Inventory Demand Forecasting Assistant

Expert MULTI-STEP WORKFLOW Cost-reduction

Use this prompt to

Build an agentic workflow that analyzes 12 months of sales data, seasonal patterns, and supplier lead times to generate a rolling 90-day demand forecast with reorder alerts and safety stock recommendations.

Pro tip

Export sales data as CSV and include columns for date, SKU, units sold, and any promotional flags. For best results, pair with a Code Interpreter session to generate visual trend charts.

inventory forecasting demand planning supply chain reorder

Prompt Variants by Model

Claude Claude 4.x
FRESH APR 2026
You are a supply chain analyst. I will provide 12 months of sales data as a CSV. Build a comprehensive demand forecast.

<business_context>
Business type: [INDUSTRY]
Number of SKUs: [COUNT]
Average...
You are a supply chain analyst. I will provide 12 months of sales data as a CSV. Build a comprehensive demand forecast.

<business_context>
Business type: [INDUSTRY]
Number of SKUs: [COUNT]
Average...

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You are a supply chain analyst. I will provide 12 months of sales data as a CSV. Build a comprehensive demand forecast.

<business_context>
Business type: [INDUSTRY]
Number of SKUs: [COUNT]
Average supplier lead time: [DAYS]
Seasonal patterns: [DESCRIBE ANY KNOWN SEASONALITY]
Upcoming promotions: [LIST ANY PLANNED PROMOS WITH DATES]
</business_context>

Analyze the attached data and produce:
1. **Trend analysis:** Overall sales trajectory, month-over-month growth rates
2. **Seasonality detection:** Identify recurring patterns and quantify their impact
3. **90-day rolling forecast:** Weekly demand projections for the next 90 days, per SKU category
4. **Reorder alerts:** Flag any SKU where projected demand will exceed current inventory within lead time
5. **Safety stock recommendations:** Calculate recommended safety stock levels using a 95% service level
6. **Promotional impact modeling:** Estimate demand uplift for planned promotions based on historical promo data

Output the forecast as a structured table. Flag high-confidence vs. low-confidence predictions. Highlight the top 5 SKUs by revenue risk (highest chance of stockout).
Notes: Upload the CSV directly. For visualization, ask Claude to generate Python code for charts in an Artifact.

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