DEMAND FORECASTING ACCURACY REVIEW

Created by ChecklistGuro (https://checklistguro.com)

--- DATA & SYSTEM SETUP ---
[ ] 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 ---
[ ] 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 ---
[ ] 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 ---
[ ] 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, No, 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) â€“ impact on customer expectations and rush orders.

--- COLLABORATION & COMMUNICATION ---
[ ] 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: (Email, Shared Spreadsheet, Dedicated Platform, Verbal Communication)

--- 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? (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? (Email, Meeting, Shared Document, Automated Reports)
[ ] Upload Documentation of Model Tuning Parameters
[ ] Document Lessons Learned during this review and recommendations for future improvements.

--- END OF TEMPLATE ---
Transform this text into a digital, automated, and trackable mobile app!
Visit: https://checklistguro.com/templates/logistics/demand-forecasting-accuracy-review
(Click "Install Template" to launch your digital inspection tool immediately)