> ## Documentation Index
> Fetch the complete documentation index at: https://betterdatainc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Forecast Accuracy

> How to measure, analyze, and improve forecast accuracy with MAPE, WAPE, and bias

# Forecast Accuracy

Forecast accuracy measures how well your forecasts match actual demand. Understanding and improving accuracy is critical for effective planning.

## Recommended Starting Points by Role

### Planner (Forecasting + Replenishment)

**Start here:**

1. **Accuracy Metrics** - Review MAPE, WAPE, and bias metrics
2. **Forecast vs Actuals** - Compare forecasts to actual demand
3. **Deviation Analysis** - Identify products/channels with poor accuracy
4. **Improvement Actions** - Take actions to improve accuracy

**Daily workflow:**

* Review forecast accuracy metrics
* Compare forecasts to actuals
* Identify accuracy issues
* Adjust forecast models and events
* Track accuracy improvements

### Finance (COGS, Holding Cost, Cost-to-Serve)

**Start here:**

1. **Accuracy Reports** - Generate forecast accuracy reports
2. **Cost Impact** - Analyze forecast accuracy impact on costs
3. **Bias Analysis** - Review forecast bias trends
4. **Accuracy Trends** - Monitor accuracy over time

**Daily workflow:**

* Review accuracy metrics and trends
* Analyze cost impact of forecast errors
* Monitor bias trends (over/under forecasting)
* Generate accuracy reports for analysis

### Executive (Summary Dashboards)

**Start here:**

1. **Accuracy Dashboard** - High-level accuracy overview
2. **Accuracy KPIs** - Key accuracy performance indicators
3. **Accuracy Trends** - Long-term accuracy trends

**Daily workflow:**

* Review accuracy dashboard summaries
* Monitor accuracy KPIs
* Track accuracy trends over time
* Review accuracy improvement initiatives

## Accuracy Metrics Explained

### MAPE (Mean Absolute Percentage Error)

Average percentage error across all forecasts:

```
MAPE = (1/n) × Σ|Actual - Forecast| / Actual × 100
```

**Interpretation:**

* **\< 10%**: Excellent accuracy
* **10-20%**: Good accuracy
* **20-30%**: Fair accuracy
* **> 30%**: Poor accuracy (needs improvement)

**Use cases:**

* Overall forecast accuracy assessment
* Comparing different forecast models
* Tracking accuracy improvements

### WAPE (Weighted Absolute Percentage Error)

Weighted average percentage error, giving more weight to high-volume items:

```
WAPE = Σ|Actual - Forecast| / ΣActual × 100
```

**Interpretation:**

* Similar to MAPE but weighted by volume
* Better reflects impact on high-volume items
* More relevant for inventory planning

**Use cases:**

* Accuracy assessment for high-volume items
* Inventory planning accuracy
* Cost impact analysis

### Bias

Measures whether forecasts are consistently over or under:

```
Bias = (Σ(Forecast - Actual) / ΣActual) × 100
```

**Interpretation:**

* **Positive Bias**: Over-forecasting (forecast > actual)
* **Negative Bias**: Under-forecasting (forecast \< actual)
* **Zero Bias**: Balanced forecasting (ideal)

**Use cases:**

* Identifying systematic over/under forecasting
* Adjusting forecast models
* Balancing inventory levels

### Hit Rate

Percentage of forecasts within acceptable tolerance:

* **Within 10%**: Forecasts within 10% of actual
* **Within 20%**: Forecasts within 20% of actual
* **Within 50%**: Forecasts within 50% of actual

**Use cases:**

* Service level assessment
* Planning confidence levels
* Risk assessment

## Step-by-Step: Measuring Forecast Accuracy

### 1. View Accuracy Metrics

1. Navigate to **Plan** → **Forecast Accuracy**
2. Select:
   * **Forecast Scenario**: Scenario to analyze
   * **Time Period**: Date range for analysis
   * **Products**: Products to analyze (or all)
   * **Channels**: Channels to analyze (or all)
   * **Locations**: Locations to analyze (or all)

### 2. Review Overall Metrics

View overall accuracy:

* **MAPE**: Mean Absolute Percentage Error
* **WAPE**: Weighted Absolute Percentage Error
* **Bias**: Forecast bias
* **Hit Rate**: Percentage within tolerance

### 3. Analyze by Dimension

Analyze accuracy by:

* **Product**: Accuracy per product (identify problem products)
* **Channel**: Accuracy per channel (identify problem channels)
* **Location**: Accuracy per location (identify problem locations)
* **Time Period**: Accuracy over time (identify trends)

### 4. Review Forecast vs Actuals

1. Navigate to **Analyze** → **Forecast vs Actuals**
2. View side-by-side comparison:
   * Forecast values
   * Actual values
   * Deviation
   * Percentage error

### 5. Identify Problem Areas

Identify:

* **Top Under-Forecast**: Products/channels consistently under-forecast
* **Top Over-Forecast**: Products/channels consistently over-forecast
* **High MAPE Items**: Items with poor accuracy
* **Trending Issues**: Accuracy getting worse over time

## Forecast vs Actuals Analysis

### Viewing Forecast vs Actuals

1. Navigate to **Analyze** → **Forecast vs Actuals**
2. Configure:
   * **Forecast Scenario**: Scenario to compare
   * **Time Period**: Date range
   * **Products**: Products to compare
   * **Channels**: Channels to compare
3. View comparison:
   * **Forecast**: Forecasted demand
   * **Actual**: Actual demand
   * **Deviation**: Difference (Actual - Forecast)
   * **Percentage Error**: Percentage deviation

### Understanding Deviations

**Positive Deviation** (Actual > Forecast):

* Under-forecasted demand
* Risk of stockout
* May indicate missed events or trends

**Negative Deviation** (Actual \< Forecast):

* Over-forecasted demand
* Risk of overstock
* May indicate events didn't have expected impact

### Deviation Alerts

System can alert when deviations exceed thresholds:

* **Alert Threshold**: Configurable (e.g., ±20%)
* **Alert Frequency**: Configurable (e.g., daily, weekly)
* **Alert Delivery**: Email, webhook, Slack

**Note**: Push alerts may be work in progress. Check alert configuration for current status.

## Improving Forecast Accuracy

### 1. Apply Events Properly

**Action**: Ensure all relevant events are applied:

* Promotions
* Seasonality
* Marketing campaigns
* External factors

**Impact**: Events can significantly improve accuracy when properly applied.

### 2. Adjust Forecast Models

**Action**: Try different forecast models:

* Time series models
* Seasonal models
* Trend models
* Custom models

**Impact**: Different models work better for different products/channels.

### 3. Review Historical Patterns

**Action**: Analyze historical demand patterns:

* Identify trends
* Recognize seasonality
* Understand cycles
* Account for anomalies

**Impact**: Better understanding of patterns improves forecasts.

### 4. Address Bias

**Action**: If bias is consistently positive or negative:

* **Positive Bias (Over-forecasting)**: Reduce forecast values
* **Negative Bias (Under-forecasting)**: Increase forecast values
* Adjust forecast model parameters
* Review event applications

**Impact**: Reducing bias improves overall accuracy.

### 5. Focus on Problem Areas

**Action**: Prioritize improving accuracy for:

* High-volume items (biggest impact)
* High-value items (cost impact)
* Problem products/channels (biggest improvement opportunity)

**Impact**: Focused improvements yield better results.

## What Success Looks Like

### Planner Success

* ✅ MAPE below 20% for most products
* ✅ Bias near zero (balanced forecasting)
* ✅ Accuracy improving over time
* ✅ Problem areas identified and addressed
* ✅ Forecasts trusted for planning

### Finance Success

* ✅ Accuracy metrics tracked regularly
* ✅ Cost impact of forecast errors understood
* ✅ Bias trends monitored
* ✅ Accuracy reports generated for analysis

### Executive Success

* ✅ Accuracy meets targets
* ✅ Accuracy trends positive
* ✅ Accuracy improvements demonstrated
* ✅ Forecasts drive confident decisions

## Common Pitfalls

### 1. Not Measuring Accuracy

**Problem**: Creating forecasts but not measuring how accurate they are.

**Solution**: Regularly measure forecast accuracy:

* Set up accuracy reviews
* Track accuracy metrics
* Compare forecasts to actuals
* Monitor accuracy trends

**How to avoid**: Make accuracy measurement part of forecast workflow.

### 2. Ignoring Bias

**Problem**: Focusing only on MAPE without addressing bias.

**Solution**: Monitor and address bias:

* Review bias metrics regularly
* Identify systematic over/under forecasting
* Adjust forecast models to reduce bias
* Track bias trends

**How to avoid**: Include bias in accuracy reviews.

### 3. Not Acting on Accuracy Insights

**Problem**: Measuring accuracy but not taking action to improve.

**Solution**: Take action based on accuracy insights:

* Identify problem products/channels
* Adjust forecast models
* Apply events properly
* Focus improvement efforts

**How to avoid**: Make accuracy improvement part of planning process.

### 4. Not Understanding Channel Policy Effects

**Problem**: Not understanding how channel policies affect forecast interpretation.

**Solution**: Understand channel policy effects:

* Review ATP policies
* Understand replenishment rules
* Consider alert thresholds
* Account for channel allocations

**How to avoid**: Familiarize yourself with channel policy configuration.

## Troubleshooting

### Accuracy Metrics Not Available

**Symptoms**: Forecast accuracy metrics not showing or unavailable.

**Possible causes:**

1. Forecasts not generated
2. Actuals data not available
3. Time period mismatch
4. System calculation issue

**Steps to resolve:**

1. Verify forecasts are generated
2. Check actuals data availability
3. Verify time period alignment
4. Review calculation settings
5. Contact support if issue persists

### Accuracy Consistently Poor

**Symptoms**: Forecast accuracy (MAPE) consistently high across products/channels.

**Possible causes:**

1. Events not applied
2. Forecast models not appropriate
3. Insufficient historical data
4. External factors not accounted for
5. Data quality issues

**Steps to resolve:**

1. Review and apply relevant events
2. Try different forecast models
3. Verify sufficient historical data
4. Account for external factors
5. Check data quality
6. Adjust forecast parameters
7. Monitor accuracy improvements

### Bias Not Improving

**Symptoms**: Forecast bias remains consistently positive or negative.

**Possible causes:**

1. Forecast model parameters not adjusted
2. Events not properly applied
3. Systematic data issues
4. Model not appropriate for data

**Steps to resolve:**

1. Adjust forecast model parameters
2. Review event applications
3. Check for data quality issues
4. Try different forecast models
5. Monitor bias trends

### Deviation Alerts Not Working

**Symptoms**: Forecasts deviate significantly but alerts not triggering.

**Possible causes:**

1. Alert thresholds not configured
2. Alert policies not enabled
3. Deviation calculation issue
4. Alert delivery issue
5. Push alerts work in progress

**Steps to resolve:**

1. Check alert threshold configuration
2. Verify alert policies are enabled
3. Review deviation calculations
4. Check alert delivery settings
5. Verify push alert status (may be WIP)
6. Test alert functionality

***

## Related Pages

* [Forecasting Overview](/decide/forecasting-overview)
* [Forecast Scenarios](/decide/forecast-scenarios)
* [Forecast Events](/decide/forecast-events)
* [Replenishment Planner](/decide/replenishment-planner)
* [Dashboards](/improve/dashboards)

***

## Permissions & Roles

<Tip>
  Viewing forecast accuracy requires standard user permissions. All accuracy metrics are scoped to your organization.
</Tip>