Industry-Level Forecasting and Volatility Analysis
- James Gifford
- Oct 8
- 2 min read
Updated: Oct 10
This analysis examines U.S. retail and food-service sales to assess forecast reliability, volatility, and demand risk across key sectors.
Written from the perspective of Nationwide Apparel Retailer Inc., the project models how external disruptions (such as COVID-19) affect apparel demand, recovery timing, and future planning scenarios.
The analysis demonstrates how structured data modeling and forecasting can help retail leaders quantify uncertainty, plan for demand shocks, and strengthen strategic decisions under volatility.
📊 Business Questions
Identify long-term retail trends after removing seasonal effects
Measure volatility and forecast uncertainty for different sectors
Model supply and demand shock scenarios
Connect statistical findings to real business strategies
🔍 Findings & Takeaways
Clothing and Accessories had the sharpest swings during COVID but smoothing improved forecast stability.
Food Service remained the most volatile and works as an early signal of consumer confidence.
Department Stores were the most resilient and useful as a benchmark for overall retail health.
Even small 5-10% shocks in supply or demand had a large impact on forecasted revenue, proving why scenario planning matters.
📘 Project Files
Explore the full analysis below:
🔗 View on GitHub
Includes SQL queries, workflow documentation, and visual outputs
📄 Download Full Report (PDF)
Complete report with visuals, tables, and business insights
📊 Download Forecast Workbook (Excel)
Interactive calculator with all charts, calculations, and scenario modeling tools
💡 Business Impact
This project connects forecasting methods with real retail planning needs. It provides a clear framework for evaluating uncertainty, measuring sector-level volatility, and supporting data-driven decisions for inventory and marketing strategy.
The accompanying Excel workbook helps retailers and analysts compare their internal sales trends with national volatility benchmarks, making it easier to see whether performance aligns with or diverges from broader market conditions.
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