Demand Forecasting Framework

Build demand forecasts using time series, causal models, and expert judgment for planning, inventory, and capacity decisions with scenario analysis.

Security Scan

The skill is an instruction-only demand forecasting playbook that is internally consistent with its stated purpose and does not request credentials, install software, or contain executable code.

Purpose & Capability

The name/description (demand forecasting) matches the SKILL.md content (time series, causal models, judgmental methods, blended forecasts, metrics, and process guidance). No unrelated binaries, env vars, or config paths are requested.

Instruction Scope

SKILL.md contains high-level formulas, processes, and checklists only; it does not instruct the agent to read local files, access environment variables, or transmit data to third parties. It references external marketing/documentation links but does not direct data exfiltration.

Install Mechanism

No install specification or code files are present; this is instruction-only and does not write or execute code on disk.

Credentials

The skill requests no environment variables, credentials, or config paths—proportionate to an advisory/framework skill.

Persistence & Privilege

always is false and model invocation is default; the skill does not request elevated persistence or to modify other skills or system configuration.

Assessment

This is a non-executable, advisory playbook for forecasting and appears coherent with no direct risks like credential requests or installers. Before using: (1) verify the source if you plan to follow external links or purchase context packs; (2) don't paste sensitive or proprietary data into unknown external sites; (3) treat the formulas and ROI claims as guidance—validate with your team or historical data before operationalizing; and (4) if you want an automated agent to run models from this playbook, expect to need separate, secure deployment code and appropriate credentials/controls (not provided here).

Build accurate demand forecasts using multiple methodologies. Combines statistical models with market intelligence for actionable predictions.

When to Use

• Quarterly/annual demand planning
• New product launch forecasting
• Inventory optimization
• Capacity planning decisions
• Budget cycle preparation

Forecasting Methodologies

1. Time Series Analysis

Best for: Established products with 24+ months of history.

Decompose into: Trend + Seasonality + Cyclical + Residual

Moving Average (3-month):
  Forecast = (Month_n + Month_n-1 + Month_n-2) / 3

Weighted Moving Average:
  Forecast = (0.5 × Month_n) + (0.3 × Month_n-1) + (0.2 × Month_n-2)

Exponential Smoothing (α = 0.3):
  Forecast_t+1 = α × Actual_t + (1-α) × Forecast_t

2. Causal / Regression Models

Best for: Products where external factors drive demand.

Key drivers to model:

• Price elasticity: % demand change per 1% price change
• Marketing spend: Lag effect (typically 2-6 weeks)
• Seasonality index: Monthly coefficient vs annual average
• Economic indicators: GDP growth, consumer confidence, industry PMI
• Competitor actions: New entrants, price changes, promotions

Demand = β₀ + β₁(Price) + β₂(Marketing) + β₃(Season) + β₄(Economic) + ε

3. Judgmental / Qualitative

Best for: New products, market disruptions, limited data.

Methods:

• Delphi method: 3+ expert rounds, anonymous, converging estimates
• Sales force composite: Bottom-up from territory reps (apply 15-20% optimism correction)
• Market research: Survey-based purchase intent (apply 30-40% intent-to-purchase conversion)
• Analogous forecasting: Map to similar product launch curves

4. Blended Forecast (Recommended)

Combine methods using confidence-weighted average:

Method	Weight (Mature Product)	Weight (New Product)
Time Series	50%	10%
Causal	30%	20%
Judgmental	20%	70%

Forecast Accuracy Metrics

Metric	Formula	Target
MAPE	Avg(	Actual - Forecast
Bias	Σ(Forecast - Actual) / n	Near 0
Tracking Signal	Cumulative Error / MAD	-4 to +4
Weighted MAPE	Revenue-weighted MAPE	<10% for top SKUs

Demand Planning Process

Monthly Cycle

1. Week 1: Statistical forecast generation (auto-run models)
2. Week 2: Market intelligence overlay (sales input, competitor intel)
3. Week 3: Consensus meeting — align Sales, Marketing, Ops, Finance
4. Week 4: Finalize, communicate to supply chain, track vs prior forecast

Demand Segmentation (ABC-XYZ)

Segment	Volume	Variability	Approach
AX	High	Low	Auto-replenish, tight safety stock
AY	High	Medium	Statistical + review quarterly
AZ	High	High	Collaborative planning, buffer stock
BX	Medium	Low	Statistical, periodic review
BY	Medium	Medium	Hybrid model
BZ	Medium	High	Judgmental + safety stock
CX	Low	Low	Min/max rules
CY	Low	Medium	Periodic review
CZ	Low	High	Make-to-order where possible

Safety Stock Calculation

Safety Stock = Z × σ_demand × √(Lead Time)

Where:
  Z = Service level factor (95% = 1.65, 98% = 2.05, 99% = 2.33)
  σ_demand = Standard deviation of demand
  Lead Time = In same units as demand period

Scenario Planning

For each forecast, generate three scenarios:

Scenario	Probability	Assumptions
Bear	20%	-15% to -25% vs base. Recession, market contraction, competitor disruption
Base	60%	Historical trends + known pipeline. Most likely outcome
Bull	20%	+15% to +25% vs base. Market expansion, product virality, competitor exit

Red Flags in Your Forecast

• MAPE consistently >20% — model needs retraining
• Persistent positive bias — sales team sandbagging
• Persistent negative bias — over-optimism, check incentive structure
• Tracking signal outside ±4 — systematic error, investigate root cause
• Forecast never changes — "spreadsheet copy-paste" problem
• No external inputs — pure statistical = blind to market shifts

Industry Benchmarks

Industry	Typical MAPE	Forecast Horizon	Key Driver
CPG/FMCG	20-30%	3-6 months	Promotions, seasonality
Retail	15-25%	1-3 months	Trends, weather, events
Manufacturing	10-20%	6-12 months	Orders, lead times
SaaS	10-15%	12 months	Pipeline, churn, expansion
Healthcare	15-25%	3-6 months	Regulation, demographics
Construction	20-35%	12-24 months	Permits, economic cycle

ROI of Better Forecasting

For a company doing $10M revenue:

• 5% MAPE improvement → $200K-$500K inventory savings
• Reduced stockouts → 2-5% revenue recovery ($200K-$500K)
• Lower expediting costs → $50K-$150K savings
• Better capacity utilization → 3-8% OpEx reduction

Total impact: $450K-$1.15M annually from a 5-point MAPE improvement.

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