Predicting the Future of Consumer Demand

Predicting the Future of Consumer Demand

Improving demand forecasting is a priority for manufacturers—61% of respondents to a recent AIMMS report say that improving forecast accuracy is either important or extremely important for their organizations. However, only 2% of respondents are extremely satisfied with their forecasts. Many bet on new technologies such as machine learning and demand sensing to improve forecast accuracy (see chart). According to the survey:

Demand-forecasting technology makes a difference in manufacturers’ satisfaction:

  • Only 2% are extremely satisfied with the tools they use for demand forecasting
  • 39% use spreadsheets for demand forecasting, and nearly 45% of them are dissatisfied
  • 30% use a specialist forecasting package, and 61% of them are satisfied
  • 23% use bundled enterprise resource planning, and most feel indifferent about the tool

Organizations are looking into alternative techniques to improve demand forecasting:

  • 56% are exploring statistical modeling of historical demand at their organizations
  • 41% are looking into machine learning
  • 41% are investigating demand sensing
  • 60% are curious about new techniques such as demand-driven material requirements planning

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