Logistics in Application Path of Neural Networks
Accurately forecasting resource demand within the supply chain has never been easy, particular given the constantly changing nature of the data over periods of time.
What may have been true in measurements around demand or logistics one minute might be entirely different an hour, day or week later, which can throw off a short-term load forecast (STLF) and lead to costly over- or under-estimations, which in turn can lead to too much or too little supply.
To improve such forecasts, there are multiple efforts underway to create new models that can more accurately predict load needs, and while they have …
Logistics in Application Path of Neural Networks was written by Jeffrey Burt at The Next Platform.
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