Demand Forecasting & Inventory Optimization

Pro v1.0.0 1 view

Analyze historical sales data to forecast demand, detect seasonal patterns, and calculate optimal inventory levels to prevent stockouts while minimizing overstock.

What You Get

Transform raw sales data into actionable forecasts with confidence intervals, optimal reorder points, and specific ordering recommendations that reduce stockouts and excess inventory.

The Problem

Inventory managers and business owners struggle to predict demand accurately, leading to costly stockouts that lose sales or excess inventory that ties up capital. Without statistical analysis, it's difficult to determine optimal reorder points, safety stock levels, or identify seasonal patterns that affect ordering decisions.

The Solution

This skill analyzes historical sales CSV files using statistical time series methods to generate demand forecasts with confidence intervals, detect seasonal patterns and trends, calculate optimal safety stock and reorder points based on lead time and service level targets, and provide specific ordering recommendations with timing. It handles seasonal products, new product trends, and multi-SKU portfolio optimization.

How It Works

  1. 1 Validate sales data CSV and assess data quality (warn if <6 months history)
  2. 2 Perform exploratory analysis calculating key statistics and demand variability
  3. 3 Detect seasonality using time series decomposition and calculate seasonal indices
  4. 4 Analyze trends using linear regression to identify growth or decline patterns
  5. 5 Generate forecasts using appropriate method (Holt-Winters, exponential smoothing, or moving average)
  6. 6 Calculate optimal safety stock and reorder points based on lead time and service level
  7. 7 Assess stockout and overstock risks with quantified probabilities
  8. 8 Provide specific ordering recommendations organized by timing (immediate, upcoming, strategic)

What You'll Need

  • Historical sales CSV with date and quantity columns (minimum 6 months, 12+ months recommended)
  • Product/SKU information (name or identifier)
  • Supplier lead time in days or weeks
  • Target service level percentage (optional, defaults to 95%)
  • Python 3 with pandas, numpy, scipy, and statsmodels libraries

Get This Skill

Requires Pro subscription ($9/month)

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Examples

Seasonal Outdoor Grill Inventory Planning

Forecasting demand for a highly seasonal product (outdoor grills) with 12 months of history. Demonstrates detection of strong seasonality (12x peak-to-trough ratio), calculation of season-specific safety stock levels, and critical timing for peak season ordering with 4-week lead time.

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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.

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Growing Product with Strong Trend Analysis

Inventory optimization for Widget-X showing 95% year-over-year growth with 9 months of data. Demonstrates trend detection with high R² confidence, current inventory risk assessment (180 units on hand), and proactive ordering recommendations for continued growth trajectory.

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