# 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.

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.

At a high level, the demand planning cycle follows four steps:

1. Data cleansing — Remove outliers, correct errors, handle promotions and one-off events so history reflects true underlying demand.
2. Statistical baseline — Apply forecasting models to generate an unbiased starting point (see methods below).
3. Enrichment — Layer in market intelligence, sales input, promotional plans, and known events.
4. 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.

\displaystyle F_t = \frac{1}{n} \sum_{i=1}^{n} D_{t-i}

\displaystyle F_t = \alpha \cdot D_{t-1} + (1 - \alpha) \cdot F_{t-1}

\displaystyle D = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \cdots + \epsilon

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.

To keep overrides useful, define clear rules for when and how they are allowed.

Operating rules:

• Start from the statistical baseline every cycle.
• Require explicit reason codes for every override.
• Use FVA monthly to confirm overrides improve outcomes.
• Retire override patterns that repeatedly reduce accuracy.

Demand reviews should focus on exceptions, not every SKU. Use a consistent format to keep the discussion decision-oriented.

Recommended working rule:

• review top 10-15 exceptions by business impact each cycle
• close or escalate each exception before meeting end

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.

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.

• Forecast Accuracy — How to measure whether your demand plan is any good.
• Demand Sensing & Shaping — Techniques for real-time adjustment and proactive demand management.