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Demand Planning
Demand planning is the process of estimating future customer demand and translating those estimates into actionable plans that drive supply, inventory, and financial decisions across the business. 🎯
It sits at the heart of every IBP process — get demand wrong, and everything downstream suffers.
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Demand Planning vs. Demand Forecasting
These terms get used interchangeably, but they shouldn't be.
Forecasting is a component of demand planning — an important one, but not the whole story. A demand plan also incorporates demand shaping decisions, new product launches, and consensus from sales and marketing.
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The Demand Planning Process
At a high level, the demand planning cycle follows four steps:
- Data cleansing — Remove outliers, correct errors, handle promotions and one-off events so history reflects true underlying demand.
- Statistical baseline — Apply forecasting models to generate an unbiased starting point (see methods below).
- Enrichment — Layer in market intelligence, sales input, promotional plans, and known events.
- Consensus — Cross-functional review to agree on one demand number the organization will execute against.
This cycle typically repeats monthly in an IBP cadence, though short-term adjustments may happen weekly via demand sensing.
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Statistical Forecasting Methods
Averages the last n periods of demand. Simple and transparent, but lags behind trends and ignores seasonality. Best suited for stable, mature products with low variability.
\displaystyle F_t = \frac{1}{n} \sum_{i=1}^{n} D_{t-i}Applies exponentially decreasing weights to older observations. More responsive than moving average, with a smoothing parameter alpha controlling how fast old data is discounted. Variants (Holt, Holt-Winters) handle trend and seasonality.
\displaystyle F_t = \alpha \cdot D_{t-1} + (1 - \alpha) \cdot F_{t-1}Models demand as a function of one or more independent variables (price, GDP, weather, marketing spend). Useful when causal drivers are well understood, but requires clean driver data and careful variable selection.
\displaystyle D = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \cdots + \epsilon
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Judgmental Forecasting
Statistical models can't capture everything. Judgmental forecasting fills the gap:
- Sales input — Account-level intelligence on pipeline, customer plans, and competitive dynamics.
- Market intelligence — Category trends, regulatory changes, macroeconomic shifts.
- Event-driven adjustments — Promotions, product launches, supply disruptions, weather events.
The key is to apply judgment on top of a statistical baseline, not instead of one. Unstructured overrides tend to introduce bias rather than accuracy — this is why tracking Forecast Value Added matters.
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Demand Segmentation: ABC-XYZ Analysis
Not all items deserve the same forecasting approach. ABC-XYZ analysis classifies products on two dimensions:
- ABC (volume / revenue contribution): A = top 80%, B = next 15%, C = remaining 5%
- XYZ (demand variability): X = low variability, Y = moderate, Z = highly erratic
This matrix helps demand planners allocate their time where it matters most — spend energy on high-value, hard-to-forecast items (AY, AZ) rather than spreading attention evenly.
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Planning Horizons 📆
Short-range forecasts benefit most from demand sensing. Medium-range is the sweet spot for the IBP process. Long-range planning is less about precision and more about directional alignment with strategy.
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Further Reading
- Forecast Accuracy — How to measure whether your demand plan is any good.
- Demand Sensing & Shaping — Techniques for real-time adjustment and proactive demand management.