Strum AI
Free Supply Chain Analytics Tools

Data-Driven Supply Chain Diagnostics

Get a quick assessment of your forecast process, quality of human overrides, demand volatility and identify opportunities to optimize your Planning strategy
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Hands-Off-The-Wheel Score

Quantify the value (or waste) of human forecast overrides

  • Compare system vs. human forecast accuracy
  • Calculate override frequency and impact
  • Get automation opportunity score (0-100)
  • Identify where planners add value vs. noise
Entropy Scanner

Supply Chain Entropy Scanner

Visualize demand volatility and forecastability by SKU

  • Calculate Coefficient of Variation (CV) & Average Demand Interval (ADI)
  • Classify SKUs: Smooth, Intermittent, Erratic, Lumpy
  • Get Forecastability Score per SKU (0-100)
  • Identify which items need buffer-based strategies

🎯 Hands-Off-The-Wheel Score

Quantify the value (or waste) of human forecast overrides

Upload Your Data

Upload a CSV or Excel file with three columns: System Forecast, Human Forecast, and Actuals (any number of periods).

📤
Click to upload or drag and drop
CSV or Excel file (max 10MB)
🔒 Your data is secure: All analysis happens in your browser. We never store or transmit your data.

Analysis Results

HOTW Score
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Automation opportunity (0-100)
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Override Rate
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of forecasts modified
System MAPE
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baseline error
Human MAPE
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after overrides

📊 Recommendation

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What This Means

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Supply Chain Entropy Scanner Supply Chain Entropy Scanner

Classify demand patterns and identify which SKUs need forecasting vs. buffer-based strategies

Upload Your Data

Upload a CSV or Excel file with columns for SKU and Demand over time.

📤
Click to upload or drag and drop
CSV or Excel file (max 10MB)
🔒 Your data is secure: All analysis happens in your browser. We never store or transmit your data.

Analysis Results

Overall Forecastability
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Total SKUs
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analyzed
Smooth SKUs
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easy to forecast
Intermittent SKUs
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sporadic demand
Erratic SKUs
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variable demand
Lumpy SKUs
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need buffers

📊 Demand Pattern Classification

The chart above plots your SKUs based on two key metrics: Coefficient of Variation (CV) and Average Demand Interval (ADI).

🟢 SMOOTH Regular demand, low variance. Best for traditional forecasting methods.
🔵 INTERMITTENT Sporadic demand, low variance. Use Croston's method or similar approaches.
🟡 ERRATIC Regular but highly variable. Requires advanced forecasting with safety stock.
🔴 LUMPY Sporadic and variable. Focus on buffer strategies, not forecasts.

📐 How We Calculate This

Coefficient of Variation (CV)

Measures demand variability relative to average demand.

CV = Standard Deviation ÷ Mean

Lower CV (<0.5): Stable demand
Higher CV (≥0.5): Volatile demand

Average Demand Interval (ADI)

Measures how frequently demand occurs.

ADI = Total Periods ÷ Periods with Demand

Lower ADI (<1.32): Frequent demand
Higher ADI (≥1.32): Intermittent/sporadic demand