Industrial
Data Science
The initial situation
In industrial practice today, many decisions are made on the basis of gut instinct, even though issues such as Business Intelligence, Data Warehouse and Big Data have already been established for a long time. Often, the success of measures that a company is supposed to develop continuously cannot even be quantified. Even worse - today, in industrial practice, we are often unable to even qualify the potential of possible fields of action from which these measures are derived on the basis of the data situation.
From the situation described, one follows slogans and management fashions, whereby the action is usually based on the hope that with the implementation of many measures, some hits will be there all by themselves.
When times get worse, there is often no other option than to intervene harshly and sweepingly in the company, which then regularly eliminates important parts of the organism "company" without necessity. The encounter of such an initial situation in the age of Data Science, Machine Learning and AI (artificial intelligence) seems surprising. But it is not.
Today, companies are still controlled in a similar way to the first steamships. The captain sets his engine telegraph, and somewhere deep down in the engine room, orders are shouted and many small levers are turned. This is followed by anxious hopes that the direction is right and that everything will work out somehow.
Our future scenario
Imagine the following: You are the pilot of the jet "Enterprise" and control it with a side stick. The smallest movements lead immediately to precise reaction and feedback. The fly-by-wire control prevents oversteer and helps to keep the Enterprise stable even in large gusts.
That still sounds unbelievable today? With our development of Industrial Data Science, we have set ourselves the goal of doing just that, and we know that this will be possible in the future.
The special feature of Industrial Data Science
What is the difference between Industrial Data Science and Data Science or approaches like Process Mining or Product or Portfolio Mining?
Our Industrial Data Science approach is based on the holistic theory of summarization and resolution levels. This combines systemic management theory with process and information architecture and IT systems in companies. The theory of condensation and resolution levels creates completely new prerequisites for viewing the "enterprise" system.
Data and information can be easily generated from the IT systems in companies. The key is to refine this data by condensing it and extracting its essence in such a way that the hidden core aspects of the company crystallize in the data. Only if this succeeds can the extracted information be used to guide action and directly for the various decision-making and action areas of a company.
RIM-Industrial Data Science thus combines a novel business theory with the practical application of modern Data Science methods.
The underlying theoretical framework was developed by our Managing Partner Prof. Fischer with his STZ-RIM Industrial Data Science team together with some of our closest customers. Currently we are in the process of industrializing this theoretical framework and making it available exclusively to selected customers.
You too can be one of the first companies to benefit from our revolutionary approach, which prepares your company to be precisely and accurately controllable according to the fly-by-wire principle.
