In industrial processes data of all kinds are collected; like production data, for example the output of a production line, but also process parameters of customers. The fast development of automation has opened possibilities to store large D-bases for all kinds of industrial processes. At the same time, however, one often has only a few data, for instance due to shorter production runs. Both for larger and smaller number of data mathematics can help to derive useful conclusions. Often data are used to compute simple statistical indicators like mean or standard deviation.
However, by using mathematics much more can be done. One can use probabilistic models to analyze the data, i.e. models for describing random outcomes; for example throwing a coin or counting the number of clients in a certain period. Also, if the data are too complex to draw a simple conclusion from them, mathematics can provide the tools to reduce them appropriately.
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