Demand Modeling and Forecasting

Understand how external factors influence your demand to increase forecast accuracy

Challenge:

Demand planning approaches that rely on historical sales, with no consideration of external causal factors, are insufficient. It’s impossible to predict the impact of external factors and changes in economic conditions. This issue is exacerbated by demand instability amid rising consumerism, shrinking product life cycles and SKU and channel proliferation.

Solution:

With access to over 550,000 time series data sets of external causal factors including weather and economic data, the llama.ai platform enables you to master demand attributes, drivers and variables for more accurate projections and confident decisions. Traditional statistical forecasting techniques are augmented with significant use of machine learning to drive substantial improvements in forecast accuracy.

  • Double-digit percentage gains in forecast accuracy
  • Ability to determine which external factors exert the most influence on demand
  • Reduced working capital and improved asset utilization

Harness the power of machine learning technology to identify the real factors impacting demand – going far beyond traditional forecasting methods to enable dramatically better predictions of future demand.

llama.ai is an AI-enabled enterprise decision platform built exclusively for analysis of the extended supply chain.  It combines three key analytics capabilities into a single platform to accelerate the speed and quality of business decisions with advanced, in-context analytics that span the breadth and depth of the extended supply chain and its specific functions. LLama.ai highlights operational trends, patterns and trade-offs through a centrally accessible supply chain digital twin that continuously pulls and prepares data from the comprehensive supply chain ecosystem.

See Solution Case Study

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