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Do we need ML models to decipher the evolving patterns in consumer demand?
Demand Sensing helps in deciphering the evolving demand patterns at a granular level. Many of these patterns are caused by multiple causal factors, which are difficult to model in a conventional statistical model. Their impact is also non-linear at times.
ML models using Neural Network techniques pick up such demand shifts much earlier than other techniques, giving us more time to respond comprehensively. Faster detection and quick response are the twin approaches of agility to synchronize supplies with demand on a continuous basis.
If your current approach is focused only on faster response, look at deploying ML based Demand Sensing engine for faster detection of what to respond to.