15pp
forecast accuracy
SHAiPE: the LTPlabs framework
Set the Decision
Define the demand planning scope
SKU, channel, geography, time horizon
Clarify the decision level
strategic, tactical, operational
Define forecasting objective weighted
MAPE, BIAS
Highlight what matters
Identify key demand drivers
promotions, seasonality, price, sales force incentives, external factors
Define forecasting hierarchy and granularity
product, segment, market segment
Align success metrics
accuracy, BIAS, service level impact
Augment with AI
Develop causal and ML models to capture demand patterns, and incorporate additional business context and external factors
- DataHistorical demandDemand driversTrend and seasonalityExternal factorsAI ModelOutputsForecasted demandScenario analysisModel performanceCommercial alerts
Generate baseline forecasts and scenario-based demand predictions
Promote continuous updates with newly acquired data
Identify forecasts that call for validation from commercial team
Prototype your solution
Pilot on selected products / markets and compare the performance of AI generated forecasts with current forecasts
Involve commercial and demand planning teams to test the new process
Expand to scale
Integrate in the planning and S&OP processes
Deploy tools for dynamic forecast validation and adjustment
Train teams and embed demand plans into decision routines
Continuously monitor performance and improve AI models
Nutshell
What this means for your business
Improved forecast accuracy and reduced bias
Better anticipation of demand variability and market shifts
Enhanced service levels through better planning inputs
Reduced stockouts and excess inventory
Faster and more responsive planning cycles
InsightsConteúdo
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