Forecast Issues

This guide helps resolve problems with demand forecasting.

Common Forecast Problems

Forecasts Showing Zero

Cause 1: No sales history

Products without sales cannot be forecasted.

Solution:

• Wait until product has sales
• Forecasts will generate as data accumulates

Cause 2: New product

Recently added products lack historical data.

Solution:

• Allow 14+ days of sales data
• Monitor product manually until forecasts develop

Cause 3: Inventory tracking not enabled

Products must have inventory tracking for forecasting.

Solution:

1. Enable inventory tracking in Shopify
2. Wait for sync
3. Forecasts will generate as sales occur

Forecasts Seem Inaccurate

Cause 1: Insufficient history

Solution:

• Wait for more data to accumulate
• Use higher safety stock in the meantime

Cause 2: Unusual events

Promotions, viral moments, or stockouts skew data.

Solution:

• Allow time for forecasts to normalize
• Adjust safety stock temporarily

Cause 3: Seasonal products

Seasonal patterns need full cycle data.

Solution:

• First season forecasts will be limited
• Accuracy improves after full cycle

Cause 4: Wrong threshold settings

Incorrect lead time or safety stock affects recommendations.

Solution:

1. Review settings
2. Compare to actual supplier times
3. Adjust as needed

Daily Consumption Seems Wrong

Cause 1: Recent changes

Sales velocity can change quickly.

Solution:

• Recent data weighted more heavily
• Wait for patterns to stabilize

Cause 2: Stockout periods

If product was out of stock, sales were artificially zero.

Solution:

• Understand context
• Forecasts will adjust as normal sales resume

Cause 3: Calculation period

Consumption is averaged over historical period.

Solution:

• Check what period is being used
• Consider if it's representative

Reorder Recommendations Wrong

Cause 1: Lead time setting

Incorrect lead time causes timing issues.

Solution:

1. Verify actual supplier lead time
2. Update Lead Time in settings
3. Include all delivery steps

Cause 2: Safety stock setting

Too low = stockout risk Too high = overstock risk

Solution:

• Review safety stock days
• Adjust based on experience

Cause 3: Consumption rate changes

If sales velocity changed, recommendations may lag.

Solution:

• Wait for forecasts to adapt
• Manually adjust if urgent

Improving Forecast Quality

Ensure Data Quality

• Accurate Shopify inventory
• Completed orders properly recorded
• Consistent order processing

Provide Adequate History

• More data = better forecasts
• 30+ days recommended
• Full season for seasonal items

Set Realistic Thresholds

• Use actual lead times
• Appropriate safety stock
• Review settings periodically

Monitor and Adjust

• Compare forecast vs actual
• Adjust settings based on results
• Be patient with new products

Understanding Forecast Limitations

Forecasts are based on historical patterns. They don't account for:

• Planned promotions
• Marketing campaigns
• Competitor actions
• Supply disruptions
• Economic changes

Consider these factors when making final decisions.

When Forecasts Won't Help

Some situations are difficult to forecast:

• Brand new products (no history)
• One-time/unique items
• Highly irregular demand
• Products with few sales

For these, rely on:

• Manual judgment
• Higher safety stock
• Frequent monitoring

Getting Help

If forecast issues persist:

1. Review this guide
2. Check Forecast Accuracy concepts
3. Contact support

Include:

• Specific products affected
• What seems wrong
• Your expectations vs reality

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Related: Understanding Forecast Accuracy | Common Issues