Workbook

Forecasting & Planning Workbook

Interactive workbook with step-by-step problem framing, a data-preparation checklist, baseline forecast choices, seasonal decomposition cues, configurable backtesting plan, selectable error metrics (including CRPS for probabilistic forecasts), and a scenario-planning worksheet that links forecasts to concrete operational decisions (inventory, staffing, budgets). Responses are saved so teams can iterate, compare runs, and track follow-through.

Interactive Tool

Forecasting & Planning Workbook

Use this workbook to produce forecasts that are evaluated, explainable, and actionable.

This interactive workbook guides you from problem framing through data preparation, baseline and seasonal checks, backtesting design, and scenario planning. Save each run so you can compare models, record assumptions, and link forecasts to operational actions (inventory, staffing, budgets).

Tip: Be honest about uncertainty — save probabilistic forecasts when possible and use CRPS or other proper scoring rules to evaluate them.

A short name that identifies the item, product, region, or metric you are forecasting.
Who uses the forecast, what decisions depend on it, and what outcomes you hope to improve (inventory turns, service level, staffing efficiency, budget accuracy).
Typical planning horizon for decisions tied to this forecast (e.g., 3, 6, 12 months).
Identify the primary data sources you will use for modeling.
If you selected 'Other' or have notes about data quality, record them here.
These are common checks—choose those you will apply and record how.
Record important cleaning steps, imputations, aggregations, or feature engineering decisions.
Start with a simple baseline—it's essential for meaningful evaluation.
Use decomposition or plots to decide; record the pattern you observe.
Pick metrics appropriate to your business context. Avoid MAPE alone when volumes can be near zero.
Choose how you'll simulate production forecasting: rolling tests usually mirror operations better.
How many windows or folds you will use for backtesting (e.g., 3-12).
Describe how you'll avoid leakage and how you'll measure stability across windows.
Record models tried (e.g., ETS, ARIMA, Prophet, XGBoost), why you prefer one, and trade-offs (explainability, latency, data needs).
Name the scenario (e.g., Base, Upside, Downside).
Describe the scenario, key assumptions, and drivers (demand shock, promotion plan, supply disruption).
Express your subjective probability or use a calibrated quantitative estimate.
1.0 10.0
Be specific: e.g., reorder points, safety stock, planned hires, budget revisions, promotional commitments.
Operational steps to increase capacity, expedite orders, or adjust staffing.
Actions to reduce inventory risk, adjust staffing, or scale back purchases.
Which metrics you will track (forecast bias, service level, inventory days) and how often you'll review them.
A short, honest rating to help decision-makers weigh uncertain forecasts.
1.0 10.0
Choose whether this saved run should be visible to your team for review and follow-up.
Name or role responsible for tracking the monitoring plan and executing actions.
Date for the next review (YYYY-MM-DD). Use your cadence.
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