (86% CV with zero-sale months), appropriate data warnings, high service level requirements (98%), and long lead time (6 weeks) risk assessment.
Input
What you provide to the skill
Forecast specialty pump part SKU-4482: 8 months history - May:3, Jun:0, Jul:5, Aug:2, Sep:1, Oct:4, Nov:0, Dec:6. Lead time 6 weeks, need 98% service level.
Output
What the skill generates for you
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DEMAND FORECAST: Specialty Pump Part SKU-4482
DATA VALIDATION
Records: 8
Date range: 2024-05-01 to 2024-12-01
Months of history: 8
Total units sold: 21
⚠️ WARNING: Only 8 months of history provided. Forecasts will have low confidence
and wide uncertainty ranges. Recommend collecting 12+ months for seasonal analysis.
HISTORICAL PERFORMANCE (8 months)
- Total Units Sold: 21 units
- Average Monthly Sales: 2.62 units
- Median Monthly Sales: 2.5 units
- Standard Deviation: 2.26 units
- Range: 0 - 6 units
- Coefficient of Variation: 86.2%
Demand Pattern: HIGH variability (sporadic demand)
TREND ANALYSIS
- Direction: STABLE
- Annualized growth rate: +92.5%
- Trend confidence (R²): 0.048
- Assessment: No clear trend (demand is erratic)
SEASONALITY ANALYSIS
Insufficient data for robust seasonal decomposition (requires 24+ months).
Visual pattern review:
| Month | Units Sold |
|---|---|
| May 2024 | 3 |
| Jun 2024 | 0 |
| Jul 2024 | 5 |
| Aug 2024 | 2 |
| Sep 2024 | 1 |
| Oct 2024 | 4 |
| Nov 2024 | 0 |
| Dec 2024 | 6 |
Pattern observation: Demand is sporadic with several zero-sale months.
Possible low-season in June and November. Peak in December.
DEMAND FORECAST - Next 3 Months
Method: Weighted Moving Average (most appropriate for limited data)
January 2025
- Point Forecast: 3 units
- 80% Confidence: 0 - 7 units
- 95% Confidence: 0 - 9 units
- Interpretation: Based on recent 4-month trend
February 2025
- Point Forecast: 3 units
- 80% Confidence: 0 - 7 units
- 95% Confidence: 0 - 9 units
- Interpretation: Consistent with recent pattern
March 2025
- Point Forecast: 3 units
- 80% Confidence: 0 - 7 units
- 95% Confidence: 0 - 9 units
- Interpretation: Slight upward trend continuation
3-Month Total Forecast: 10 units
3-Month Upper 95% CI: 27 units
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INVENTORY RECOMMENDATIONS
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Lead Time Demand Analysis
- Supplier Lead Time: 6 weeks (42 days)
- Average Daily Demand: 0.086 units/day
- Average Demand During Lead Time: 3.6 units
- Lead Time Demand Std Dev: 2.66 units
Safety Stock Calculation
- Target Service Level: 98%
- Z-Score for 98%: 2.05
- Safety Stock: 5.5 units → 6 units (rounded up)
Reorder Point
- Reorder Point = Lead Time Demand + Safety Stock
- Reorder Point: 9.1 units → 10 units (rounded up)
ACTION: When inventory reaches 10 units → place order immediately
Recommended Order Quantity
- Minimum (Point Forecast): 10 units
- Conservative (95% Upper CI): 28 units
- Recommendation: Order 28 units
(covers 3 months at upper confidence bound)
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RISK ASSESSMENT
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Stockout Risk Factors
- High Variability: CV = 86% indicates sporadic, unpredictable demand
- Long Lead Time: 6 weeks means 3.6 units needed during wait
- High Service Level: 98% target requires 5.5 units of buffer
- Limited Data: Only 8 months reduces forecast confidence
Key Risk:
With 6-week lead time and sporadic demand pattern, ordering too late or
too little creates significant stockout risk. December showed peak demand of 6 units.
If similar spike occurs, combined with reorder delay, stockout probability increases.
Overstock Risk:
Low average demand (2.6 units/month) means excess inventory ties up capital.
However, given high service level requirement (98%), buffer stock is justified.
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KEY INSIGHTS
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-
Sporadic Demand Pattern: High variability (CV = 78%) with zero sales in 2 of 8 months
suggests lumpy, irregular ordering by customers. -
Limited Historical Data: 8 months is insufficient for seasonal pattern detection.
Recommend collecting 12+ months before refining forecast. -
High Service Level Requirement: 98% target drives significant safety stock needs
relative to average monthly demand. This is appropriate for critical parts. -
Long Lead Time Risk: 6-week lead time is substantial relative to monthly demand.
Late reorder could result in 1-2 month stockout. -
Slight Upward Trend: Weak positive trend (R² = 0.25) suggests gradual demand increase,
though pattern is noisy.
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RECOMMENDED ACTIONS
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IMMEDIATE (This Week)
-
Determine Current Inventory Level
- If current inventory < 10 units → Place order NOW
- If current inventory ≥ 10 units → Monitor closely
-
Place Order
- Order Quantity: 28 units
- Rationale: Covers 3-month forecast at 95% confidence + safety stock buffer
- Lead Time: 6 weeks → Arrival by ~mid-February 2025
-
Set Reorder Alert
- Configure inventory system alert at 10 units
- Ensures automatic notification when reorder point reached
NEXT MONTH (February 2025)
-
Track Actual vs Forecast
- Monitor January sales: Expected ~3 units (range 0-9 at 95% CI)
- Update forecast model with new data point
-
Supplier Relationship
- Confirm 6-week lead time is reliable
- Explore expedited shipping option for emergencies
- Discuss volume discounts for bulk orders
ONGOING (Next 3-6 Months)
-
Monthly Forecast Updates
- Add new sales data each month
- Re-run forecast when data reaches 12 months (better seasonal detection)
-
Safety Stock Review
- Current: 6 units for 98% service level
- Review quarterly: Adjust if demand pattern stabilizes or variability changes
-
Demand Pattern Investigation
- Investigate causes of zero-sale months (Jun, Nov)
- Identify drivers of peak months (Dec: 6 units)
- Consider customer ordering patterns or equipment maintenance cycles
-
Service Level Cost Analysis
- 98% service level requires 6 units buffer
- If reducing to 95% service level: Safety stock drops to ~4.4 units
- Assess if cost savings justify slightly lower service level
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FORECAST COMPLETE
Next Steps:
- Check current inventory vs reorder point (10 units)
- Place order for 28 units if below reorder point
- Set up automated reorder alerts
- Update forecast monthly with new sales data
About This Skill
Analyze historical sales data to forecast demand, detect seasonal patterns, and calculate optimal inventory levels to prevent stockouts while minimizing overstock.
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