Demand Planning

Demand Planning

Build accurate, actionable demand forecasts that align supply with expected customer demand while minimizing forecast error and inventory risk.

Input Gathering

Collect the following before starting the forecast cycle:

Step-by-Step Process

Step 1 — Data Cleansing and Baseline Preparation

• Remove or flag outliers (stockouts, one-time bulk orders, data entry errors).
• Normalize for calendar effects (trading days, leap years).
• Fill gaps using interpolation or proxy SKU behavior.
• Document every adjustment with reason codes.

Step 2 — Statistical Forecast Generation

Run appropriate models based on demand pattern:

• Generate point forecasts and prediction intervals (80% and 95%).
• Produce forecasts at the lowest meaningful granularity, then aggregate upward.

Step 3 — Demand Sensing and Signal Integration

Layer external signals onto the statistical baseline:

• Leading indicators: Web search trends, social media sentiment, foot traffic.
• Economic signals: GDP growth, consumer confidence, commodity prices.
• Customer intelligence: Pipeline data, customer forecasts, POS sell-through.
• Competitive signals: Product launches, pricing moves, store openings/closures.

Weight each signal by its historical correlation to actual demand.

Step 4 — Consensus Planning and Collaborative Review

Facilitate a structured demand review meeting:

1. Present statistical forecast vs. prior period actuals.
2. Overlay sales team input (bottom-up account-level adjustments).
3. Incorporate marketing input (promotions, campaigns, launches).
4. Reconcile finance top-down targets with bottom-up build.
5. Lock the consensus number for each SKU-location combination.
6. Record assumption log for every manual override.

Step 5 — Forecast Accuracy Measurement

Calculate and track these KPIs each cycle:

Step 6 — Bias Correction and Continuous Improvement

• Identify persistent over- or under-forecasting by product, region, or planner.
• Apply bias correction factors to the statistical baseline.
• A/B test new models or signals against the incumbent.
• Archive each forecast vintage for retrospective analysis.
• Update model parameters quarterly; retrain models annually.

Output Format

## Demand Forecast — [Product/Region] — [Period]

### Executive Summary
- Forecast horizon: [start] to [end]
- Total forecasted demand: [units/revenue]
- Forecast confidence: [high/medium/low]
- Key assumptions: [list]

### Forecast by Period
| Period | Statistical Forecast | Adjusted Forecast | YoY Change | Confidence Interval |
|--------|----------------------|-------------------|------------|---------------------|
| ...    | ...                  | ...               | ...        | ...                 |

### Accuracy Scorecard (Prior Cycle)
| Metric | Value | Target | Status |
|--------|-------|--------|--------|
| MAPE   | ...   | < 20%  | ...    |
| Bias   | ...   | +/- 5% | ...    |

### Assumptions and Risks
- [Numbered list of key assumptions]
- [Identified upside/downside risks with probability]

### Recommendations
- [Actionable next steps]

Quality Checklist

• Historical data covers at least 24 months and has been cleansed
• Outliers documented and handled with reason codes
• Appropriate model selected for each demand pattern
• Prediction intervals generated alongside point forecasts
• External signals evaluated and weighted
• Consensus review completed with cross-functional input
• Every manual override has a documented rationale
• Accuracy metrics calculated against prior forecast
• Bias correction applied where persistent drift detected
• Forecast locked and communicated to supply planning

Edge Cases

• New product launch with zero history: Use analogous product mapping; identify 2-3 similar past launches and scale by expected distribution and marketing spend.
• Demand during supply-constrained periods: Back-calculate unconstrained demand using lost-sales estimates; do not train models on constrained actuals.
• Promotional cannibalization: Model baseline and promotional lift separately; account for pull-forward and pantry-loading effects.
• Market discontinuities (pandemic, regulation change): Segment data into pre- and post-event; weight recent data more heavily or restart baseline.
• Highly intermittent / spare-parts demand: Switch to Croston or bootstrapping; report demand probability and expected order size separately.
• Currency or price-driven volume shifts: Decompose demand into price-driven and organic components; forecast each independently.