The term "predictive resource allocation" is particularly at home in the fields of automation, industry and Factory 4.0 as well as artificial intelligence. It describes a method that companies use to plan their materials, machines and employees in such a way that everything is utilised as efficiently as possible - before a bottleneck even occurs.
In contrast to traditional planning, which only reacts to problems, predictive resource allocation uses data analysis and smart predictions to learn where future demand will arise. For example, production data, customer orders or even weather forecasts are taken into account in order to initiate the right steps in good time.
A simple example: In a modern car factory, the system recognises from the current order situation and stock levels that a particular part could be in short supply in a fortnight' time. It automatically reorders this part today and adjusts the production lines so that everything continues to run smoothly. In this way, the forward-looking allocation of resources prevents unnecessary waiting times or downtime and saves costs.
In the digital industry in particular, this method helps companies to produce more flexibly and successfully.
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