Demand Forecasting Analysis Assistant
Validate demand forecasts with statistical rigor and scenario analysis
What You Can Do
You can validate your forecasting methodologies to ensure statistical soundness, analyze historical demand patterns to identify trends and seasonality, detect anomalies that signal supply chain disruptions or market shifts, and stress-test your forecasts across multiple scenarios. This helps you improve forecast accuracy, reduce demand planning risk, and make confident decisions on inventory, procurement, and resource allocation.
Features
Review your forecasting approach (exponential smoothing, ARIMA, regression, machine learning) against best practices and your data characteristics
Identify trend components, seasonal cycles, cyclical patterns, and structural breaks in your historical demand data
Spot unusual demand spikes, drops, or outliers that indicate supply chain events, promotions, or market disruptions
Calculate MAPE, RMSE, MAE, and bias to quantify how well your forecasts perform against actuals
Model demand under optimistic, baseline, and pessimistic scenarios to stress-test your plans and identify risks
Investigate forecast errors and anomalies to understand what drove misalignment and how to prevent recurrence
Receive actionable suggestions to adjust parameters, switch methods, incorporate new signals, or refine your forecast process
Example Output
Input: You upload 24 months of weekly demand data for SKU-XYZ and share your current exponential smoothing forecast.
Output:
- Methodology assessment: Your method is appropriate for data with moderate seasonality but may underfit demand spikes during promotional periods
- Pattern summary: 12-week seasonal cycle (summer peaks), 8% YoY trend growth, three significant anomalies in weeks 8, 15, and 22
- Accuracy check: Current MAPE of 12.3%, RMSE of 450 units. Bias trending positive (forecast runs low by 3%)
- Scenario forecasts: Baseline 5,200 units next quarter; upside scenario (new market entry) 6,800 units; downside scenario (competitor promo) 3,900 units
- Recommendations: Incorporate promotional calendar as an exogenous variable; test SARIMA for better seasonal fit; investigate week-22 anomaly (supply disruption?)
What's Included
- Statistical Audit Framework: Checklist-driven review of your data quality, forecasting methodology selection, and parameter choices
- Time Series Decomposition: Separation of trend, seasonal, and residual components to understand what is driving your demand patterns
- Accuracy Benchmarking: Side-by-side comparison of your forecast performance against baseline methods (naive, average, seasonal naive)
- Scenario Planning Templates: Structured prompts to define optimistic, baseline, and pessimistic scenarios with quantified assumptions
- Error Diagnostic Report: Detailed breakdown of forecast bias, spike errors, and systematic misses by time period or product segment
Who It's For
- Demand Planners
- Supply Chain Managers
- Business Analysts and Forecasting Teams
- Operations Managers
- Finance and Revenue Planning Leaders
Best For
- Improving forecast accuracy and reducing planning error
- Validating new forecasting methodologies before implementation
- Root cause analysis of forecast misses and anomalies
- Stress-testing plans across demand scenarios and market conditions
- Building data-driven business cases for supply chain investments
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