Demand Forecasting Accuracy Review Checklist

Data & System Setup

1 of 6

Focuses on the foundational elements of data quality, system integration, and parameter settings impacting forecast accuracy. Checks data sources, integration, and system configurations.

Forecast Horizon (Days/Weeks)

Data History Length (Days/Weeks/Months)

Data Source(s) for Demand Forecasts

ERP System

POS Data

External Market Data

Sales Team Input

Other (Specify in Long Text)

Describe any known data quality issues impacting forecasts (missing data, outliers, etc.)

Data Integration Method (e.g., API, Batch File)

API

Batch File

Manual Upload

Other (Specify in Long Text)

Upload a sample of the raw data used for forecasting (if possible & permissible)

Last Data Integration/Synchronization Date

Describe the system environment used for forecasting (software versions, infrastructure)

Forecast Methodologies

2 of 6

Evaluates the appropriateness and implementation of chosen forecasting models. Addresses model selection, parameter tuning, and algorithm limitations.

Primary Forecasting Model Used:

Time Series (e.g., Moving Average, Exponential Smoothing)

Causal/Regression Models

Machine Learning (e.g., Random Forest, Neural Networks)

Combination of Models

Number of Historical Periods Used in Forecast:

Justification for Chosen Forecasting Method:

Frequency of Model Retraining:

Seasonality Adjustment Method:

Additive

Multiplicative

None

Description of any custom adjustments or overrides applied to the forecast:

Model Parameter Optimization Technique:

Manual Adjustment

Grid Search

Genetic Algorithm

Other

Summary of Recent Model Tuning Efforts (dates and changes):

Forecast Error Metrics & Analysis

3 of 6

Focuses on the calculation, interpretation, and analysis of key forecasting error metrics. Covers bias, accuracy, and overall performance.

MAPE (Mean Absolute Percentage Error) - Current Value

MAPE (Mean Absolute Percentage Error) - Target Value

Bias (Mean Error) - Current Value

Bias (Mean Error) - Target Value

RMSE (Root Mean Squared Error) - Current Value

RMSE (Root Mean Squared Error) - Target Value

Error Distribution (e.g., Normal, Skewed):

Normal

Positively Skewed

Negatively Skewed

Other (Specify in LONG_TEXT)

Describe any observed patterns or trends in forecast errors.

Upload Error Analysis Chart/Visualization

Logistics-Specific Influences & Considerations

4 of 6

Addresses factors unique to logistics that impact demand and forecasting (e.g., seasonality, promotions, carrier capacity, geopolitical events).

Describe any recurring disruptions in carrier capacity and how they impact forecast adjustments.

What is the average lead time (days) for key logistics components?

Are weather patterns significantly impacting logistics demand? (Yes/No/Sometimes)

Yes

Sometimes

Which of the following events significantly impact logistics demand? (Select all that apply)

Promotions

Holiday Season

Geopolitical Events

Supplier Disruptions

Unexpected Surge in E-commerce Orders

Date of last review of key logistics partner agreements/contracts (impacts availability & cost)

Detail any specific impacts of customs regulations or trade policies on demand and forecasting needs.

Average order fulfillment time (days) – impact on customer expectations and rush orders.

Collaboration & Communication

5 of 6

Examines the processes for collaboration between demand planners, logistics teams, sales, and other stakeholders.

Frequency of cross-functional meetings regarding demand & logistics coordination:

Daily

Weekly

Bi-Weekly

Monthly

Ad-hoc

Describe the process for escalating significant forecast discrepancies between demand planning and logistics:

Average lead time (days) communicated from logistics to demand planning for capacity updates:

Which stakeholders are involved in the monthly forecast review?

Demand Planning

Logistics/Transportation

Sales

Supply Chain Management

Finance

Marketing

Summarize recent feedback received from logistics regarding the accuracy of demand forecasts and their impact on operations.

Method used to communicate forecast updates to Logistics:

Shared Spreadsheet

Dedicated Platform

Verbal Communication

Continuous Improvement & Documentation

6 of 6

Focuses on processes for ongoing model refinement, documenting changes, and sharing learnings.

Last Model Review/Update Date

Summary of Key Changes Made to Forecasting Models in the Last Period

Frequency of Forecast Model Calibration (e.g., weekly, monthly, quarterly)

What factors drove the need for model adjustments?

Changes in Customer Demand

Promotional Activity

Supply Chain Disruptions

New Product Introductions

External Economic Factors

Other (Specify in Long Text)

Describe any 'Other' factors influencing model adjustments (if selected above)

How are changes to forecasting models communicated to relevant stakeholders?

Meeting

Shared Document

Automated Reports

Data & System Setup

Forecast Horizon (Days/Weeks)

Data History Length (Days/Weeks/Months)

Data Source(s) for Demand Forecasts

Describe any known data quality issues impacting forecasts (missing data, outliers, etc.)

Data Integration Method (e.g., API, Batch File)

Upload a sample of the raw data used for forecasting (if possible & permissible)

Last Data Integration/Synchronization Date

Describe the system environment used for forecasting (software versions, infrastructure)

Forecast Methodologies

Primary Forecasting Model Used:

Number of Historical Periods Used in Forecast:

Justification for Chosen Forecasting Method:

Frequency of Model Retraining:

Seasonality Adjustment Method:

Description of any custom adjustments or overrides applied to the forecast:

Model Parameter Optimization Technique:

Summary of Recent Model Tuning Efforts (dates and changes):

Forecast Error Metrics & Analysis

MAPE (Mean Absolute Percentage Error) - Current Value

MAPE (Mean Absolute Percentage Error) - Target Value

Bias (Mean Error) - Current Value

Bias (Mean Error) - Target Value

RMSE (Root Mean Squared Error) - Current Value

RMSE (Root Mean Squared Error) - Target Value

Error Distribution (e.g., Normal, Skewed):

Describe any observed patterns or trends in forecast errors.

Upload Error Analysis Chart/Visualization

Logistics-Specific Influences & Considerations

Describe any recurring disruptions in carrier capacity and how they impact forecast adjustments.

What is the average lead time (days) for key logistics components?

Are weather patterns significantly impacting logistics demand? (Yes/No/Sometimes)

Which of the following events significantly impact logistics demand? (Select all that apply)

Date of last review of key logistics partner agreements/contracts (impacts availability & cost)

Detail any specific impacts of customs regulations or trade policies on demand and forecasting needs.

Average order fulfillment time (days) – impact on customer expectations and rush orders.

Collaboration & Communication

Frequency of cross-functional meetings regarding demand & logistics coordination:

Describe the process for escalating significant forecast discrepancies between demand planning and logistics:

Average lead time (days) communicated from logistics to demand planning for capacity updates:

Which stakeholders are involved in the monthly forecast review?

Summarize recent feedback received from logistics regarding the accuracy of demand forecasts and their impact on operations.

Method used to communicate forecast updates to Logistics:

Continuous Improvement & Documentation

Last Model Review/Update Date

Summary of Key Changes Made to Forecasting Models in the Last Period

Frequency of Forecast Model Calibration (e.g., weekly, monthly, quarterly)

What factors drove the need for model adjustments?

Describe any 'Other' factors influencing model adjustments (if selected above)

How are changes to forecasting models communicated to relevant stakeholders?

Upload Documentation of Model Tuning Parameters

Document Lessons Learned during this review and recommendations for future improvements.

Logistics Management Solution Screen Recording

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