← Back to Analysis Directory Sample Report: Demand Forecasting

Context and Data Preparation

Analysis Overview and Time Series Preparation

OV

Analysis Overview

Demand Forecasting Configuration

Analysis overview and configuration

Demand Forecasting
Analytics Corp
Analyze shopify_orders data
Module Configuration
forecast_horizon 12
seasonality_period 7
confidence_level 0.95
min_observations 14
Processing ID
test_1766288023
analysis type: Demand Forecasting
company: Analytics Corp
objective: Analyze shopify_orders data
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Key Insights

Analysis Overview

Purpose

This section provides insights into the key metrics and data characteristics of the demand forecasting analysis conducted for Shopify orders by Analytics Corp.

Key Findings

  • MAE: 0.63 - Represents the average magnitude of errors in the forecasts.
  • RMSE: 0.85 - Indicates the square root of the average squared differences between forecasted and actual values.
  • MAPE: 41.7 - Reflects the percentage of the mean absolute errors relative to the actual values.
  • Trend Strength: 0.234 - Shows the proportion of variance explained by the trend component.
  • Seasonality Strength: 0 - Suggests no significant seasonal pattern in the data.

Interpretation

The analysis reveals a relatively low MAE and RMSE, indicating accurate forecasts. However, the high MAPE suggests a significant percentage error in predictions. The trend component explains 23.4% of the variance, while seasonality seems negligible, impacting the forecasting accuracy.

Context

The analysis assumes consistent historical patterns for future forecasts and highlights limitations in predicting promotional impacts or product-level variations. The data retention rate of 51.2% indicates missing values affecting the analysis quality.

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Key Insights

Analysis Overview

Purpose

This section provides insights into the key metrics and data characteristics of the demand forecasting analysis conducted for Shopify orders by Analytics Corp.

Key Findings

  • MAE: 0.63 - Represents the average magnitude of errors in the forecasts.
  • RMSE: 0.85 - Indicates the square root of the average squared differences between forecasted and actual values.
  • MAPE: 41.7 - Reflects the percentage of the mean absolute errors relative to the actual values.
  • Trend Strength: 0.234 - Shows the proportion of variance explained by the trend component.
  • Seasonality Strength: 0 - Suggests no significant seasonal pattern in the data.

Interpretation

The analysis reveals a relatively low MAE and RMSE, indicating accurate forecasts. However, the high MAPE suggests a significant percentage error in predictions. The trend component explains 23.4% of the variance, while seasonality seems negligible, impacting the forecasting accuracy.

Context

The analysis assumes consistent historical patterns for future forecasts and highlights limitations in predicting promotional impacts or product-level variations. The data retention rate of 51.2% indicates missing values affecting the analysis quality.

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Data Preprocessing

Time Series Aggregation

41
Daily Periods

Data preprocessing and column mapping

Data Pipeline
80
Initial Records
41
Clean Records
Column Mapping
order_date
→
Created at
quantity
→
Lineitem quantity
revenue
→
Total
product_name
→
Lineitem name
product_sku
→
Lineitem sku
vendor
→
Vendor
41 Records
MCP Analytics
41 Records
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Key Insights

Data Preprocessing

Purpose

This section outlines the data preprocessing steps, including data quality checks, retention rate, and the impact of transformations on the dataset.

Key Findings

  • Initial Rows: 80 - The original dataset size.
  • Final Rows: 41 - The number of rows retained after cleaning.
  • Rows Removed: 39 - Instances removed during preprocessing.
  • Retention Rate: 51.2% - Percentage of data retained after cleaning.

Interpretation

The data preprocessing resulted in a significant reduction in the dataset size, with 51.2% of the original data retained. The removal of rows may impact the model’s training and testing phases, potentially affecting the accuracy of the demand forecasting.

Context

The data quality concerns addressed during preprocessing are crucial for ensuring the reliability of the demand forecasting model. The retention rate indicates the extent of data loss and its potential implications for the accuracy of the forecasted demand.

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Key Insights

Data Preprocessing

Purpose

This section outlines the data preprocessing steps, including data quality checks, retention rate, and the impact of transformations on the dataset.

Key Findings

  • Initial Rows: 80 - The original dataset size.
  • Final Rows: 41 - The number of rows retained after cleaning.
  • Rows Removed: 39 - Instances removed during preprocessing.
  • Retention Rate: 51.2% - Percentage of data retained after cleaning.

Interpretation

The data preprocessing resulted in a significant reduction in the dataset size, with 51.2% of the original data retained. The removal of rows may impact the model’s training and testing phases, potentially affecting the accuracy of the demand forecasting.

Context

The data quality concerns addressed during preprocessing are crucial for ensuring the reliability of the demand forecasting model. The retention rate indicates the extent of data loss and its potential implications for the accuracy of the forecasted demand.

Executive Summary

Key Findings and Recommendations

TLDR

Executive Summary

Key Findings & Recommendations

12
Forecast Horizon

Key Performance Indicators

Forecast horizon
12
Avg daily demand
1.4
Mape
41.7
Trend strength
23.4%
Seasonality strength
0
Model method
ETS(A,N,N)

Key Metrics

Key findings

Finding Value
Forecast Horizon 12 periods
Avg Daily Demand 1.4 units
Forecast Accuracy (MAPE) 41.7% (review)
Trend Strength 23.4% (weak)
Seasonality Strength 0% (weak)

Executive Summary

Bottom Line: Demand forecast generated for next 12 periods using ETS(A,N,N) model. Average daily demand is 1.4 units with 41.7% average forecast error (MAPE).

Key Findings:
• Stable baseline demand (23.4% strength) - Demand is relatively stable
• Limited seasonality (0% strength) - Demand is relatively uniform across periods
• Review forecast uncertainty - MAPE of 41.7% indicates forecast should be used with caution
• Analyzed 80 order records across 29 days

Recommendations:
Maintain higher safety stock due to forecast uncertainty. Consider reviewing seasonality parameters. Review high-volatility products for separate forecasting strategies or inventory adjustments.

Executive Summary
Demand Forecast
Action Items
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Key Insights

Executive Summary

Purpose

This section provides a concise summary of the key findings and insights from the executive summary and key takeaways.

Key Findings

  • Forecast Horizon: 12 periods - Indicates the time frame for the demand forecast.
  • Avg Daily Demand: 1.4 units - Represents the average daily demand for the analyzed period.
  • MAPE: 41.7% (review) - Indicates the average forecast error percentage, suggesting caution in using the forecast.
  • Trend Strength: 23.4% (weak) - Shows the stability of baseline demand.
  • Seasonality Strength: 0% (weak) - Indicates limited seasonality in demand patterns.

Interpretation

The demand forecasting model, ETS(A,N,N), generated a 12-period forecast with an average daily demand of 1.4 units. The relatively high MAPE of 41.7% suggests caution in relying solely on the forecast accuracy. The weak trend and seasonality strengths imply stable baseline demand and limited seasonal variations.

Context

The findings highlight the need for careful consideration when using the forecast due to the moderate forecast error. Understanding the stability of baseline demand and limited seasonality can help in refining inventory management strategies and adjusting safety stock levels.

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Key Insights

Executive Summary

Purpose

This section provides a concise summary of the key findings and insights from the executive summary and key takeaways.

Key Findings

  • Forecast Horizon: 12 periods - Indicates the time frame for the demand forecast.
  • Avg Daily Demand: 1.4 units - Represents the average daily demand for the analyzed period.
  • MAPE: 41.7% (review) - Indicates the average forecast error percentage, suggesting caution in using the forecast.
  • Trend Strength: 23.4% (weak) - Shows the stability of baseline demand.
  • Seasonality Strength: 0% (weak) - Indicates limited seasonality in demand patterns.

Interpretation

The demand forecasting model, ETS(A,N,N), generated a 12-period forecast with an average daily demand of 1.4 units. The relatively high MAPE of 41.7% suggests caution in relying solely on the forecast accuracy. The weak trend and seasonality strengths imply stable baseline demand and limited seasonal variations.

Context

The findings highlight the need for careful consideration when using the forecast due to the moderate forecast error. Understanding the stability of baseline demand and limited seasonality can help in refining inventory management strategies and adjusting safety stock levels.

Demand Forecast

Future Demand Predictions with Confidence Intervals

FC

Demand Forecast

Predictions with Confidence Intervals

12
Forecast Horizon

Demand forecast with confidence intervals

12
forecast horizon
1.4
avg daily demand
ETS(A,N,N)
model method
forecast horizon: 12
avg daily demand: 1.4
model method: ETS(A,N,N)
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Key Insights

Demand Forecast

Purpose

This section presents the forecasted demand values for the next 12 periods using the ETS(A,N,N) model. It includes the average daily demand of 1.4 units and displays the forecasted values along with confidence intervals to indicate the range of uncertainty.

Key Findings

  • Forecast Horizon: 12 - Indicates the number of periods into the future the model forecasts.
  • Average Daily Demand: 1.4 - Represents the historical average daily demand.
  • Forecasted Demand: 1.4 - The predicted demand for each period.
  • Confidence Intervals: Lower and upper bounds provide a range of uncertainty around the forecasted values.

Interpretation

The forecasted demand values, along with the confidence intervals, help in planning inventory orders. Wider confidence intervals suggest higher uncertainty, which may require adjustments like safety stock to mitigate risks of stockouts or overstocking.

Context

These forecasted values are crucial for inventory management decisions, ensuring adequate stock levels to meet demand while minimizing excess inventory costs. The ETS(A,N,N) model’s predictions provide insights into future demand patterns, aiding in strategic planning and resource allocation.

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Key Insights

Demand Forecast

Purpose

This section presents the forecasted demand values for the next 12 periods using the ETS(A,N,N) model. It includes the average daily demand of 1.4 units and displays the forecasted values along with confidence intervals to indicate the range of uncertainty.

Key Findings

  • Forecast Horizon: 12 - Indicates the number of periods into the future the model forecasts.
  • Average Daily Demand: 1.4 - Represents the historical average daily demand.
  • Forecasted Demand: 1.4 - The predicted demand for each period.
  • Confidence Intervals: Lower and upper bounds provide a range of uncertainty around the forecasted values.

Interpretation

The forecasted demand values, along with the confidence intervals, help in planning inventory orders. Wider confidence intervals suggest higher uncertainty, which may require adjustments like safety stock to mitigate risks of stockouts or overstocking.

Context

These forecasted values are crucial for inventory management decisions, ensuring adequate stock levels to meet demand while minimizing excess inventory costs. The ETS(A,N,N) model’s predictions provide insights into future demand patterns, aiding in strategic planning and resource allocation.

Time Series Decomposition

Trend, Seasonal, and Remainder Components

DC

Time Series Decomposition

Trend & Seasonal Components

0.234
Trend Strength

Time series decomposition into trend, seasonal, and remainder components

0.234
trend strength
0
seasonality strength
trend strength: 0.234
seasonality strength: 0
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Key Insights

Time Series Decomposition

Purpose

This section presents the decomposition of demand data into trend, seasonal, and remainder components to identify underlying patterns in the time series data.

Key Findings

  • Trend Strength: 23.4% (weak) - Indicates the long-term direction of demand growth or decline.
  • Seasonality Strength: 0% (weak) - Suggests the absence of significant recurring patterns in demand.
  • Pattern Observed: The trend is weak, implying a relatively stable baseline demand over time. Seasonality is also weak, indicating uniform demand across time periods.

Interpretation

The weak trend and seasonality strengths suggest that demand for the analyzed product does not exhibit strong long-term growth trends or distinct seasonal patterns. This implies a stable and consistent demand pattern without significant fluctuations over time.

Context

Understanding the weak trend and seasonality components is crucial for forecasting accurate demand levels and identifying any potential shifts in consumer behavior or market dynamics that may impact future demand patterns.

IN

Key Insights

Time Series Decomposition

Purpose

This section presents the decomposition of demand data into trend, seasonal, and remainder components to identify underlying patterns in the time series data.

Key Findings

  • Trend Strength: 23.4% (weak) - Indicates the long-term direction of demand growth or decline.
  • Seasonality Strength: 0% (weak) - Suggests the absence of significant recurring patterns in demand.
  • Pattern Observed: The trend is weak, implying a relatively stable baseline demand over time. Seasonality is also weak, indicating uniform demand across time periods.

Interpretation

The weak trend and seasonality strengths suggest that demand for the analyzed product does not exhibit strong long-term growth trends or distinct seasonal patterns. This implies a stable and consistent demand pattern without significant fluctuations over time.

Context

Understanding the weak trend and seasonality components is crucial for forecasting accurate demand levels and identifying any potential shifts in consumer behavior or market dynamics that may impact future demand patterns.

Forecast Accuracy

Model Performance and Error Metrics

AC

Forecast Accuracy

Model Performance Metrics

0.63
MAPE

Forecast accuracy metrics and model performance

0.63
mae
0.85
rmse
41.7
mape
mae: 0.63
rmse: 0.85
mape: 41.7
IN

Key Insights

Forecast Accuracy

Purpose

This section evaluates the accuracy of the forecast by analyzing metrics such as MAE, RMSE, and MAPE. It provides insights into the level of error and uncertainty in the forecasting model.

Key Findings

  • MAE: 0.63 units - Represents the average forecast error in original units.
  • RMSE: 0.85 - Indicates the root mean square error, penalizing larger errors more heavily.
  • MAPE: 41.7% - Reflects the average error as a percentage, highlighting higher uncertainty in forecasts.

Interpretation

The metrics suggest that the forecasting model has relatively low MAE and RMSE values, indicating good accuracy in predicting demand. However, the high MAPE of 41.7% suggests a significant percentage error, indicating potential areas for improvement in forecast precision.

Context

These accuracy metrics are crucial for understanding the reliability of the forecasting model and can guide decision-making processes related to inventory management, resource allocation, and overall business planning. The insights derived from these metrics can help in refining forecasting strategies and enhancing operational efficiency.

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Key Insights

Forecast Accuracy

Purpose

This section evaluates the accuracy of the forecast by analyzing metrics such as MAE, RMSE, and MAPE. It provides insights into the level of error and uncertainty in the forecasting model.

Key Findings

  • MAE: 0.63 units - Represents the average forecast error in original units.
  • RMSE: 0.85 - Indicates the root mean square error, penalizing larger errors more heavily.
  • MAPE: 41.7% - Reflects the average error as a percentage, highlighting higher uncertainty in forecasts.

Interpretation

The metrics suggest that the forecasting model has relatively low MAE and RMSE values, indicating good accuracy in predicting demand. However, the high MAPE of 41.7% suggests a significant percentage error, indicating potential areas for improvement in forecast precision.

Context

These accuracy metrics are crucial for understanding the reliability of the forecasting model and can guide decision-making processes related to inventory management, resource allocation, and overall business planning. The insights derived from these metrics can help in refining forecasting strategies and enhancing operational efficiency.

Residual Diagnostics

Forecast Error Analysis and Validation

RD

Residual Diagnostics

Forecast Error Analysis

Forecast error analysis and model validation

Residuals
Error Analysis
Model Validation
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Key Insights

Residual Diagnostics

Purpose

This section evaluates whether forecast errors exhibit random scatter or systematic patterns, crucial for assessing model accuracy and identifying missing information.

Key Findings

  • Residuals: Mean of 0 with outliers up to 2.59, skewness indicating non-randomness
  • Standardized Residuals: Mean of 0 with outliers up to 2.98, skewness suggesting non-randomness
  • Pattern Observed: Residuals show non-random behavior with skewness, indicating potential model inadequacies

Interpretation

The non-random scatter of residuals and standardized residuals suggests the model may not capture all underlying patterns in the data. Skewness indicates systematic errors, possibly due to unaccounted factors or model limitations.

Context

Understanding the residual patterns is vital for improving forecast accuracy and identifying areas where the model may need adjustments. This analysis helps in refining the forecasting model to better predict future demand accurately.

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Key Insights

Residual Diagnostics

Purpose

This section evaluates whether forecast errors exhibit random scatter or systematic patterns, crucial for assessing model accuracy and identifying missing information.

Key Findings

  • Residuals: Mean of 0 with outliers up to 2.59, skewness indicating non-randomness
  • Standardized Residuals: Mean of 0 with outliers up to 2.98, skewness suggesting non-randomness
  • Pattern Observed: Residuals show non-random behavior with skewness, indicating potential model inadequacies

Interpretation

The non-random scatter of residuals and standardized residuals suggests the model may not capture all underlying patterns in the data. Skewness indicates systematic errors, possibly due to unaccounted factors or model limitations.

Context

Understanding the residual patterns is vital for improving forecast accuracy and identifying areas where the model may need adjustments. This analysis helps in refining the forecasting model to better predict future demand accurately.

Product Volatility Analysis

Demand Variability and Forecast Difficulty

PA

Product Volatility

Demand Variability by Product

10
Products

Product-level demand variability and forecast difficulty

product_name avg_demand
Adidas Ultraboost 1.000
Allbirds Wool Runners 1.000
Hoka Clifton 9 1.000
Lululemon Align Leggings 1.000
Nike Air Max 270 1.000
North Face Thermoball 1.000
On Cloud Running Shoes 1.000
Patagonia Better Sweater 1.000
Adidas Ultraboost 0.000
Allbirds Wool Runners 0.000
Coefficient of Variation
Forecast Difficulty
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Key Insights

Product Volatility

Purpose

This section highlights the products with the most variable demand, aiding in understanding forecast difficulty based on the coefficient of variation (CV). It helps identify products that may require specialized forecasting approaches due to their demand volatility.

Key Findings

  • Adidas Ultraboost: CV = 1.0 - Moderate demand variability
  • Allbirds Wool Runners: CV = 0 - Stable demand
  • Pattern Observed: Some products exhibit high volatility (CV > 1.0) while others have stable demand patterns.

Interpretation

Understanding demand variability for each product is crucial for accurate forecasting. Products with higher CV values may require more sophisticated forecasting techniques to account for their unpredictable demand patterns. Stable demand products can be forecasted more reliably using standard approaches.

Context

These insights help in tailoring forecasting strategies to individual products, aligning with the overall objective of analyzing product-level demand patterns. It underscores the importance of considering demand variability in forecasting accuracy and inventory management decisions.

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Key Insights

Product Volatility

Purpose

This section highlights the products with the most variable demand, aiding in understanding forecast difficulty based on the coefficient of variation (CV). It helps identify products that may require specialized forecasting approaches due to their demand volatility.

Key Findings

  • Adidas Ultraboost: CV = 1.0 - Moderate demand variability
  • Allbirds Wool Runners: CV = 0 - Stable demand
  • Pattern Observed: Some products exhibit high volatility (CV > 1.0) while others have stable demand patterns.

Interpretation

Understanding demand variability for each product is crucial for accurate forecasting. Products with higher CV values may require more sophisticated forecasting techniques to account for their unpredictable demand patterns. Stable demand products can be forecasted more reliably using standard approaches.

Context

These insights help in tailoring forecasting strategies to individual products, aligning with the overall objective of analyzing product-level demand patterns. It underscores the importance of considering demand variability in forecasting accuracy and inventory management decisions.

Job ID: test_commerce__shopify__orders__demand_forecasting_20251220_193735
Secret Hash: 36f2d40ab80e