6 Sub-dimensions · Click to expand L2 detailClic para expandir detalle L2
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› L2N2
L2 · Market Signal Capture & Sensing
Demand sensing from POS & IoT, external data integration, retailer collaboration & VMI, consumer behavior analytics, and competitive intelligence.
The eyes of the demand plan — the real-time signals that tell the supply chain what the market is actually doing right now, before the formal order arrives.
L3 Sub-componentsSubcomponentes L3 5 items · click to explore elementos · clic para explorar
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Demand sensing: real-time signal capture from POS, IoT & digital channels
POS daily as the highest-value demand sensing signal, weather and event correlations, and the 20–40% MAPE improvement of demand sensing vs. statistical forecasting alone.
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External data integration: weather, events, macroeconomics & social signals
The R²>0.4 validation threshold for external variables, the seasonal demand model for temperature-sensitive categories, and the data freshness requirement for external signals.
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Retailer collaboration: POS sharing, VMI & CPFR programs
The 4 collaboration models (transactional → POS sharing → VMI → CPFR), the VMI service level target of >98%, and the 55% MAPE improvement from retailer POS data.
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Consumer behavior analytics & social listening for demand signals
The TikTok effect quantified, the Google Trends leading indicator (7–14 day lead time), and the emergency S&OP protocol triggered by high-impact social signals.
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Competitive intelligence & market share dynamics in demand planning
Price elasticity cross-effects, the stockout transfer demand model, and the Nielsen/KANTAR panel as the most precise source of market share dynamics.
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› L2N2
L2 · AI-Driven Forecasting & Demand Intelligence
Statistical forecasting (ETS/ARIMA/ensemble), ML forecasting (XGBoost/neural networks), accuracy measurement (MAPE/WMAPE/bias), demand segmentation (ABC/XYZ), and GenAI in demand intelligence.
The analytical core of demand planning — the models and metrics that turn historical data and market signals into the most accurate possible view of future demand.
L3 Sub-componentsSubcomponentes L3 5 items · click to explore elementos · clic para explorar
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Statistical forecasting foundations: ETS, ARIMA & ensemble models
ETS for trend+seasonality, Croston for intermittent demand, ensemble as the best-practice standard, and automatic model selection by SKU profile.
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Machine learning forecasting: XGBoost, neural networks & gradient boosting
10–25% ML lift over statistical models when external variables are numerous, SHAP values for explainability, and overfitting as the #1 ML forecasting risk.
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Forecast accuracy measurement: MAPE, WMAPE, bias & error decomposition
WMAPE as the financially-correct accuracy metric, forecast bias as the highest-impact financial KPI (systematic over/underestimation), and the 3-component error decomposition.
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Demand segmentation: ABC/XYZ, lifecycle & channel mix
The 9 ABC/XYZ segments with differentiated forecasting strategies, the lifecycle dimension (launch/growth/maturity/decline), and the 80–20 focus rule for demand planning effort.
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Generative AI & LLMs in demand intelligence: emerging applications
LLM for market signal synthesis and S&OP narrative generation, the 62% reduction in S&OP preparation time, and the hallucination risk that requires human validation.
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› L2N2
L2 · Demand Shaping & Commercial Alignment
Demand shaping levers (promotions, pricing, availability), S&OP commercial alignment & consensus demand, price elasticity modeling, NPI demand planning, and demand risk management.
The active layer of demand planning — not just predicting demand, but influencing it, aligning the commercial plan with the supply plan, and managing allocation when supply is constrained.
L3 Sub-componentsSubcomponentes L3 5 items · click to explore elementos · clic para explorar
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Demand shaping levers: promotions, pricing & product availability
Promotion uplift modeling (TPR 20% → demand +40–150%), the pantry loading effect, and the 6-week advance capture requirement for the commercial events calendar.
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S&OP commercial alignment: sales & marketing plan integration
The single number consensus process, the override accuracy KPI that measures whether sales adjustments improve or degrade MAPE, and the “two numbers problem” that destroys supply chain efficiency.
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Price elasticity modeling & revenue management
Own and cross-price elasticity by channel, dynamic pricing governance, and the revenue-per-unit metric that integrates the price-volume trade-off.
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New product introduction (NPI) demand planning & launch forecasting
The 4 NPI forecasting methods (analogy, Delphi, market test, Bass model), the 3-scenario framework, and the weekly review cadence in the first 12 weeks.
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Demand risk management: supply constraints, allocations & backorder governance
The 4 allocation models (pro-rata, strategic priority, historical fill rate, margin-based), the pre-defined allocation policy requirement, and the backorder resolution time KPI.
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› L2N2
L2 · NPI Demand Planning & Launch Forecasting
Stage-gate demand planning, cannibalization & portfolio rationalization, channel fill modeling, discontinuation planning, and market test design.
The new product dimension of demand planning — from the first rough-order-of-magnitude estimate at Gate 1 to the market test that cuts launch forecast error in half.
L3 Sub-componentsSubcomponentes L3 5 items · click to explore elementos · clic para explorar
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Stage-gate demand planning for new product launches
The forecast accuracy target by gate (±50% at G1 → ±15% at G4), the test market as the highest-ROI NPI investment, and the transition to the statistical model at 6–12 months of history.
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Cannibalization & portfolio rationalization in demand planning
The 3 cannibalization types, the NPI net portfolio uplift as the correct ROI metric, and the annual 3–8% portfolio rationalization rate that keeps the SKU portfolio healthy.
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Channel fill & initial stocking for new product launches
Channel fill vs. consumer demand decomposition, the post-launch demand vs. channel fill ratio as the early health indicator, and the 8-week advance coordination with production.
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Discontinuation planning & end-of-life inventory management
The EOL write-off % as the cost of poor discontinuation planning, the 12–16 week notification lead time, and the last-time-buy as the most precise signal of residual demand.
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Market test design & demand signal validation for NPI
The 3 market test types, the sell-through rate and repurchase rate as the most predictive NPI success signals, and the 10–40x ROI of market test investment vs. launch forecast error cost.
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› L2N2
L2 · Demand Operations & Process Governance
Demand planning team design & skills, cadence (weekly/monthly/S&OP), system architecture & data quality, forecast override management, and KPI framework.
The operational backbone of demand planning — the people, the process cadence, the data architecture, and the governance framework that make the demand plan reliable and continuously improving.
L3 Sub-componentsSubcomponentes L3 5 items · click to explore elementos · clic para explorar
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Demand planning team design & skill requirements
The demand planner of the future (statistical + ML + commercial + facilitation), the 200–400 SKUs/planner productivity benchmark with advanced tools, and the CPF certification.
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Demand planning cadence: weekly, monthly & S&OP integration
The 3-level cadence (weekly demand sensing → monthly S&OP cycle → quarterly IBP), the 4-week S&OP sequence, and the S&OP decision rate % as the process health KPI.
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Demand planning system architecture & data quality
The 4 data problems that degrade MAPE (outliers, stale hierarchy, returns distortion, stale events calendar), the data quality audit, and the 95% clean data requirement.
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Forecast override management & human judgment integration
Override accuracy by type (commercial events: 78% improve MAPE; intuitive: 28% improve), the challenge process, and the governance framework that eliminates undocumented overrides.
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Demand planning KPI framework & continuous improvement
The 4-dimension KPI framework (accuracy, process, business impact, improvement), the PDCA continuous improvement cycle, and the financial translation of MAPE into safety stock cost.
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› L2N2
L2 · Demand-CX Link & Customer Demand Analytics
Customer demand pattern segmentation, demand forecasting for OTIF commitments, demand-driven replenishment (DDMRP), e-commerce demand sensing, and omnichannel demand planning.
The customer-facing dimension of demand planning — connecting the forecast to the customer experience, the safety stock to the OTIF promise, and the digital channel signals to the replenishment plan.
L3 Sub-componentsSubcomponentes L3 5 items · click to explore elementos · clic para explorar
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Customer demand patterns: segmentation & behavior analytics
CV-based demand pattern segmentation (regular/seasonal/intermittent), the Croston migration for CV>0.7 segments, and the monthly monitoring of pattern stability changes.
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Demand forecasting for customer OTIF & service level commitments
The safety stock formula as the financial bridge between MAPE and OTIF, the 8–10% inventory reduction per 5-point MAPE improvement, and the forecast-driven inventory reduction potential.
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Demand-driven replenishment: pull vs. push in the supply chain
DDMRP buffer zones (red/yellow/green), the 38% DIO reduction and 86% stockout reduction from DDMRP implementation, and the complementary role of DDMRP and statistical forecasting.
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Demand sensing from e-commerce data & digital channels
Add-to-cart as the lowest-latency demand signal (1–2 day lead), the R²=0.74 correlation for digital signals in high-digital-presence categories, and the Mercado Libre API for Mexico.
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Demand planning for omnichannel: unified demand view across channels
The unified demand hierarchy (SKU × Channel × Region × Period), differentiated forecasting models by channel, and the channel mix forecast as the critical signal for omnichannel supply planning.
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